KNN_Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "## Dataset - 1\n",
    "### Title: Adult Income\n",
    "### URL: https://archive.ics.uci.edu/dataset/2/adult\n",
    "### UCI ID: 2\n",
    "### Domain: Social Science\n",
    "### Feature Type: Mixed\n",
    "### No. of Features: 14\n",
    "### No. of Rows: 48842\n",
    "# \n",
    "## Dataset - 2\n",
    "### Title: Credit Approval\n",
    "### URL: https://archive.ics.uci.edu/dataset/27/credit+approval\n",
    "### UCI ID: 27\n",
    "### Domain: Business\n",
    "### Feature Type: Mixed\n",
    "### No. of Features: 15\n",
    "### No. of Rows:690\n",
    "# \n",
    "## Dataset - 3\n",
    "### Title: Mushroom Classification\n",
    "### URL: https://archive.ics.uci.edu/dataset/73/mushroom\n",
    "### UCI ID: 73\n",
    "### Domain: Biology\n",
    "### Feature Type: Categorical\n",
    "### No. of Features: 22\n",
    "### No. of Rows:8124\n",
    "# \n",
    "## Dataset - 4\n",
    "### Title: Bank Marketing\n",
    "### URL: https://archive.ics.uci.edu/dataset/222/bank+marketing\n",
    "### UCI ID: 222\n",
    "### Domain: Business\n",
    "### Feature Type: Mixed\n",
    "### No. of Features: 16\n",
    "### No. of Rows: 45211\n",
    "# \n",
    "## Dataset - 5\n",
    "### Title: Heart Disease\n",
    "### URL: https://archive.ics.uci.edu/dataset/45/heart+disease\n",
    "### UCI ID: 45\n",
    "### Domain: Health & Medicine\n",
    "### Feature Type: Mixed\n",
    "### No. of Features: 13\n",
    "### No. of Rows: 303"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#pip install ucimlrepo in anaconda command line prompt\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
    "from scipy.stats import skew, kurtosis, jarque_bera\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#pip install lazypredict in anaconda command line prompt\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, roc_auc_score, plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Function\n",
    "\n",
    "# Data Fetching Function\n",
    "def get_data(id):\n",
    "    data=fetch_ucirepo(id=id)\n",
    "    X = data.data.features \n",
    "    y = data.data.targets\n",
    "    df= pd.merge(X, y, left_index=True, right_index=True)\n",
    "    return df, y.columns[0]\n",
    "\n",
    "# Missing Values Treatment\n",
    "def missing_value_treatment(df):\n",
    "    missing_columns=(df.isna().sum()!=0)[(df.isna().sum()!=0)].index\n",
    "    for i in missing_columns:\n",
    "        if ((df[i].isna().sum()/len(df))<=0.05) & len(df)>30000:\n",
    "            df[i].dropna(inplace=True)\n",
    "            df.reset_index(inplace=True,drop=True)\n",
    "        elif (df[i].isna().sum()/len(df))>0.5:\n",
    "            df.drop([i],axis=1,inplace=True)\n",
    "        elif (df[i].dtype=='object') or (df[i].dtype.name=='category'):\n",
    "            mode_val = df[i].mode()[0]\n",
    "            df[i].fillna(mode_val, inplace=True)\n",
    "        else:\n",
    "            skewness=skew(df[i].dropna())\n",
    "            if abs(skewness)>1:\n",
    "                df[i].fillna(df[i].median(), inplace=True)\n",
    "            else:\n",
    "                df[i].fillna(df[i].mean(), inplace=True)\n",
    "    print(\"Dataset is treated for missing values successfully\")\n",
    "\n",
    "# Standardization of Numerical Columns\n",
    "def data_standardization(df,type):\n",
    "    num_var=[]\n",
    "    for i in df.drop([label_column],axis=1).columns:\n",
    "        if (df[i].dtype!='object') and (df[i].dtype.name!='category'):\n",
    "            num_var.append(i)\n",
    "    if type==1:\n",
    "        scaler=StandardScaler()\n",
    "        for i in num_var:\n",
    "            df[i]=scaler.fit_transform(df[[i]]).round(2)\n",
    "    elif type==2:\n",
    "        scaler = MinMaxScaler()\n",
    "        for i in num_var:\n",
    "            df[i]=scaler.fit_transform(df[[i]]).round(2)\n",
    "\n",
    "# Categorical Encoding\n",
    "def cat_encoding(df):\n",
    "    cat_var=[]\n",
    "    for i in df.drop([label_column],axis=1).columns:\n",
    "        if (df[i].dtype=='object') or (df[i].dtype.name=='category'):\n",
    "            cat_var.append(i)\n",
    "    label_encoder = LabelEncoder()\n",
    "    for feature in cat_var:\n",
    "        df[feature] = label_encoder.fit_transform(df[feature])\n",
    "\n",
    "# Numerical Summary\n",
    "def num_summary(df):\n",
    "    return df.describe()\n",
    "\n",
    "# Class Balance\n",
    "def class_balance(df):\n",
    "    return (df[label_column].value_counts(normalize=True)*100)\n",
    "\n",
    "# Pick top 5 important numerical features\n",
    "def top_5_num_features(df):\n",
    "    num_var=[]\n",
    "    for i in df.drop([label_column],axis=1).columns:\n",
    "        if (df[i].dtype!='object') and (df[i].dtype.name!='category'):\n",
    "            num_var.append(i)\n",
    "    X_train=df[num_var]\n",
    "    y_train=df[label_column]\n",
    "    rf_classifier = RandomForestClassifier()\n",
    "    rf_classifier.fit(X_train, y_train)\n",
    "    feature_importances = rf_classifier.feature_importances_\n",
    "    feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})\n",
    "    feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "    top_5_numerical_features = feature_importance_df.head(5)['Feature'].tolist()\n",
    "    return top_5_numerical_features\n",
    "\n",
    "# Construct histogram and boxplot of important numerical features\n",
    "def hist_box_plots(df):\n",
    "    num_features=top_5_num_features(df)\n",
    "    for i in num_features:\n",
    "        print(\"\")\n",
    "        print(f\"Box and Distribution Plot - {i}\")\n",
    "        plt.figure(figsize=(8, 4))\n",
    "        sns.boxplot(data=df[i])\n",
    "        plt.title('Box Plot')\n",
    "        plt.show()\n",
    "\n",
    "        plt.figure(figsize=(8, 4))\n",
    "        sns.histplot(data=df[i])\n",
    "        plt.title('Distribution Plot')\n",
    "        plt.show()\n",
    "\n",
    "        skewness=skew(df[i])\n",
    "        kurt=kurtosis(df[i])\n",
    "        JB,p_val=jarque_bera(df[i])\n",
    "    \n",
    "        print(\"Skewness:\",skewness)\n",
    "        if skewness<0:\n",
    "            print(\"Left Skewed\",'\\n')\n",
    "        elif skewness>0:\n",
    "            print(\"Right Skewed\",'\\n')\n",
    "        else:\n",
    "            print(\"Center\",'\\n')\n",
    "\n",
    "        print(\"Kurtosis:\",kurt)\n",
    "        if kurt<3:\n",
    "            print(\"Platykurtic\",'\\n')\n",
    "        elif kurt>3:\n",
    "            print(\"Leptokurtic\",'\\n')\n",
    "        else:\n",
    "            print(\"Mesokurtic\",'\\n')\n",
    "    \n",
    "        print(\"JB Test for Normality\")\n",
    "        print(JB,p_val)\n",
    "        if p_val<0.05:\n",
    "            print(\"Distribution is not normal\",'\\n')\n",
    "        else:\n",
    "            print(\"Distribution is normal\",'\\n')\n",
    "\n",
    "# Data Split and CV\n",
    "def data_split_and_CV(df):\n",
    "    seed=int(input(\"Random State? \"))\n",
    "    test_size=int(input(\"What % of data to be left for test? \"))/100\n",
    "    k=int(input(\"How many folds required? \"))\n",
    "    X=df.drop([label_column],axis=1)\n",
    "    y=df[label_column]\n",
    "    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=test_size,random_state=seed)\n",
    "    \n",
    "    sk_folds=StratifiedKFold(n_splits=k,random_state=seed,shuffle=True)\n",
    "    \n",
    "    return X_train,X_test,y_train,y_test,sk_folds\n",
    "\n",
    "# Model Selection\n",
    "def models(X_train, X_test, y_train, y_test):\n",
    "    clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
    "    models,predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "    print(models)\n",
    "    \n",
    "# Model Training\n",
    "def knn_learn(df,X_train,y_train,X_test,sk_folds):\n",
    "    k=int(input(\"What is the value of K? \"))\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    scoresCV=cross_val_score(knn, df.drop([label_column],axis=1), df[label_column], cv = sk_folds)\n",
    "    return knn, scoresCV\n",
    "\n",
    "# Model Evaluation\n",
    "def model_eval(knn,X_test,y_test,scoresCV):\n",
    "    y_prob=knn.predict_proba(X_test)\n",
    "    y_pred_test=knn.predict(X_test)\n",
    "    auc=roc_auc_score(y_test, y_prob[:, 1])\n",
    "    class_report=classification_report(y_test, y_pred_test, output_dict=True,target_names=df[label_column].unique().tolist())\n",
    "    df_report = pd.DataFrame(class_report).transpose()\n",
    "    \n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print (\"Train-Test Split Evaluation\")\n",
    "    print(df_report)\n",
    "    \n",
    "    plot_roc_curve(knn, X_test, y_test)\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"Cross-Validation Evaluation\")\n",
    "    print(\"Cross Validation Scores: \", scoresCV)\n",
    "    print(\"Average CV Score: \", scoresCV.mean())\n",
    "    print(\"Number of CV Scores used in Average: \", len(scoresCV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Master Function\n",
    "def master(data=None,X_train=None, X_test=None, y_train=None, y_test=None,sk_folds=None,knn=None,scoresCV=None):\n",
    "    print(\"1- Fetch Data\",\"2- Missing Values Treatment\",\"3- EDA: Numerical Summary\",\n",
    "          \"4- EDA: Class Balance(%)\",\"5- EDA: Histogram & Box Plot of Important Numerical Features\",\n",
    "          \"6- Categorical Encoding\",\"7- Standardization of Numerical Features\",\n",
    "          \"8- Outliers Detection & Treatment\",\"9-  Data Splitting & Cross-Validation\",\n",
    "          \"10- Model Selection\",\"11- Model Training(KNN)\", \"12- Model Evaluation\",\n",
    "          \"13- More Functions to be Added\",sep='\\n')\n",
    "    sr=int(input(\"What do you want to do? \"))\n",
    "    \n",
    "    if sr==1 and data==None:\n",
    "        id=int(input(\"Please enter the UCI ID \"))\n",
    "        df=get_data(id)\n",
    "        print(\"Successfuly Connected to the Data Source\")\n",
    "        return df\n",
    "    \n",
    "    elif sr==2:\n",
    "        missing_value_treatment(data)\n",
    "    \n",
    "    elif sr==7:\n",
    "        print(\"1- Standard Scaler\",\"2- Min-Max Scaler\", sep='\\n')\n",
    "        type=int(input(\"Which type of Scaler? \"))\n",
    "        data_standardization(data,type)\n",
    "    \n",
    "    elif sr==6:\n",
    "        cat_encoding(data)\n",
    "    \n",
    "    elif sr==3:\n",
    "        return num_summary(data)\n",
    "    \n",
    "    elif sr==4:\n",
    "        return class_balance(data)\n",
    "    \n",
    "    elif sr==5:\n",
    "        hist_box_plots(data)\n",
    "    \n",
    "    elif sr==8:\n",
    "        return \"Outlier detection and treatment functionality under progress!\"\n",
    "    \n",
    "    elif sr==9:\n",
    "        return data_split_and_CV(data)\n",
    "    \n",
    "    elif sr==10:\n",
    "        models(X_train, X_test, y_train, y_test)\n",
    "    \n",
    "    elif sr==11:\n",
    "        return knn_learn(data,X_train,y_train,X_test,sk_folds)\n",
    "    \n",
    "    elif sr==12:\n",
    "        model_eval(knn,X_test,y_test,scoresCV)\n",
    "    \n",
    "    else:\n",
    "        return \"Please either select correct option or wait for future updates!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset - 1\n",
    "### Title: Adult Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 1\n",
      "Please enter the UCI ID 2\n",
      "Successfuly Connected to the Data Source\n"
     ]
    }
   ],
   "source": [
    "# Dataset Fetching (Pipeline Function)\n",
    "df,label_column=master()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
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       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>245487</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>176756</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>28887</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>292175</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age         workclass  fnlwgt     education  education-num  \\\n",
       "0    39         State-gov   77516     Bachelors             13   \n",
       "1    50  Self-emp-not-inc   83311     Bachelors             13   \n",
       "2    38           Private  215646       HS-grad              9   \n",
       "3    53           Private  234721          11th              7   \n",
       "4    28           Private  338409     Bachelors             13   \n",
       "5    37           Private  284582       Masters             14   \n",
       "6    49           Private  160187           9th              5   \n",
       "7    52  Self-emp-not-inc  209642       HS-grad              9   \n",
       "8    31           Private   45781       Masters             14   \n",
       "9    42           Private  159449     Bachelors             13   \n",
       "10   37           Private  280464  Some-college             10   \n",
       "11   30         State-gov  141297     Bachelors             13   \n",
       "12   23           Private  122272     Bachelors             13   \n",
       "13   32           Private  205019    Assoc-acdm             12   \n",
       "14   40           Private  121772     Assoc-voc             11   \n",
       "15   34           Private  245487       7th-8th              4   \n",
       "16   25  Self-emp-not-inc  176756       HS-grad              9   \n",
       "17   32           Private  186824       HS-grad              9   \n",
       "18   38           Private   28887          11th              7   \n",
       "19   43  Self-emp-not-inc  292175       Masters             14   \n",
       "\n",
       "           marital-status         occupation   relationship  \\\n",
       "0           Never-married       Adm-clerical  Not-in-family   \n",
       "1      Married-civ-spouse    Exec-managerial        Husband   \n",
       "2                Divorced  Handlers-cleaners  Not-in-family   \n",
       "3      Married-civ-spouse  Handlers-cleaners        Husband   \n",
       "4      Married-civ-spouse     Prof-specialty           Wife   \n",
       "5      Married-civ-spouse    Exec-managerial           Wife   \n",
       "6   Married-spouse-absent      Other-service  Not-in-family   \n",
       "7      Married-civ-spouse    Exec-managerial        Husband   \n",
       "8           Never-married     Prof-specialty  Not-in-family   \n",
       "9      Married-civ-spouse    Exec-managerial        Husband   \n",
       "10     Married-civ-spouse    Exec-managerial        Husband   \n",
       "11     Married-civ-spouse     Prof-specialty        Husband   \n",
       "12          Never-married       Adm-clerical      Own-child   \n",
       "13          Never-married              Sales  Not-in-family   \n",
       "14     Married-civ-spouse       Craft-repair        Husband   \n",
       "15     Married-civ-spouse   Transport-moving        Husband   \n",
       "16          Never-married    Farming-fishing      Own-child   \n",
       "17          Never-married  Machine-op-inspct      Unmarried   \n",
       "18     Married-civ-spouse              Sales        Husband   \n",
       "19               Divorced    Exec-managerial      Unmarried   \n",
       "\n",
       "                  race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                White    Male          2174             0              40   \n",
       "1                White    Male             0             0              13   \n",
       "2                White    Male             0             0              40   \n",
       "3                Black    Male             0             0              40   \n",
       "4                Black  Female             0             0              40   \n",
       "5                White  Female             0             0              40   \n",
       "6                Black  Female             0             0              16   \n",
       "7                White    Male             0             0              45   \n",
       "8                White  Female         14084             0              50   \n",
       "9                White    Male          5178             0              40   \n",
       "10               Black    Male             0             0              80   \n",
       "11  Asian-Pac-Islander    Male             0             0              40   \n",
       "12               White  Female             0             0              30   \n",
       "13               Black    Male             0             0              50   \n",
       "14  Asian-Pac-Islander    Male             0             0              40   \n",
       "15  Amer-Indian-Eskimo    Male             0             0              45   \n",
       "16               White    Male             0             0              35   \n",
       "17               White    Male             0             0              40   \n",
       "18               White    Male             0             0              50   \n",
       "19               White  Female             0             0              45   \n",
       "\n",
       "   native-country income  \n",
       "0   United-States  <=50K  \n",
       "1   United-States  <=50K  \n",
       "2   United-States  <=50K  \n",
       "3   United-States  <=50K  \n",
       "4            Cuba  <=50K  \n",
       "5   United-States  <=50K  \n",
       "6         Jamaica  <=50K  \n",
       "7   United-States   >50K  \n",
       "8   United-States   >50K  \n",
       "9   United-States   >50K  \n",
       "10  United-States   >50K  \n",
       "11          India   >50K  \n",
       "12  United-States  <=50K  \n",
       "13  United-States  <=50K  \n",
       "14              ?   >50K  \n",
       "15         Mexico  <=50K  \n",
       "16  United-States  <=50K  \n",
       "17  United-States  <=50K  \n",
       "18  United-States  <=50K  \n",
       "19  United-States   >50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The snapshot below shows that the data contains data entry errors where some missing values are written as '?'\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age                int64\n",
      "workclass         object\n",
      "fnlwgt             int64\n",
      "education         object\n",
      "education-num      int64\n",
      "marital-status    object\n",
      "occupation        object\n",
      "relationship      object\n",
      "race              object\n",
      "sex               object\n",
      "capital-gain       int64\n",
      "capital-loss       int64\n",
      "hours-per-week     int64\n",
      "native-country    object\n",
      "income            object\n",
      "dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# No Manual Data Type Correction Required\n",
    "print(df.dtypes,'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age               False\n",
      "workclass          True\n",
      "fnlwgt            False\n",
      "education         False\n",
      "education-num     False\n",
      "marital-status    False\n",
      "occupation         True\n",
      "relationship      False\n",
      "race              False\n",
      "sex               False\n",
      "capital-gain      False\n",
      "capital-loss      False\n",
      "hours-per-week    False\n",
      "native-country     True\n",
      "income            False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# Manual Data Entry Error Correction\n",
    "print(df.isin([\"?\"]).sum()!=0)\n",
    "question_mark_columns=(df.isin([\"?\"]).sum()!=0)[(df.isin([\"?\"]).sum()!=0)].index\n",
    "for i in question_mark_columns:\n",
    "    df[i]=df[i].replace(to_replace='?',value=np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<=50K' '>50K' '<=50K.' '>50K.']\n"
     ]
    }
   ],
   "source": [
    "# Manual data label cleaning\n",
    "print(df[label_column].unique())\n",
    "df[label_column] = df[label_column].replace({'<=50K.': '<=50K', '>50K.': '>50K'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 2\n",
      "Dataset is treated for missing values successfully\n",
      " \n",
      "age               0\n",
      "workclass         0\n",
      "fnlwgt            0\n",
      "education         0\n",
      "education-num     0\n",
      "marital-status    0\n",
      "occupation        0\n",
      "relationship      0\n",
      "race              0\n",
      "sex               0\n",
      "capital-gain      0\n",
      "capital-loss      0\n",
      "hours-per-week    0\n",
      "native-country    0\n",
      "income            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing Value Treatment (Pipeline Function)\n",
    "master(df)\n",
    "print(\" \")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manually removing redundant columns\n",
    "# fnlwgt appears to be some sort of sampling weight (number of people in population representated by a particular sample or record) which is irrelevant to when analyzing a subset of large dataset and for the purpose of EDA\n",
    "df=df.drop(columns='fnlwgt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  education  education-num      marital-status  \\\n",
       "0   39         State-gov  Bachelors             13       Never-married   \n",
       "1   50  Self-emp-not-inc  Bachelors             13  Married-civ-spouse   \n",
       "2   38           Private    HS-grad              9            Divorced   \n",
       "3   53           Private       11th              7  Married-civ-spouse   \n",
       "4   28           Private  Bachelors             13  Married-civ-spouse   \n",
       "\n",
       "          occupation   relationship   race     sex  capital-gain  \\\n",
       "0       Adm-clerical  Not-in-family  White    Male          2174   \n",
       "1    Exec-managerial        Husband  White    Male             0   \n",
       "2  Handlers-cleaners  Not-in-family  White    Male             0   \n",
       "3  Handlers-cleaners        Husband  Black    Male             0   \n",
       "4     Prof-specialty           Wife  Black  Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week native-country income  \n",
       "0             0              40  United-States  <=50K  \n",
       "1             0              13  United-States  <=50K  \n",
       "2             0              40  United-States  <=50K  \n",
       "3             0              40  United-States  <=50K  \n",
       "4             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.00</td>\n",
       "      <td>48842.00</td>\n",
       "      <td>48842.00</td>\n",
       "      <td>48842.00</td>\n",
       "      <td>48842.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.64</td>\n",
       "      <td>10.08</td>\n",
       "      <td>1079.07</td>\n",
       "      <td>87.50</td>\n",
       "      <td>40.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.71</td>\n",
       "      <td>2.57</td>\n",
       "      <td>7452.02</td>\n",
       "      <td>403.00</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>99999.00</td>\n",
       "      <td>4356.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  education-num  capital-gain  capital-loss  hours-per-week\n",
       "count 48842.00       48842.00      48842.00      48842.00        48842.00\n",
       "mean     38.64          10.08       1079.07         87.50           40.42\n",
       "std      13.71           2.57       7452.02        403.00           12.39\n",
       "min      17.00           1.00          0.00          0.00            1.00\n",
       "25%      28.00           9.00          0.00          0.00           40.00\n",
       "50%      37.00          10.00          0.00          0.00           40.00\n",
       "75%      48.00          12.00          0.00          0.00           45.00\n",
       "max      90.00          16.00      99999.00       4356.00           99.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical Features Summary (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<=50K   76.07\n",
       ">50K    23.93\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Feature Class Balance (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 5\n",
      "\n",
      "Box and Distribution Plot - age\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.5575631924658626\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: -0.18437271998309956\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "2599.8192722344866 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - capital-gain\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 11.894293693350868\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 152.6773425724958\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "48590194.712563224 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - education-num\n"
     ]
    },
    {
     "data": {
      "image/png": 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kBixgSZIaSFUdu8mSReDmYzahpHvtAG5rHUIaQ6dV1cRqO45pAUtqI8l8VU23ziHp/3kIWpKkBixgSZIasICl8TDXOoCk+/IcsCRJDbgCliSpAQtYkqQGLGCp55Kcm+QzSRaSXNQ6j6QlngOWeizJFuCzwDnAQeDjwAVV9emmwSS5ApZ67ieBhaq6saruAt4FnNc4kyQsYKnvJoEvLHt9cLBNUmMWsNRvWWWb552kTcAClvrtIHDqstenAF9slEXSMhaw1G8fBx6b5NFJjgfOB97fOJMkYGvrAJI2TlXdneQVwL8DW4B9VXV941iS8DIkSZKa8BC0JEkNWMCSJDVgAUuS1IAFLElSAxawJEkNWMCSJDVgAUuS1MD/AULtS4v9Fd6hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.3165151356965018\n",
      "Left Skewed \n",
      "\n",
      "Kurtosis: 0.6255583739336319\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "1611.8889827431972 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - hours-per-week\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.2387423248342843\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 2.950634153321035\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "18181.909686694897 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - capital-loss\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 4.569668511893423\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 20.012174283988244\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "985010.0471924777 0.0\n",
      "Distribution is not normal \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Histograms and Boxplots of important numerical variables (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Interpretation\n",
    "\n",
    "## 1. Age \n",
    "\n",
    "####   Based on the interpretation of the box plot, it is observed that the interquartile range (IQR), representing the middle 50% of the data, spans from approximately 28 to 48. Additionally, the median line, which denotes the central tendency of the data, is positioned closer to the lower end of the box. These observations indicate a positive skewness in the distribution. Positive skewness implies that the tail of the distribution extends towards higher values, with a greater concentration of data points towards the lower end.\n",
    "\n",
    "#### The data distribution is right-skewed, indicating that it is asymmetric with a longer tail on the right side.\n",
    "#### The distribution has lighter tails and is less peaked than a normal distribution, suggesting fewer extreme values.\n",
    "#### The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot. This implies that the assumptions of normality may not hold for this dataset, and further analysis or modeling should take this into account.\n",
    "    \n",
    "## 2. education num (the number of years of education)\n",
    "\n",
    "####  Based on the box plot analysis, it is observed that 50% of the dataset falls within the range of 9 to 12, with the median line positioned slightly closer to the lower quartile, indicative of a mild positive skew. This observed skewness contrasts with the left-skewed impression conveyed by the histogram. This discrepancy is attributed to the presence of outliers predominantly concentrated at the lower end of the data distribution.\n",
    "\n",
    "#### The data distribution is left-skewed, with a higher concentration of data towards higher values and a tail extending towards lower values.\n",
    "#### The distribution is platykurtic, indicating a lower likelihood of extreme values and a flatter peak compared to a normal distribution.\n",
    "#### The data does not follow a normal distribution, as indicated by both the skewness, kurtosis, and the Jarque-Bera test. This suggests that assumptions of normality are violated, and caution should be exercised in subsequent analyses or modeling\n",
    "\n",
    "## 3. Hours (worked) per week: Discrete (from 1 to 99)\n",
    "\n",
    "####  The box plot analysis reveals that 50% of the data observations are situated within the interquartile range, spanning from approximately 40 to 45. Additionally, the median line, denoting the central tendency, is positioned towards the upper portion of the box. These observations indicate a negatively skewed distribution, where the tail extends towards lower values while the bulk of the data is concentrated towards higher values. However, it is pertinent to note the presence of abnormally high outliers at the upper end, which contribute to a divergence from the expected distributional pattern.\n",
    "\n",
    "#### The data distribution is right-skewed, with a higher concentration of data towards lower values and a tail extending towards higher values.\n",
    "#### The distribution is platykurtic, indicating a lower likelihood of extreme values and a flatter peak compared to a normal distribution.\n",
    "#### The data does not follow a normal distribution, as indicated by both the skewness, kurtosis, and the Jarque-Bera test. This suggests that assumptions of normality are violated, and caution should be exercised in subsequent analyses or modeling\n",
    "\n",
    "\n",
    "## 4. Capital Gain:\n",
    "\n",
    "####   The interpretation of the box plot reveals that 50% of the data values fall within the range of 0 to 0, as indicated by the interquartile range (IQR). Additionally, the median line, representing the central tendency, is situated towards the lower portion of the box. These observations suggest a positive skewness in the distribution, signifying that the tail of the distribution extends towards higher values while the bulk of the data is concentrated towards lower values. However, it is noteworthy that the box plot exhibits extreme outliers, indicating the presence of values significantly deviating from the overall trend of the data\n",
    "\n",
    "#### The data distribution is heavily right-skewed, indicating that it is highly asymmetric with a very long tail on the right side.\n",
    "#### The distribution has very heavy tails and a very sharp peak, suggesting many extreme values and a high concentration of values around the mean.\n",
    "#### The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot. This implies that the assumptions of normality do not hold for this dataset, and further analysis or modeling should take this into account.\n",
    "\n",
    "## 5. Capital Loss:\n",
    "\n",
    "####   The analysis of the box plot reveals that the interquartile range (IQR), representing the middle 50% of the data, spans from 0 to 0. Furthermore, the median line, which represents the central tendency of the data, is positioned towards the lower portion of the box. These observations collectively suggest a positive skewness in the distribution, indicating that the tail of the distribution extends towards higher values while the bulk of the data is concentrated towards lower values.\n",
    "\n",
    "#### Heavy right-skewness, with a long tail towards higher values.\n",
    "#### Leptokurtosis, indicating heavy tails and a sharp peak.\n",
    "#### Significant deviation from normality, as evidenced by both the high skewness and kurtosis values, as well as the Jarque-Bera test result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 6\n"
     ]
    }
   ],
   "source": [
    "# Categorical Encoding (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 7\n",
      "1- Standard Scaler\n",
      "2- Min-Max Scaler\n",
      "Which type of Scaler? 1\n"
     ]
    }
   ],
   "source": [
    "# Standardization of Numerical Features (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.03</td>\n",
       "      <td>2.61</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.26</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>1.71</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>0.26</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.26</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.05</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.26</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  workclass  education  education-num  marital-status  occupation  \\\n",
       "0  0.03       2.61      -0.33           1.14            0.92       -1.55   \n",
       "1  0.83       1.71      -0.33           1.14           -0.41       -0.79   \n",
       "2 -0.05      -0.09       0.18          -0.42           -1.74       -0.29   \n",
       "3  1.05      -0.09      -2.40          -1.20           -0.41       -0.29   \n",
       "4 -0.78      -0.09      -0.33           1.14           -0.41        0.72   \n",
       "\n",
       "   relationship  race   sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0         -0.28  0.39  0.70          0.15         -0.22           -0.03   \n",
       "1         -0.90  0.39  0.70         -0.14         -0.22           -2.21   \n",
       "2         -0.28  0.39  0.70         -0.14         -0.22           -0.03   \n",
       "3         -0.90 -1.97  0.70         -0.14         -0.22           -0.03   \n",
       "4          2.22 -1.97 -1.42         -0.14         -0.22           -0.03   \n",
       "\n",
       "   native-country income  \n",
       "0            0.26  <=50K  \n",
       "1            0.26  <=50K  \n",
       "2            0.26  <=50K  \n",
       "3            0.26  <=50K  \n",
       "4           -5.38  <=50K  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier Detection & Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Outlier detection and treatment functionality under progress!'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection & Dimensionality Reduction not to be done in this submission as per the instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 9\n",
      "Random State? 42\n",
      "What % of data to be left for test? 20\n",
      "How many folds required? 5\n"
     ]
    }
   ],
   "source": [
    "# Train-Test Split and CV Folds (Pipeline Function)\n",
    "X_train,X_test,y_train,y_test,sk_folds=master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Selection (Pipeline Function)\n",
    "#master(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)\n",
    "# Output not shown in compiled notebook due to limited RAM\n",
    "# Cell below shows the relevant excerpt from the output when one dataset per notebook is executed separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this submission, our model of choice is KNN:\n",
    "\n",
    "# Model                            Accuracy  Balanced Accuracy  ROC AUC  F1 Score                                                                       \n",
    "# KNeighborsClassifier               0.83               0.74     None      0.82 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 11\n",
      "What is the value of K? 3\n"
     ]
    }
   ],
   "source": [
    "# Model Training (Pipeline Function)\n",
    "knn,scoresCV=master(data=df,X_train=X_train, X_test=X_test, y_train=y_train,sk_folds=sk_folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 12\n",
      "\n",
      "\n",
      "\n",
      "Train-Test Split Evaluation\n",
      "              precision  recall  f1-score  support\n",
      "<=50K              0.87    0.90      0.89  7414.00\n",
      ">50K               0.65    0.58      0.61  2355.00\n",
      "accuracy           0.82    0.82      0.82     0.82\n",
      "macro avg          0.76    0.74      0.75  9769.00\n",
      "weighted avg       0.82    0.82      0.82  9769.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Cross-Validation Evaluation\n",
      "Cross Validation Scores:  [0.8232163  0.82516122 0.82585995 0.83200246 0.8272932 ]\n",
      "Average CV Score:  0.8267066261768893\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation (Pipeline Function)\n",
    "master(knn=knn,X_test=X_test,y_test=y_test,scoresCV=scoresCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Result Interpretation\n",
    "\n",
    "### Classes: >50k , <=50k\n",
    "## Train-Test Split Evaluation:\n",
    "#### For the class '<=50K', the precision is 0.87, indicating that 87% of the instances classified as '<=50K' are actually in the '<=50K' income category.\n",
    "#### For the class '>50K', the precision is 0.65, indicating that 65% of the instances classified as '>50K' are actually in the '>50K' income category.\n",
    "#### For the class '<=50K', the recall is 0.90, indicating that 90% of the actual '<=50K' instances were correctly identified by the classifier.\n",
    "#### For the class '>50K', the recall is 0.58, indicating that 58% of the actual '>50K' instances were correctly identified by the classifier.\n",
    "#### For the class '<=50K', the F1-score is 0.89, representing the overall performance in terms of both precision and recall.\n",
    "#### For the class '>50K', the F1-score is 0.61, also representing the overall performance in terms of both precision and recall.\n",
    "#### Accuracy measures the overall correctness of the classifier across all classes. The overall accuracy of the classifier is 0.82, or 82%.\n",
    "\n",
    "#### AUC (Area Under the ROC Curve): The AUC score of 0.82 represents the model's ability to discriminate between positive and negative instances, with a higher value indicating better discrimination capability.\n",
    "\n",
    "## Cross-Validation Evaluation:\n",
    "\n",
    "#### Cross-Validation Scores: The cross-validation scores, ranging from 0.8232 to 0.8320, represent the accuracy achieved by the model across different folds of the dataset during cross-validation.\n",
    "#### Average CV Score: The average cross-validation score, calculated as 0.8267, provides an overall estimate of the model's performance across all folds. This score indicates the generalization ability of the model.\n",
    "#### Number of CV Scores used in Average: The average cross-validation score is based on 5 cross-validation scores, ensuring a robust estimate of the model's performance.\n",
    "\n",
    "\n",
    "#### In summary, both evaluations indicate that the model demonstrates favorable performance in terms of precision, recall, F1 score, accuracy, and AUC. The consistency of performance across cross-validation folds further validates the model's effectiveness and generalization ability.\n",
    "\n",
    "#### The Cross-Validation Evaluation shows slightly better results in terms of generalization performance. This suggests that the model's performance, as assessed through cross-validation, is slightly more reliable and indicative of its ability to generalize to unseen data compared to the single train-test split evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset - 2\n",
    "### Title: Credit Approval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 1\n",
      "Please enter the UCI ID 27\n",
      "Successfuly Connected to the Data Source\n"
     ]
    }
   ],
   "source": [
    "# Dataset Fetching (Pipeline Function)\n",
    "df,label_column=master()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A15</th>\n",
       "      <th>A14</th>\n",
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       "      <th>A12</th>\n",
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       "      <th>670</th>\n",
       "      <td>150</td>\n",
       "      <td>465.00</td>\n",
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       "      <th>671</th>\n",
       "      <td>2</td>\n",
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       "      <td>f</td>\n",
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       "      <th>672</th>\n",
       "      <td>117</td>\n",
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       "      <td>g</td>\n",
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       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
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       "      <td>0.83</td>\n",
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       "      <th>673</th>\n",
       "      <td>17</td>\n",
       "      <td>256.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>e</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>2.00</td>\n",
       "      <td>29.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>246</td>\n",
       "      <td>260.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.21</td>\n",
       "      <td>h</td>\n",
       "      <td>i</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>2.50</td>\n",
       "      <td>37.33</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>237</td>\n",
       "      <td>240.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.67</td>\n",
       "      <td>v</td>\n",
       "      <td>aa</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>1.04</td>\n",
       "      <td>41.58</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>3</td>\n",
       "      <td>129.00</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>12</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0.09</td>\n",
       "      <td>h</td>\n",
       "      <td>q</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>10.66</td>\n",
       "      <td>30.58</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0.04</td>\n",
       "      <td>v</td>\n",
       "      <td>m</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>7.25</td>\n",
       "      <td>19.42</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>10.21</td>\n",
       "      <td>17.92</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>v</td>\n",
       "      <td>c</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>1.25</td>\n",
       "      <td>20.08</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>364</td>\n",
       "      <td>280.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.29</td>\n",
       "      <td>v</td>\n",
       "      <td>k</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>0.29</td>\n",
       "      <td>19.50</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>537</td>\n",
       "      <td>176.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>3.00</td>\n",
       "      <td>h</td>\n",
       "      <td>d</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.83</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2</td>\n",
       "      <td>140.00</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.34</td>\n",
       "      <td>v</td>\n",
       "      <td>i</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>3.29</td>\n",
       "      <td>17.08</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>3</td>\n",
       "      <td>240.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.58</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>0.75</td>\n",
       "      <td>36.42</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>s</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>3.50</td>\n",
       "      <td>v</td>\n",
       "      <td>m</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>3.29</td>\n",
       "      <td>40.58</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0</td>\n",
       "      <td>260.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.25</td>\n",
       "      <td>h</td>\n",
       "      <td>e</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>10.09</td>\n",
       "      <td>21.08</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>394</td>\n",
       "      <td>200.00</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>2.00</td>\n",
       "      <td>v</td>\n",
       "      <td>c</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>0.75</td>\n",
       "      <td>22.67</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>1</td>\n",
       "      <td>200.00</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>2.00</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>13.50</td>\n",
       "      <td>25.25</td>\n",
       "      <td>a</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>750</td>\n",
       "      <td>280.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.04</td>\n",
       "      <td>v</td>\n",
       "      <td>aa</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>0.20</td>\n",
       "      <td>17.92</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>g</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>8.29</td>\n",
       "      <td>h</td>\n",
       "      <td>c</td>\n",
       "      <td>g</td>\n",
       "      <td>u</td>\n",
       "      <td>3.38</td>\n",
       "      <td>35.00</td>\n",
       "      <td>b</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A15    A14 A13 A12  A11 A10 A9   A8  A7  A6 A5 A4    A3    A2   A1 A16\n",
       "670  150 465.00   g   f    0   f  f 5.50   v   w  g  u  5.83 47.17    b   -\n",
       "671    2   0.00   g   f    0   f  f 0.50   v  cc  g  u 12.84 25.83    b   -\n",
       "672  117 240.00   g   t    0   f  f 0.50   v  aa  g  u  0.83 50.25    a   -\n",
       "673   17 256.00   g   f    0   f  f 2.00   h   e  p  y  2.00 29.50  NaN   -\n",
       "674  246 260.00   g   f    0   f  f 0.21   h   i  g  u  2.50 37.33    a   -\n",
       "675  237 240.00   g   f    0   f  f 0.67   v  aa  g  u  1.04 41.58    a   -\n",
       "676    3 129.00   g   t   12   t  f 0.09   h   q  g  u 10.66 30.58    a   -\n",
       "677    1 100.00   g   f    1   t  f 0.04   v   m  g  u  7.25 19.42    b   -\n",
       "678   50   0.00   g   f    0   f  f 0.00  ff  ff  g  u 10.21 17.92    a   -\n",
       "679    0   0.00   g   f    0   f  f 0.00   v   c  g  u  1.25 20.08    a   -\n",
       "680  364 280.00   g   f    0   f  f 0.29   v   k  g  u  0.29 19.50    b   -\n",
       "681  537 176.00   g   f    0   f  f 3.00   h   d  p  y  1.00 27.83    b   -\n",
       "682    2 140.00   g   t    0   f  f 0.34   v   i  g  u  3.29 17.08    b   -\n",
       "683    3 240.00   g   f    0   f  f 0.58   v   d  p  y  0.75 36.42    b   -\n",
       "684    0 400.00   s   t    0   f  f 3.50   v   m  g  u  3.29 40.58    b   -\n",
       "685    0 260.00   g   f    0   f  f 1.25   h   e  p  y 10.09 21.08    b   -\n",
       "686  394 200.00   g   t    2   t  f 2.00   v   c  g  u  0.75 22.67    a   -\n",
       "687    1 200.00   g   t    1   t  f 2.00  ff  ff  p  y 13.50 25.25    a   -\n",
       "688  750 280.00   g   f    0   f  f 0.04   v  aa  g  u  0.20 17.92    b   -\n",
       "689    0   0.00   g   t    0   f  f 8.29   h   c  g  u  3.38 35.00    b   -"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values are Present\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A15     690 non-null    int64  \n",
      " 1   A14     677 non-null    float64\n",
      " 2   A13     690 non-null    object \n",
      " 3   A12     690 non-null    object \n",
      " 4   A11     690 non-null    int64  \n",
      " 5   A10     690 non-null    object \n",
      " 6   A9      690 non-null    object \n",
      " 7   A8      690 non-null    float64\n",
      " 8   A7      681 non-null    object \n",
      " 9   A6      681 non-null    object \n",
      " 10  A5      684 non-null    object \n",
      " 11  A4      684 non-null    object \n",
      " 12  A3      690 non-null    float64\n",
      " 13  A2      678 non-null    float64\n",
      " 14  A1      678 non-null    object \n",
      " 15  A16     690 non-null    object \n",
      "dtypes: float64(4), int64(2), object(10)\n",
      "memory usage: 86.4+ KB\n",
      "None\n",
      "\n",
      "-    383\n",
      "+    307\n",
      "Name: A16, dtype: int64\n"
     ]
    },
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       "      <td>0</td>\n",
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       "      <th>685</th>\n",
       "      <td>0</td>\n",
       "      <td>260.00</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
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       "      <th>686</th>\n",
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      ],
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       "     A15    A14 A13 A12  A11 A10 A9   A8  A7  A6 A5 A4    A3    A2 A1 A16\n",
       "0      0 202.00   g   f    1   t  t 1.25   v   w  g  u  0.00 30.83  b   +\n",
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       "4      0 120.00   s   f    0   f  t 1.71   v   w  g  u  5.62 20.17  b   +\n",
       "..   ...    ...  ..  ..  ...  .. ..  ...  ..  .. .. ..   ...   ... ..  ..\n",
       "685    0 260.00   g   f    0   f  f 1.25   h   e  p  y 10.09 21.08  b   -\n",
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       "688  750 280.00   g   f    0   f  f 0.04   v  aa  g  u  0.20 17.92  b   -\n",
       "689    0   0.00   g   t    0   f  f 8.29   h   c  g  u  3.38 35.00  b   -\n",
       "\n",
       "[690 rows x 16 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Checking\n",
    "# No Manual Data Type Correction and Removal of Redundant Column Required \n",
    "print(df.info())\n",
    "\n",
    "# No Manual label Correction Required\n",
    "print(\"\")\n",
    "print(df[label_column].value_counts())\n",
    "\n",
    "# No Manual Data Entry Error Correction Required\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 2\n",
      "Dataset is treated for missing values successfully\n",
      " \n",
      "A15    0\n",
      "A14    0\n",
      "A13    0\n",
      "A12    0\n",
      "A11    0\n",
      "A10    0\n",
      "A9     0\n",
      "A8     0\n",
      "A7     0\n",
      "A6     0\n",
      "A5     0\n",
      "A4     0\n",
      "A3     0\n",
      "A2     0\n",
      "A1     0\n",
      "A16    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing Value Treatment (Pipeline Function)\n",
    "master(df)\n",
    "print(\" \")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>1.54</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   A15    A14 A13 A12  A11 A10 A9   A8 A7 A6 A5 A4   A3    A2 A1 A16\n",
       "0    0 202.00   g   f    1   t  t 1.25  v  w  g  u 0.00 30.83  b   +\n",
       "1  560  43.00   g   f    6   t  t 3.04  h  q  g  u 4.46 58.67  a   +\n",
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       "3    3 100.00   g   t    5   t  t 3.75  v  w  g  u 1.54 27.83  b   +\n",
       "4    0 120.00   s   f    0   f  t 1.71  v  w  g  u 5.62 20.17  b   +"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 3\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690.00</td>\n",
       "      <td>690.00</td>\n",
       "      <td>690.00</td>\n",
       "      <td>690.00</td>\n",
       "      <td>690.00</td>\n",
       "      <td>690.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1017.39</td>\n",
       "      <td>183.56</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.22</td>\n",
       "      <td>4.76</td>\n",
       "      <td>31.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5210.10</td>\n",
       "      <td>172.19</td>\n",
       "      <td>4.86</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.98</td>\n",
       "      <td>11.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.00</td>\n",
       "      <td>22.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.00</td>\n",
       "      <td>160.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>28.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>395.50</td>\n",
       "      <td>272.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.62</td>\n",
       "      <td>7.21</td>\n",
       "      <td>37.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>28.50</td>\n",
       "      <td>28.00</td>\n",
       "      <td>80.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A15     A14    A11     A8     A3     A2\n",
       "count    690.00  690.00 690.00 690.00 690.00 690.00\n",
       "mean    1017.39  183.56   2.40   2.22   4.76  31.51\n",
       "std     5210.10  172.19   4.86   3.35   4.98  11.86\n",
       "min        0.00    0.00   0.00   0.00   0.00  13.75\n",
       "25%        0.00   80.00   0.00   0.17   1.00  22.67\n",
       "50%        5.00  160.00   0.00   1.00   2.75  28.46\n",
       "75%      395.50  272.00   3.00   2.62   7.21  37.71\n",
       "max   100000.00 2000.00  67.00  28.50  28.00  80.25"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical Features Summary (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-   55.51\n",
       "+   44.49\n",
       "Name: A16, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Feature Class Balance (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 5\n",
      "\n",
      "Box and Distribution Plot - A8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 2.885041123784296\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 11.110512282615252\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "4506.198304368205 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - A11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 5.141311956483638\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 50.453165554366365\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "76223.56023351505 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - A15\n"
     ]
    },
    {
     "data": {
      "image/png": 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5/75rwZi5fX2geY8fm9FLkqTFtZnRv5aPAfcluQH4AfBBgKp6JMl9wKPALHBTVZ1oxtwIfBZ4M/Bg8wC4G/hckil6M/mdp6FfSZI6K12bII+MjNTk5OSg25Ak6YxJ8nBVjSy2zjvjSZLUYQa9JEkdZtBLktRhBr0kSR1m0EuS1GEGvSRJHWbQS5LUYQa9pBUxMzPDnj17mJmZGXQrkvoY9JJWxPj4OIcOHWLfvn2DbkVSH4NeUmszMzNMTExQVUxMTDirl1YRg15Sa+Pj45w8eRKAEydOOKuXVhGDXlJrBw4cYHZ2FoDZ2Vn2798/4I4kzTHoJbW2bds2hoZ6P4Y5NDTE9u3bB9yRpDkGvaTWxsbGWLeu98/J+vXr2bVr14A7kjTHoJfU2vDwMKOjoyRhdHSU4eHhQbckqTE06AYkdcPY2BiHDx92Ni+tMs7oJUnqMINe0orwhjnS6mTQS2rNG+ZIq5dBL6k1b5gjrV4GvaTWvGGOtHoZ9JJa84Y50upl0EtqzRvmSKuXQS+pNW+YI61e3jBH0orwhjnS6mTQS1oRw8PD7N27d9BtSFrAQ/eSJHWYQS9JUocZ9JIkdZhBL0lShy076JNcmuS/J3ksySNJPtzUL0iyP8kTzfP5fWNuSzKV5PEk1/bVr0xyqFm3N0ma+oYk9zb1h5JsafFZJUlac9rM6GeBf1xVfwm4GrgpyRXArcDBqtoKHGyWadbtBN4FjAKfTrK+2dcdwG5ga/MYbeo3AM9X1eXA7cDHW/QrSdKas+ygr6pnquo7zesXgceATcAOYLzZbBy4vnm9A7inql6pqieBKeCqJJcA51TVN6uqgH0Lxszt637gmrnZviRJOrUVOUffHFJ/D/AQcHFVPQO9LwPARc1mm4Cn+oZNN7VNzeuF9XljqmoWeAH4sVtuJdmdZDLJ5PHjx1fiI0n6/zQzM8OePXv8iVpplWkd9EnOBn4P+IdV9cdLbbpIrZaoLzVmfqHqzqoaqaqRjRs3nqplSafB+Pg4hw4d8idqpVWmVdAneRO9kP+dqvp8Uz7aHI6neT7W1KeBS/uGbwaebuqbF6nPG5NkCDgXeK5Nz5JW3szMDBMTE1QVExMTzuqlVaTNVfcB7gYeq6p/17fqAWCseT0GfKGvvrO5kv4yehfdfas5vP9ikqubfe5aMGZuXx8Avtqcx5e0ioyPj3Py5EkATpw44axeWkXazOh/FvhV4OeS/EHz+HngY8D2JE8A25tlquoR4D7gUWACuKmqTjT7uhG4i94Fev8HeLCp3w0MJ5kCfp3mCn5Jq8uBAweYnZ0FYHZ2lv379w+4I0lzlv2jNlX1+yx+Dh3gmtcY81Hgo4vUJ4F3L1L/EfDB5fYo6czYtm0bX/rSl5idnWVoaIjt27cPuiVJDe+MJ6m1sbEx5v7yNYk/VSutIga9pNaGh4fZsGEDABs2bGB4+Mf+ClbSgBj0klqbmpripZdeAuCll15iampqwB1JmmPQS2rtIx/5yJLLkgbHoJfU2uHDh5dcljQ4Br2k1rZs2bLksqTBMegltXbzzTfPW77lllsG1ImkhQx6Sa194xvfWHJZ0uAY9JJaO3DgwLxl74wnrR4GvaTWtm3bNm/ZO+NJq4dBL6m19773vUsuSxocg15Sa5/85CeXXJY0OAa9pNamp6fnLT/11FMD6kTSQga9JEkdZtBLktRhBr2k1uZ+ova1liUNjkEvqTWDXlq9DHpJrV188cXzlt/2trcNqBNJCxn0klo7duzYvOWjR48OqBNJCxn0klo7ceLEksuSBseglySpwwx6SZI6zKCXJKnDDHpJkjrMoJckqcMMekmSOsyglySpwwx6SZI6zKCXJKnDDHpJkjrMoJckqcOGBt3A65FkFPhNYD1wV1V9bMAt6Q3kU5/6FFNTU4NuY8358Ic/POgWOunyyy/nlltuGXQbegNZ9UGfZD3w74HtwDTw7SQPVNWjg+1sZRhCp9+RI0f44Q9/OOg21hz/vz49jhw54n/b06xrX6ZWfdADVwFTVfU9gCT3ADuATgT917/+dZ599tlBtyGtuJdffnnQLXTSyy+/7L8Zp9mRI0cM+jNsE/BU3/I08DP9GyTZDewGeMc73nHmOlsB5513nrPN0+yVV17h5MmTg26j0xb777tunZcAnQ7r1q1jw4YNg26j084777xBt7Ci3ghBn0VqNW+h6k7gToCRkZFaZPtV66677hp0C9KKeN/73venr7/2ta8NrA9J870RvnJPA5f2LW8Gnh5QL5Jew1lnnQXAW9/61gF3IqnfG2FG/21ga5LLgCPATuDvD7YlSQt98YtfHHQLkhax6oO+qmaT3Ax8md6f132mqh4ZcFuSJL0hrPqgB6iqLwFfGnQfkiS90bwRztFLkqRlMuglSeowg16SpA4z6CVJ6rBUvaHuL3NKSY4D3x90H9IadSHg/VmlM++dVbVxsRWdC3pJg5NksqpGBt2HpD/joXtJkjrMoJckqcMMekkr6c5BNyBpPs/RS5LUYc7oJUnqMINekqQOM+gltZZkNMnjSaaS3DrofiT9Gc/RS2olyXrgfwPbgWng28Dfq6pHB9qYJMAZvaT2rgKmqup7VfUnwD3AjgH3JKlh0EtqaxPwVN/ydFOTtAoY9JLayiI1zwlKq4RBL6mtaeDSvuXNwNMD6kXSAga9pLa+DWxNclmSnwB2Ag8MuCdJjaFBNyDpja2qZpPcDHwZWA98pqoeGXBbkhr+eZ0kSR3moXtJkjrMoJckqcMMekmSOsyglySpwwx6SZI6zKCXJKnDDHpJkjrs/wElT0kS3bflIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 13.112071105201627\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 213.10886283673105\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "1325463.9253092878 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - A3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.4855746179702647\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 2.2488937993362494\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "399.20096920903694 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - A2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.1647650704131591\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 1.1861668575026014\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "196.4689466121067 0.0\n",
      "Distribution is not normal \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Histograms and Boxplots of important numerical variables (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Interpretation\n",
    "\n",
    "### This file concerns credit card applications.  All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.\n",
    "\n",
    "## 1. A11\n",
    "\n",
    "#### Based on the box plot analysis, it is observed that approximately 50% of the data points are distributed within the range of 0 to 3. The positioning of the median line towards the bottom box indicates a positive skewness in the distribution.\n",
    "\n",
    "#### Heavy right-skewness, with a concentration of data towards lower values and a long tail towards higher values.\n",
    "#### Leptokurtosis, indicating heavier tails and a sharper peak compared to a normal distribution.\n",
    "#### Significant departure from normality, as indicated by both the skewness, kurtosis, and the Jarque-Bera test. This suggests that the data distribution significantly deviates from the assumptions of a normal distribution, which should be considered in subsequent analyses or modeling.\n",
    "\n",
    "## 2. A8\n",
    "\n",
    "#### The box plot reveals that 50% of the data values lie within the range of 0.17 to 2.62. Additionally, the median line is positioned towards the bottom of the box. These observations indicate a positively skewed distribution.\n",
    "\n",
    "#### Right-skewness, with a concentration of data towards lower values and a tail towards higher values.\n",
    "#### Leptokurtosis, indicating heavier tails and a sharper peak compared to a normal distribution.\n",
    "#### Significant deviation from normality, as evidenced by both the skewness, kurtosis, and the Jarque-Bera test. This suggests that the data distribution markedly deviates from the expected characteristics of a normal distribution, necessitating careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 3. A15\n",
    "\n",
    "#### Based on the box plot analysis, it is observed that the interquartile range (IQR) spans from 0 to 5, with the median lying towards the lower quartile, indicative of a positively skewed distribution.\n",
    "\n",
    "#### Extreme right-skewness, with an overwhelming concentration of data towards lower values and an extensive tail towards higher values.\n",
    "#### Extremely high leptokurtosis, indicating an extraordinary concentration of data around the central peak with heavier tails compared to a normal distribution.\n",
    "#### An extreme deviation from normality, as indicated by the highly elevated skewness and kurtosis values, as well as the extremely large Jarque-Bera test statistic and associated p-value. This underscores a significant departure from the expected characteristics of a normal distribution, necessitating careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 4. A2\n",
    "\n",
    "#### The interquartile range (IQR) suggests that 50% of the values lie between 22.67 and 37.71. Additionally, the median line is positioned towards the bottom of the box. These observations collectively indicate a positively skewed distribution.\n",
    "\n",
    "#### The data distribution is moderately right-skewed, with a moderate concentration of data towards lower values and a tail towards higher values.\n",
    "#### The distribution is platykurtic, indicating lighter tails and a flatter peak compared to a normal distribution.\n",
    "#### The Jarque-Bera test confirms a deviation from normality, supported by the associated p-value, highlighting the need for careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 5. A3\n",
    "\n",
    "#### Based on the box plot analysis, it is observed that 50% of the data values fall within the range of 1 to 7.21. Additionally, the median line in the box plot tends towards the lower quartile, indicating a positively skewed distribution. This skewness suggests that there is a concentration of data towards lower values, with a tail extending towards higher values.\n",
    "\n",
    "#### The data distribution is moderately right-skewed, with a moderate to high concentration of data towards lower values and a tail towards higher values.\n",
    "#### The distribution is platykurtic, indicating lighter tails and a flatter peak compared to a normal distribution.\n",
    "#### The Jarque-Bera test confirms a deviation from normality, supported by the associated p-value, highlighting the need for careful consideration in subsequent analyses or modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 6\n"
     ]
    }
   ],
   "source": [
    "#Categorical Encoding (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 7\n",
      "1- Standard Scaler\n",
      "2- Min-Max Scaler\n",
      "Which type of Scaler? 1\n"
     ]
    }
   ],
   "source": [
    "# Standardization of Numerical Features (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A15</th>\n",
       "      <th>A14</th>\n",
       "      <th>A13</th>\n",
       "      <th>A12</th>\n",
       "      <th>A11</th>\n",
       "      <th>A10</th>\n",
       "      <th>A9</th>\n",
       "      <th>A8</th>\n",
       "      <th>A7</th>\n",
       "      <th>A6</th>\n",
       "      <th>A5</th>\n",
       "      <th>A4</th>\n",
       "      <th>A3</th>\n",
       "      <th>A2</th>\n",
       "      <th>A1</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.66</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>2.29</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.66</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>3.27</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>0.66</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A15   A14   A13   A12   A11   A10   A9    A8    A7   A6    A5    A4    A3  \\\n",
       "0 -0.20  0.11 -0.32 -0.92 -0.29  1.16 0.95 -0.29  0.76 1.47 -0.56 -0.54 -0.96   \n",
       "1 -0.09 -0.82 -0.32 -0.92  0.74  1.16 0.95  0.24 -0.84 1.00 -0.56 -0.54 -0.06   \n",
       "2 -0.04  0.56 -0.32 -0.92 -0.49 -0.86 0.95 -0.22 -0.84 1.00 -0.56 -0.54 -0.86   \n",
       "3 -0.19 -0.49 -0.32  1.09  0.54  1.16 0.95  0.46  0.76 1.47 -0.56 -0.54 -0.65   \n",
       "4 -0.20 -0.37  3.27 -0.92 -0.49 -0.86 0.95 -0.15  0.76 1.47 -0.56 -0.54  0.17   \n",
       "\n",
       "     A2    A1 A16  \n",
       "0 -0.06  0.66   +  \n",
       "1  2.29 -1.51   +  \n",
       "2 -0.59 -1.51   +  \n",
       "3 -0.31  0.66   +  \n",
       "4 -0.96  0.66   +  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier Detection & Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Outlier detection and treatment functionality under progress!'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection & Dimensionality Reduction not to be done in this submission as per the instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 9\n",
      "Random State? 42\n",
      "What % of data to be left for test? 20\n",
      "How many folds required? 5\n"
     ]
    }
   ],
   "source": [
    "# Train-Test Split and CV Folds (Pipeline Function)\n",
    "X_train,X_test,y_train,y_test,sk_folds=master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 18.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 315, number of negative: 237\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000172 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 476\n",
      "[LightGBM] [Info] Number of data points in the train set: 552, number of used features: 15\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.570652 -> initscore=0.284512\n",
      "[LightGBM] [Info] Start training from score 0.284512\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "                               Accuracy  Balanced Accuracy ROC AUC  F1 Score  \\\n",
      "Model                                                                          \n",
      "RandomForestClassifier             0.86               0.86    None      0.86   \n",
      "KNeighborsClassifier               0.86               0.86    None      0.85   \n",
      "ExtraTreesClassifier               0.86               0.86    None      0.86   \n",
      "BaggingClassifier                  0.85               0.85    None      0.85   \n",
      "LogisticRegression                 0.84               0.84    None      0.84   \n",
      "SVC                                0.84               0.84    None      0.84   \n",
      "NuSVC                              0.84               0.84    None      0.84   \n",
      "CalibratedClassifierCV             0.84               0.84    None      0.84   \n",
      "LGBMClassifier                     0.83               0.83    None      0.83   \n",
      "BernoulliNB                        0.83               0.83    None      0.83   \n",
      "RidgeClassifierCV                  0.83               0.83    None      0.83   \n",
      "RidgeClassifier                    0.83               0.83    None      0.83   \n",
      "LinearSVC                          0.83               0.83    None      0.83   \n",
      "LinearDiscriminantAnalysis         0.83               0.83    None      0.83   \n",
      "NearestCentroid                    0.81               0.81    None      0.81   \n",
      "PassiveAggressiveClassifier        0.80               0.80    None      0.80   \n",
      "SGDClassifier                      0.80               0.80    None      0.80   \n",
      "AdaBoostClassifier                 0.79               0.79    None      0.79   \n",
      "DecisionTreeClassifier             0.78               0.78    None      0.78   \n",
      "ExtraTreeClassifier                0.78               0.78    None      0.78   \n",
      "GaussianNB                         0.77               0.77    None      0.77   \n",
      "LabelSpreading                     0.75               0.75    None      0.74   \n",
      "LabelPropagation                   0.75               0.75    None      0.74   \n",
      "Perceptron                         0.74               0.74    None      0.74   \n",
      "QuadraticDiscriminantAnalysis      0.57               0.57    None      0.47   \n",
      "DummyClassifier                    0.49               0.50    None      0.33   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "RandomForestClassifier               0.24  \n",
      "KNeighborsClassifier                 0.04  \n",
      "ExtraTreesClassifier                 0.17  \n",
      "BaggingClassifier                    0.05  \n",
      "LogisticRegression                   0.02  \n",
      "SVC                                  0.03  \n",
      "NuSVC                                0.04  \n",
      "CalibratedClassifierCV               0.12  \n",
      "LGBMClassifier                       0.26  \n",
      "BernoulliNB                          0.01  \n",
      "RidgeClassifierCV                    0.02  \n",
      "RidgeClassifier                      0.02  \n",
      "LinearSVC                            0.05  \n",
      "LinearDiscriminantAnalysis           0.02  \n",
      "NearestCentroid                      0.02  \n",
      "PassiveAggressiveClassifier          0.02  \n",
      "SGDClassifier                        0.02  \n",
      "AdaBoostClassifier                   0.14  \n",
      "DecisionTreeClassifier               0.02  \n",
      "ExtraTreeClassifier                  0.01  \n",
      "GaussianNB                           0.02  \n",
      "LabelSpreading                       0.04  \n",
      "LabelPropagation                     0.04  \n",
      "Perceptron                           0.02  \n",
      "QuadraticDiscriminantAnalysis        0.02  \n",
      "DummyClassifier                      0.02  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Selection (Pipeline Function)\n",
    "master(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this submission, our model of choice is KNN:\n",
    "\n",
    "# Model                            Accuracy  Balanced Accuracy  ROC AUC  F1 Score                                                                       \n",
    "# KNeighborsClassifier               0.86               0.86     None      0.85 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 11\n",
      "What is the value of K? 3\n"
     ]
    }
   ],
   "source": [
    "# Model Training (Pipeline Function)\n",
    "knn,scoresCV=master(data=df,X_train=X_train, X_test=X_test, y_train=y_train,sk_folds=sk_folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 12\n",
      "\n",
      "\n",
      "\n",
      "Train-Test Split Evaluation\n",
      "              precision  recall  f1-score  support\n",
      "+                  0.88    0.74      0.81    70.00\n",
      "-                  0.77    0.90      0.83    68.00\n",
      "accuracy           0.82    0.82      0.82     0.82\n",
      "macro avg          0.83    0.82      0.82   138.00\n",
      "weighted avg       0.83    0.82      0.82   138.00\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Cross-Validation Evaluation\n",
      "Cross Validation Scores:  [0.83333333 0.81884058 0.88405797 0.85507246 0.89130435]\n",
      "Average CV Score:  0.8565217391304347\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation (Pipeline Function)\n",
    "master(knn=knn,X_test=X_test,y_test=y_test,scoresCV=scoresCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Result Interpretation\n",
    "### Classes: + , -\n",
    "## Train-Test Split Evaluation:\n",
    "\n",
    "#### For class '+', the precision is 0.88, indicating that 88% of the instances classified as '+' are actually positive.\n",
    "#### For class '-', the precision is 0.77, indicating that 77% of the instances classified as '-' are actually negative.\n",
    "#### For class '+', the recall is 0.74, indicating that 74% of the actual '+' instances were correctly identified by the classifier.\n",
    "#### For class '-', the recall is 0.90, indicating that 90% of the actual '-' instances were correctly identified by the classifier.\n",
    "#### For class '+', the F1-score is 0.81, representing the overall performance in terms of both precision and recall.\n",
    "#### For class '-', the F1-score is 0.83, also representing the overall performance in terms of both precision and recall.\n",
    "#### Accuracy measures the overall correctness of the classifier across all classes. The overall accuracy of the classifier is 0.82, or 82%.\n",
    "#### AUC (Area Under the ROC Curve): 0.86 - The AUC score of 0.86 represents the model's ability to discriminate between positive and negative instances, with a higher value indicating better discrimination capability.\n",
    "\n",
    "## Cross-Validation Evaluation:\n",
    "\n",
    "#### Cross-Validation Scores: [0.8333, 0.8188, 0.8841, 0.8551, 0.8913]\n",
    "#### Average CV Score: 0.8565\n",
    "#### Number of CV Scores used in Average: 5\n",
    "\n",
    "\n",
    "#### Comparing the average cross-validation score (0.8565) with the metrics from the train-test split evaluation, it appears that the average cross-validation score is slightly higher than the accuracy obtained from the train-test split (0.8188).\n",
    "\n",
    "#### Therefore, based on these metrics, the Cross-Validation Evaluation shows slightly better results in terms of generalization performance. This suggests that the model's performance, as assessed through cross-validation, is slightly more reliable and indicative of its ability to generalize to unseen data compared to the single train-test split evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset - 3\n",
    "### Title: Mushroom Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 1\n",
      "Please enter the UCI ID 73\n",
      "Successfuly Connected to the Data Source\n"
     ]
    }
   ],
   "source": [
    "# Dataset Fetching (Pipeline Function)\n",
    "df,label_column=master()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>s</td>\n",
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       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cap-surface cap-color bruises odor gill-attachment gill-spacing  \\\n",
       "8104           s         n       f    n               a            c   \n",
       "8105           s         n       f    n               a            c   \n",
       "8106           s         n       f    n               a            c   \n",
       "8107           s         n       f    n               a            c   \n",
       "8108           y         e       f    y               f            c   \n",
       "8109           s         w       f    n               f            w   \n",
       "8110           s         n       f    n               a            c   \n",
       "8111           s         w       f    n               f            w   \n",
       "8112           s         n       f    n               a            c   \n",
       "8113           y         e       f    y               f            c   \n",
       "8114           y         c       f    m               a            c   \n",
       "8115           s         n       f    n               a            c   \n",
       "8116           y         n       f    s               f            c   \n",
       "8117           s         e       f    y               f            c   \n",
       "8118           y         n       f    f               f            c   \n",
       "8119           s         n       f    n               a            c   \n",
       "8120           s         n       f    n               a            c   \n",
       "8121           s         n       f    n               a            c   \n",
       "8122           y         n       f    y               f            c   \n",
       "8123           s         n       f    n               a            c   \n",
       "\n",
       "     gill-size gill-color stalk-shape stalk-root  ... stalk-color-above-ring  \\\n",
       "8104         b          y           e        NaN  ...                      o   \n",
       "8105         b          y           e        NaN  ...                      o   \n",
       "8106         b          o           e        NaN  ...                      o   \n",
       "8107         b          y           e        NaN  ...                      o   \n",
       "8108         n          b           t        NaN  ...                      p   \n",
       "8109         b          w           e        NaN  ...                      w   \n",
       "8110         b          o           e        NaN  ...                      o   \n",
       "8111         b          p           e        NaN  ...                      w   \n",
       "8112         b          o           e        NaN  ...                      o   \n",
       "8113         n          b           t        NaN  ...                      p   \n",
       "8114         b          y           e          c  ...                      c   \n",
       "8115         b          y           e        NaN  ...                      o   \n",
       "8116         n          b           t        NaN  ...                      p   \n",
       "8117         n          b           t        NaN  ...                      p   \n",
       "8118         n          b           t        NaN  ...                      p   \n",
       "8119         b          y           e        NaN  ...                      o   \n",
       "8120         b          y           e        NaN  ...                      o   \n",
       "8121         b          n           e        NaN  ...                      o   \n",
       "8122         n          b           t        NaN  ...                      w   \n",
       "8123         b          y           e        NaN  ...                      o   \n",
       "\n",
       "     stalk-color-below-ring veil-type veil-color ring-number ring-type  \\\n",
       "8104                      o         p          o           o         p   \n",
       "8105                      o         p          n           o         p   \n",
       "8106                      o         p          o           o         p   \n",
       "8107                      o         p          o           o         p   \n",
       "8108                      w         p          w           o         e   \n",
       "8109                      w         p          w           t         p   \n",
       "8110                      o         p          o           o         p   \n",
       "8111                      w         p          w           t         p   \n",
       "8112                      o         p          n           o         p   \n",
       "8113                      p         p          w           o         e   \n",
       "8114                      c         p          w           n         n   \n",
       "8115                      o         p          o           o         p   \n",
       "8116                      w         p          w           o         e   \n",
       "8117                      w         p          w           o         e   \n",
       "8118                      w         p          w           o         e   \n",
       "8119                      o         p          o           o         p   \n",
       "8120                      o         p          n           o         p   \n",
       "8121                      o         p          o           o         p   \n",
       "8122                      w         p          w           o         e   \n",
       "8123                      o         p          o           o         p   \n",
       "\n",
       "     spore-print-color population habitat poisonous  \n",
       "8104                 n          v       l         e  \n",
       "8105                 y          v       l         e  \n",
       "8106                 n          v       l         e  \n",
       "8107                 n          c       l         e  \n",
       "8108                 w          v       l         p  \n",
       "8109                 w          n       g         e  \n",
       "8110                 n          v       l         e  \n",
       "8111                 w          n       g         e  \n",
       "8112                 b          v       l         e  \n",
       "8113                 w          v       d         p  \n",
       "8114                 w          c       d         p  \n",
       "8115                 o          v       l         e  \n",
       "8116                 w          v       l         p  \n",
       "8117                 w          v       d         p  \n",
       "8118                 w          v       d         p  \n",
       "8119                 b          c       l         e  \n",
       "8120                 b          v       l         e  \n",
       "8121                 b          c       l         e  \n",
       "8122                 w          v       l         p  \n",
       "8123                 o          c       l         e  \n",
       "\n",
       "[20 rows x 22 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values are Present\n",
    "df.iloc[:,1:].tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   cap-shape                 8124 non-null   object\n",
      " 1   cap-surface               8124 non-null   object\n",
      " 2   cap-color                 8124 non-null   object\n",
      " 3   bruises                   8124 non-null   object\n",
      " 4   odor                      8124 non-null   object\n",
      " 5   gill-attachment           8124 non-null   object\n",
      " 6   gill-spacing              8124 non-null   object\n",
      " 7   gill-size                 8124 non-null   object\n",
      " 8   gill-color                8124 non-null   object\n",
      " 9   stalk-shape               8124 non-null   object\n",
      " 10  stalk-root                5644 non-null   object\n",
      " 11  stalk-surface-above-ring  8124 non-null   object\n",
      " 12  stalk-surface-below-ring  8124 non-null   object\n",
      " 13  stalk-color-above-ring    8124 non-null   object\n",
      " 14  stalk-color-below-ring    8124 non-null   object\n",
      " 15  veil-type                 8124 non-null   object\n",
      " 16  veil-color                8124 non-null   object\n",
      " 17  ring-number               8124 non-null   object\n",
      " 18  ring-type                 8124 non-null   object\n",
      " 19  spore-print-color         8124 non-null   object\n",
      " 20  population                8124 non-null   object\n",
      " 21  habitat                   8124 non-null   object\n",
      " 22  poisonous                 8124 non-null   object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n",
      "None\n",
      "\n",
      "e    4208\n",
      "p    3916\n",
      "Name: poisonous, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "      <th>poisonous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
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       "      <td>t</td>\n",
       "      <td>p</td>\n",
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       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
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       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8120</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8121</th>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>t</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0            x           s         n       t    p               f   \n",
       "1            x           s         y       t    a               f   \n",
       "2            b           s         w       t    l               f   \n",
       "3            x           y         w       t    p               f   \n",
       "4            x           s         g       f    n               f   \n",
       "...        ...         ...       ...     ...  ...             ...   \n",
       "8119         k           s         n       f    n               a   \n",
       "8120         x           s         n       f    n               a   \n",
       "8121         f           s         n       f    n               a   \n",
       "8122         k           y         n       f    y               f   \n",
       "8123         x           s         n       f    n               a   \n",
       "\n",
       "     gill-spacing gill-size gill-color stalk-shape  ...  \\\n",
       "0               c         n          k           e  ...   \n",
       "1               c         b          k           e  ...   \n",
       "2               c         b          n           e  ...   \n",
       "3               c         n          n           e  ...   \n",
       "4               w         b          k           t  ...   \n",
       "...           ...       ...        ...         ...  ...   \n",
       "8119            c         b          y           e  ...   \n",
       "8120            c         b          y           e  ...   \n",
       "8121            c         b          n           e  ...   \n",
       "8122            c         n          b           t  ...   \n",
       "8123            c         b          y           e  ...   \n",
       "\n",
       "     stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                         w                      w         p          w   \n",
       "1                         w                      w         p          w   \n",
       "2                         w                      w         p          w   \n",
       "3                         w                      w         p          w   \n",
       "4                         w                      w         p          w   \n",
       "...                     ...                    ...       ...        ...   \n",
       "8119                      o                      o         p          o   \n",
       "8120                      o                      o         p          n   \n",
       "8121                      o                      o         p          o   \n",
       "8122                      w                      w         p          w   \n",
       "8123                      o                      o         p          o   \n",
       "\n",
       "     ring-number ring-type spore-print-color population habitat poisonous  \n",
       "0              o         p                 k          s       u         p  \n",
       "1              o         p                 n          n       g         e  \n",
       "2              o         p                 n          n       m         e  \n",
       "3              o         p                 k          s       u         p  \n",
       "4              o         e                 n          a       g         e  \n",
       "...          ...       ...               ...        ...     ...       ...  \n",
       "8119           o         p                 b          c       l         e  \n",
       "8120           o         p                 b          v       l         e  \n",
       "8121           o         p                 b          c       l         e  \n",
       "8122           o         e                 w          v       l         p  \n",
       "8123           o         p                 o          c       l         e  \n",
       "\n",
       "[8124 rows x 23 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Checking\n",
    "# No Manual Data Type Correction and Removal of Redundant Column Required\n",
    "print(df.info())\n",
    "\n",
    "# No Manual label Correction Required\n",
    "print(\"\")\n",
    "print(df[label_column].value_counts())\n",
    "\n",
    "# No Manual Data Entry Error Correction Required\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 2\n",
      "Dataset is treated for missing values successfully\n",
      " \n",
      "cap-shape                   0\n",
      "cap-surface                 0\n",
      "cap-color                   0\n",
      "bruises                     0\n",
      "odor                        0\n",
      "gill-attachment             0\n",
      "gill-spacing                0\n",
      "gill-size                   0\n",
      "gill-color                  0\n",
      "stalk-shape                 0\n",
      "stalk-root                  0\n",
      "stalk-surface-above-ring    0\n",
      "stalk-surface-below-ring    0\n",
      "stalk-color-above-ring      0\n",
      "stalk-color-below-ring      0\n",
      "veil-type                   0\n",
      "veil-color                  0\n",
      "ring-number                 0\n",
      "ring-type                   0\n",
      "spore-print-color           0\n",
      "population                  0\n",
      "habitat                     0\n",
      "poisonous                   0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing Value Treatment (Pipeline Function)\n",
    "master(df)\n",
    "print(\" \")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
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       "      <th>gill-color</th>\n",
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       "      <th>...</th>\n",
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       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
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       "      <th>poisonous</th>\n",
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       "  </thead>\n",
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       "      <td>g</td>\n",
       "      <td>e</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing  \\\n",
       "0         x           s         n       t    p               f            c   \n",
       "1         x           s         y       t    a               f            c   \n",
       "2         b           s         w       t    l               f            c   \n",
       "3         x           y         w       t    p               f            c   \n",
       "4         x           s         g       f    n               f            w   \n",
       "\n",
       "  gill-size gill-color stalk-shape  ... stalk-color-above-ring  \\\n",
       "0         n          k           e  ...                      w   \n",
       "1         b          k           e  ...                      w   \n",
       "2         b          n           e  ...                      w   \n",
       "3         n          n           e  ...                      w   \n",
       "4         b          k           t  ...                      w   \n",
       "\n",
       "  stalk-color-below-ring veil-type veil-color ring-number ring-type  \\\n",
       "0                      w         p          w           o         p   \n",
       "1                      w         p          w           o         p   \n",
       "2                      w         p          w           o         p   \n",
       "3                      w         p          w           o         p   \n",
       "4                      w         p          w           o         e   \n",
       "\n",
       "  spore-print-color population habitat poisonous  \n",
       "0                 k          s       u         p  \n",
       "1                 n          n       g         e  \n",
       "2                 n          n       m         e  \n",
       "3                 k          s       u         p  \n",
       "4                 n          a       g         e  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No numerical features present in the data to generate summary and plots!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "e   51.80\n",
       "p   48.20\n",
       "Name: poisonous, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Feature Class Balance (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDA Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 6\n"
     ]
    }
   ],
   "source": [
    "# Categorical Encoding (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 7\n",
      "1- Standard Scaler\n",
      "2- Min-Max Scaler\n",
      "Which type of Scaler? 1\n"
     ]
    }
   ],
   "source": [
    "# Standardization of Numerical Features (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
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       "      <th>bruises</th>\n",
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       "      <th>...</th>\n",
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       "      <td>-0.23</td>\n",
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       "      <td>-1.31</td>\n",
       "      <td>-0.30</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.63</td>\n",
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       "      <td>-0.26</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>0.87</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.03</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.19</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.26</td>\n",
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       "      <td>-0.51</td>\n",
       "      <td>2.03</td>\n",
       "      <td>p</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.28</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.87</td>\n",
       "      <td>...</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0       1.03         0.14      -0.20     1.19  0.88             0.16   \n",
       "1       1.03         0.14       1.77     1.19 -1.97             0.16   \n",
       "2      -2.09         0.14       1.37     1.19 -0.54             0.16   \n",
       "3       1.03         0.95       1.37     1.19  0.88             0.16   \n",
       "4       1.03         0.14      -0.59    -0.84  0.41             0.16   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
       "0         -0.44       1.49       -0.23        -1.14  ...   \n",
       "1         -0.44      -0.67       -0.23        -1.14  ...   \n",
       "2         -0.44      -0.67        0.05        -1.14  ...   \n",
       "3         -0.44       1.49        0.05        -1.14  ...   \n",
       "4          2.28      -0.67       -0.23         0.87  ...   \n",
       "\n",
       "   stalk-color-above-ring  stalk-color-below-ring  veil-type  veil-color  \\\n",
       "0                    0.62                    0.63       0.00        0.14   \n",
       "1                    0.62                    0.63       0.00        0.14   \n",
       "2                    0.62                    0.63       0.00        0.14   \n",
       "3                    0.62                    0.63       0.00        0.14   \n",
       "4                    0.62                    0.63       0.00        0.14   \n",
       "\n",
       "   ring-number  ring-type  spore-print-color  population  habitat  poisonous  \n",
       "0        -0.26       0.95              -0.67       -0.51     2.03          p  \n",
       "1        -0.26       0.95              -0.25       -1.31    -0.30          e  \n",
       "2        -0.26       0.95              -0.25       -1.31     0.87          e  \n",
       "3        -0.26       0.95              -0.67       -0.51     2.03          p  \n",
       "4        -0.26      -1.27              -0.25       -2.91    -0.30          e  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier Detection & Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Outlier detection and treatment functionality under progress!'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection & Dimensionality Reduction not to be done in this submission as per the instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 9\n",
      "Random State? 42\n",
      "What % of data to be left for test? 20\n",
      "How many folds required? 5\n"
     ]
    }
   ],
   "source": [
    "# Train-Test Split and CV Folds (Pipeline Function)\n",
    "X_train,X_test,y_train,y_test,sk_folds=master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 29/29 [00:14<00:00,  2.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 3134, number of negative: 3365\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000373 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 136\n",
      "[LightGBM] [Info] Number of data points in the train set: 6499, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.482228 -> initscore=-0.071118\n",
      "[LightGBM] [Info] Start training from score -0.071118\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "                               Accuracy  Balanced Accuracy ROC AUC  F1 Score  \\\n",
      "Model                                                                          \n",
      "AdaBoostClassifier                 1.00               1.00    None      1.00   \n",
      "KNeighborsClassifier               1.00               1.00    None      1.00   \n",
      "SVC                                1.00               1.00    None      1.00   \n",
      "RandomForestClassifier             1.00               1.00    None      1.00   \n",
      "BaggingClassifier                  1.00               1.00    None      1.00   \n",
      "LabelSpreading                     1.00               1.00    None      1.00   \n",
      "LabelPropagation                   1.00               1.00    None      1.00   \n",
      "LGBMClassifier                     1.00               1.00    None      1.00   \n",
      "ExtraTreesClassifier               1.00               1.00    None      1.00   \n",
      "ExtraTreeClassifier                1.00               1.00    None      1.00   \n",
      "DecisionTreeClassifier             1.00               1.00    None      1.00   \n",
      "SGDClassifier                      0.98               0.98    None      0.98   \n",
      "LogisticRegression                 0.96               0.96    None      0.96   \n",
      "CalibratedClassifierCV             0.96               0.96    None      0.96   \n",
      "LinearSVC                          0.96               0.96    None      0.96   \n",
      "PassiveAggressiveClassifier        0.95               0.95    None      0.95   \n",
      "Perceptron                         0.95               0.95    None      0.95   \n",
      "LinearDiscriminantAnalysis         0.94               0.94    None      0.94   \n",
      "RidgeClassifier                    0.94               0.94    None      0.94   \n",
      "RidgeClassifierCV                  0.94               0.94    None      0.94   \n",
      "NuSVC                              0.92               0.92    None      0.92   \n",
      "GaussianNB                         0.90               0.90    None      0.90   \n",
      "BernoulliNB                        0.90               0.89    None      0.89   \n",
      "NearestCentroid                    0.87               0.87    None      0.87   \n",
      "QuadraticDiscriminantAnalysis      0.87               0.87    None      0.87   \n",
      "DummyClassifier                    0.52               0.50    None      0.35   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "AdaBoostClassifier                   0.57  \n",
      "KNeighborsClassifier                 0.24  \n",
      "SVC                                  0.37  \n",
      "RandomForestClassifier               0.43  \n",
      "BaggingClassifier                    0.12  \n",
      "LabelSpreading                       3.92  \n",
      "LabelPropagation                     2.98  \n",
      "LGBMClassifier                       0.16  \n",
      "ExtraTreesClassifier                 0.37  \n",
      "ExtraTreeClassifier                  0.05  \n",
      "DecisionTreeClassifier               0.06  \n",
      "SGDClassifier                        0.06  \n",
      "LogisticRegression                   0.09  \n",
      "CalibratedClassifierCV               1.00  \n",
      "LinearSVC                            0.27  \n",
      "PassiveAggressiveClassifier          0.06  \n",
      "Perceptron                           0.05  \n",
      "LinearDiscriminantAnalysis           0.07  \n",
      "RidgeClassifier                      0.05  \n",
      "RidgeClassifierCV                    0.07  \n",
      "NuSVC                                3.02  \n",
      "GaussianNB                           0.04  \n",
      "BernoulliNB                          0.05  \n",
      "NearestCentroid                      0.04  \n",
      "QuadraticDiscriminantAnalysis        0.05  \n",
      "DummyClassifier                      0.04  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Selection (Pipeline Function)\n",
    "master(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this submission, our model of choice is KNN:\n",
    "\n",
    "# Model                            Accuracy  Balanced Accuracy  ROC AUC  F1 Score                                                                       \n",
    "# KNeighborsClassifier               1.00               1.00    None      1.00 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 11\n",
      "What is the value of K? 3\n"
     ]
    }
   ],
   "source": [
    "# Model Training (Pipeline Function)\n",
    "knn,scoresCV=master(data=df,X_train=X_train, X_test=X_test, y_train=y_train,sk_folds=sk_folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 12\n",
      "\n",
      "\n",
      "\n",
      "Train-Test Split Evaluation\n",
      "              precision  recall  f1-score  support\n",
      "p                  1.00    1.00      1.00   843.00\n",
      "e                  1.00    1.00      1.00   782.00\n",
      "accuracy           1.00    1.00      1.00     1.00\n",
      "macro avg          1.00    1.00      1.00  1625.00\n",
      "weighted avg       1.00    1.00      1.00  1625.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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FRDdgd+CfklYvVExmZrasQh4RbAdMj4j3IuJbYDTQP6tMAC0lCVgT+AKoKGBMZmaWpZCJYANgRkZ3edov0zXAlsAnwOvAHyJiSfaEJB0raZKkSbNmzSpUvGZmJamQiUA5+kVW9y+AKcD6QHfgGkmtlhkpYlhElEVE2TrrrFPXcZqZlbRCJoJyYMOM7vYke/6ZjgbujcR04H1giwLGZGZmWQqZCF4COknaOD0BfAgwNqvMR8AeAJJ+AnQG3itgTGZmlqVxoSYcERWSTgIeBRoBwyPiDUnHpcOvBy4CRkh6naQp6ayImF2omMzMbFkFSwQAETEOGJfV7/qMz58AexUyBjMzq5n/WWxmVuKcCMzMSpwTgZlZiXMiMDMrcU4EZmYlzonAzKzEORGYmZU4JwIzsxLnRGBmVuKcCMzMSlzeiUBSi0IGYmZm9aPWRCBpR0nTgDfT7m6Sri14ZGZmVhT5HBFcQfIAmTkAEfEqsGshgzIzs+LJq2koImZk9fq+ALGYmVk9yOc21DMk7QhE+oCZU0ibiczMrOHL54jgOOBEkgfPl5M8W/iEAsZkZmZFlM8RQeeI+E1mD0k7Ac8XJiQzMyumfI4I/i/PfmZm1gBVe0QgaQdgR2AdSadlDGpF8gxiMzNbBdTUNLQ6sGZapmVG/7nAgYUMyszMiqfaRBARTwNPSxoRER8WMSYzMyuifE4WL5B0GdAVaFrZMyJ+VrCozMysaPI5WTwSeAvYGLgA+AB4qYAxmZlZEeWTCNpGxE3AdxHxdEQcA2xf4LjMzKxI8mka+i59nympH/AJ0L5wIZmZWTHlkwgGS2oNnE7y/4FWwKmFDMrMzIqn1kQQEQ+mH78GekPVP4vNzGwVUNMfyhoBB5HcY+iRiJgqaR/gz0AzYJvihGhmZoVU0xHBTcCGwETgakkfAjsAZ0fEmCLEZmZmRVBTIigDto6IJZKaArOBzSLi0+KEZmZmxVDT5aPfRsQSgIhYBLyzvElAUh9Jb0uaLunsasrsLmmKpDckPb080zczs5VX0xHBFpJeSz8L2DTtFhARsXVNE07PMQwF9iR5jsFLksZGxLSMMm2Aa4E+EfGRpHVXvCpmZrYiakoEW67ktLcDpkfEewCSRgP9gWkZZQYA90bERwAR8flKztPMzJZTTTedW9kbzW0AZD7ruBzolVVmc6CJpPEkdzi9KiJuyZ6QpGOBYwE6dOiwkmGZmVmmvB5ev4KUo19kdTcGegD9gF8Af5W0+TIjRQyLiLKIKFtnnXXqPlIzsxKWzz+LV1Q5yeWnldqT3J4iu8zsiPgG+EbSM0A34J0CxmVmZhnyOiKQ1ExS5+Wc9ktAJ0kbS1odOAQYm1XmfmAXSY0lNSdpOnpzOedjZmYrodZEIGlfYArwSNrdXVL2Bn0ZEVEBnAQ8SrJxvzMi3pB0nKTj0jJvptN9jeSPazdGxNQVrIuZma2AfJqGBpFcATQeICKmSOqYz8QjYhwwLqvf9VndlwGX5TM9MzOre/k0DVVExNcFj8TMzOpFPkcEUyUNABpJ6gScArxQ2LDMzKxY8jkiOJnkecWLgdtJbkd9agFjMjOzIsrniKBzRJwLnFvoYMzMrPjyOSK4XNJbki6S1LXgEZmZWVHVmggiojewOzALGCbpdUl/KXRgZmZWHHn9oSwiPo2Iq4HjSP5TcF4hgzIzs+LJ5w9lW0oaJGkqcA3JFUPtCx6ZmZkVRT4ni28GRgF7RUT2vYLMzKyBqzURRMT2xQjEzMzqR7WJQNKdEXGQpNdZ+vbReT2hzMzMGoaajgj+kL7vU4xAzMysflR7sjgiZqYfT4iIDzNfwAnFCc/MzAotn8tH98zRr29dB2JmZvWjpnMEx5Ps+W8i6bWMQS2B5wsdmJmZFUdN5whuBx4GLgHOzug/LyK+KGhUZmZWNDUlgoiIDySdmD1A0tpOBmZmq4bajgj2ASaTXD6qjGEBbFLAuMzMrEiqTQQRsU/6vnHxwjEzs2LL515DO0lqkX4+TNLlkjoUPjQzMyuGfC4fvQ5YIKkbcCbwIXBrQaMyM7Oiyffh9QH0B66KiKtILiE1M7NVQD53H50n6RzgcGAXSY2AJoUNy8zMiiWfI4KDSR5cf0xEfApsAFxW0KjMzKxo8nlU5afASKC1pH2ARRFxS8EjMzOzosjnqqGDgInAr4GDgBclHVjowMzMrDjyOUdwLtAzIj4HkLQO8ARwdyEDMzOz4sjnHMFqlUkgNSfP8czMrAHI54jgEUmPkjy3GJKTx+MKF5KZmRVTPs8sPkPSr4CdSe43NCwi7it4ZGZmVhQ1PY+gEzAE2BR4HfhTRHxcrMDMzKw4amrrHw48CBxAcgfS/1veiUvqI+ltSdMlnV1DuZ6SvvfVSGZmxVdT01DLiPhX+vltSS8vz4TTfyAPJXnUZTnwkqSxETEtR7m/A48uz/TNzKxu1JQImkrahh+eQ9AsszsiaksM2wHTI+I9AEmjSe5XNC2r3MnAPUDP5YzdzMzqQE2JYCZweUb3pxndAfyslmlvAMzI6C4HemUWkLQBsH86rWoTgaRjgWMBOnTwHbDNzOpSTQ+m6b2S01aOfpHVfSVwVkR8L+UqXhXLMGAYQFlZWfY0zMxsJeTzP4IVVQ5smNHdHvgkq0wZMDpNAu2AvSVVRMSYAsZlZmYZCpkIXgI6SdoY+Bg4BBiQWSDzMZiSRgAPOgmYmRVXwRJBRFRIOonkaqBGwPCIeEPScenw6ws1bzMzy1+tiUBJu81vgE0i4sL0ecX/LyIm1jZuRIwj63YU1SWAiDgqr4jNzKxO5XPzuGuBHYBD0+55JP8PMDOzVUA+TUO9ImJbSa8ARMSXklYvcFxmZlYk+RwRfJf++zeg6nkESwoalZmZFU0+ieBq4D5gXUl/A54DLi5oVGZmVjT53IZ6pKTJwB4kfxL7ZUS8WfDIzMysKPK5aqgDsAB4ILNfRHxUyMDMzKw48jlZ/BDJ+QEBTYGNgbeBrgWMy8zMiiSfpqGfZnZL2hb4fcEiMjOzolruh9Cnt5/2LaPNzFYR+ZwjOC2jczVgW2BWwSIyM7OiyuccQcuMzxUk5wzuKUw4ZmZWbDUmgvSPZGtGxBlFisfMzIqs2nMEkhpHxPckTUFmZraKqumIYCJJEpgiaSxwF/BN5cCIuLfAsZmZWRHkc45gbWAOyXOFK/9PEIATgZnZKqCmRLBuesXQVH5IAJX83GAzs1VETYmgEbAm+T2E3szMGqiaEsHMiLiwaJGYmVm9qOmfxbmOBMzMbBVTUyLYo2hRmJlZvak2EUTEF8UMxMzM6sdy33TOzMxWLU4EZmYlzonAzKzEORGYmZU4JwIzsxLnRGBmVuKcCMzMSpwTgZlZiXMiMDMrcQVNBJL6SHpb0nRJZ+cY/htJr6WvFyR1K2Q8Zma2rIIlgvR5x0OBvkAX4FBJXbKKvQ/sFhFbAxcBwwoVj5mZ5VbII4LtgOkR8V5EfAuMBvpnFoiIFyLiy7RzAtC+gPGYmVkOhUwEGwAzMrrL037V+S3wcK4Bko6VNEnSpFmzZtVhiGZmVshEkPeTzST1JkkEZ+UaHhHDIqIsIsrWWWedOgzRzMzyeXj9iioHNszobg98kl1I0tbAjUDfiJhTwHjMzCyHQh4RvAR0krSxpNWBQ4CxmQUkdQDuBQ6PiHcKGIuZmVWjYEcEEVEh6STgUaARMDwi3pB0XDr8euA8oC1wrSSAiogoK1RMZma2rEI2DRER44BxWf2uz/g8EBhYyBjMzKxm/mexmVmJcyIwMytxTgRmZiXOicDMrMQ5EZiZlTgnAjOzEudEYGZW4pwIzMxKnBOBmVmJcyIwMytxTgRmZiXOicDMrMQ5EZiZlTgnAjOzEudEYGZW4pwIzMxKnBOBmVmJcyIwMytxTgRmZiXOicDMrMQ5EZiZlbjG9R2AWS7fffcd5eXlLFq0qL5DMWtQmjZtSvv27WnSpEne4zgR2I9SeXk5LVu2pGPHjkiq73DMGoSIYM6cOZSXl7PxxhvnPZ6bhuxHadGiRbRt29ZJwGw5SKJt27bLfSTtRGA/Wk4CZstvRX43TgRmZiXOicCsGmuuuWbV53HjxtGpUyc++ugjBg0aRPPmzfn8889zlq3O3nvvzVdffVVjmd13351JkyYt03/EiBGcdNJJ+Qe/HIYMGcIWW2zBVlttRbdu3bjllltqjGVFTJo0iVNOOQWAxYsX8/Of/5zu3btzxx13MHDgQKZNm7ZS07/yyiur4gaoqKigXbt2nHPOOUuV69ixI7Nnz67qHj9+PPvss09V98MPP0xZWRlbbrklW2yxBX/6059WKi6Ac889lw033LDW78gll1zCZpttRufOnXn00Uer+k+ePJmf/vSnbLbZZpxyyilEBADXXHMNN99880rHB04EZrV68sknOfnkk3nkkUfo0KEDAO3ateOf//znck1n3LhxtGnTpgAR1iwiWLJkSc5h119/PY8//jgTJ05k6tSpPPPMM1UbmrpUVlbG1VdfDcArr7zCd999x5QpUzj44IO58cYb6dKlS97T+v7775fqrqioYPjw4QwYMKCq32OPPUbnzp258847867P1KlTOemkk7jtttt48803mTp1KptssknecVVn3333ZeLEiTWWmTZtGqNHj+aNN97gkUce4YQTTqiq5/HHH8+wYcN49913effdd3nkkUcAOOaYY6qW6cryVUP2o3fBA28w7ZO5dTrNLuu34vx9u9Za7tlnn+V3v/sd48aNY9NNN63qf8wxxzBixAjOOuss1l577aXGue2227j66qv59ttv6dWrF9deey2NGjWiY8eOTJo0iXbt2nHRRRcxcuRINtxwQ9q1a0ePHj2q9j7vuusuTjjhBL766ituuukmdtllFwBmzJhBnz59eP/99xkwYADnn38+AJdffjnDhw8HYODAgZx66ql88MEH9O3bl969e/Pf//6XMWPGcP755zNp0iQkccwxx/DHP/6Riy++mKeeeopWrVoB0Lp1a4488shllsPxxx/PSy+9xMKFCznwwAO54IILADj77LMZO3YsjRs3Zq+99mLIkCHcddddXHDBBTRq1IjWrVvzzDPPMH78eIYMGcLw4cM57LDDmDVrFt27d+eee+7ht7/9LUOGDKGsrIzHHnuM888/n8WLF7Ppppty8803s+aaa9KxY0eOOeYYHnvsMU466SQOOeSQqtj+85//sO2229K48Q+bs1GjRvGHP/yB6667jgkTJrDDDjvUuq7/8Y9/cO6557LFFlsA0LhxY0444YRax6vN9ttvX2uZ+++/n0MOOYQ11liDjTfemM0224yJEyfSsWNH5s6dWxX/EUccwZgxY+jbty/NmzenY8eOTJw4ke22226lYnQiMKvG4sWL6d+/P+PHj6/aOFRac801OeaYY7jqqquqNooAb775JnfccQfPP/88TZo04YQTTmDkyJEcccQRVWUmTZrEPffcwyuvvEJFRQXbbrstPXr0qBpeUVHBxIkTGTduHBdccAFPPPEEQNVee/PmzenZsyf9+vVDEjfffDMvvvgiEUGvXr3YbbfdWGuttXj77be5+eabufbaa5k8eTIff/wxU6dOBeCrr75i3rx5zJs3b6kEV52//e1vrL322nz//ffssccevPbaa7Rv35777ruPt956C0lVzV4XXnghjz76KBtssMEyTWHrrrsuN954I0OGDOHBBx9catjs2bMZPHgwTzzxBC1atODvf/87l19+Oeeddx6QXB//3HPPLRPb888/v9TyW7hwIU8++SQ33HADX331FaNGjcorEUydOpXTTz+91nJPPfUUf/zjH5fp37x5c1544YVax8/l448/XiphtG/fno8//pgmTZrQvn37ZfpXKisr49lnn3UisFVfPnvuhdCkSRN23HFHbrrpJq666qplhp9yyil07959qY3Hk08+yeTJk+nZsyeQbJTWXXfdpcZ77rnn6N+/P82aNQOSpoNMv/rVrwDo0aMHH3zwQVX/Pffck7Zt21aVee6555DE/vvvT4sWLar6P/vss+y3335stNFGVRuXTTbZhPfee4+TTz6Zfv36sddeezF//vy8rzC58847GTZsGBUVFcycOZNp06bRpUsXmjZtysCBA+nXr19VW/tOO+3EUUcdxUEHHVRVl3xMmDCBadOmsdNOOwHw7bffLrUBP/jgg3OON3PmTLbccsuq7gcffJDevXvTvHlzDjjgAC666CKuuOIKGjVqlLO+y3uVTe/evZkyZcpyjVObXM1XkqrtX2ndddflrbfeWun5F/QcgaQ+kt6WNF3S2TmGS9LV6fDXJG1byHjMlsdqq63GnXfeyUsvvcTFF1+8zPA2bdowYMAArr322qp+EcGRRx7JlClTmDJlCm+//TaDBg1aarza2qzXWGMNABo1akRFRUVV/+wNVnUbikqVyQFgrbXW4tVXX2X33Xdn6NChDBw4kFatWtGiRQvee++9GuN5//33GTJkCE8++SSvvfYa/fr1Y9GiRTRu3JiJEydywAEHMGbMGPr06QMk5x0GDx7MjBkz6N69O3PmzKlx+pUigj333LNq2U2bNo2bbropZ30yNWvWbKnr5keNGsUTTzxBx44d6dGjB3PmzOGpp54CoG3btnz55ZdVZb/44gvatWsHQNeuXZk8eXKtcT711FN07959mdeOO+6YVz1zad++PTNmzKjqLi8vZ/3116d9+/aUl5cv07/SokWLqnYoVkbBEoGkRsBQoC/QBThUUvYZob5Ap/R1LHBdoeIxWxHNmzfnwQcfZOTIkUttlCqddtpp3HDDDVUb7D322IO777676oqiL774gg8//HCpcXbeeWceeOABFi1axPz583nooYfyiuXxxx/niy++YOHChYwZM4addtqJXXfdlTFjxrBgwQK++eYb7rvvvqpzCplmz57NkiVLqvaQX375ZQDOOeccTjzxRObOTc7BzJ07l2HDhi017ty5c2nRogWtW7fms88+4+GHHwZg/vz5fP311+y9995ceeWVVXvJ//vf/+jVqxcXXngh7dq1W2oDV5Ptt9+e559/nunTpwOwYMEC3nnnnVrH23LLLavGmTt3Ls899xwfffQRH3zwAR988AFDhw5l1KhRQHIl1K233gokJ51vu+02evfuDcAZZ5zBxRdfXDXPJUuWcPnlly8zv8ojguzXijYLAey3336MHj2axYsX8/777/Puu++y3Xbbsd5669GyZUsmTJhARHDLLbfQv3//qvHeeecdttpqqxWeb6VCNg1tB0yPiPcAJI0G+gOZ14n1B26JZLdmgqQ2ktaLiJkFjMtsuay99to88sgj7LrrrlV7j5XatWvH/vvvzxVXXAFAly5dGDx4MHvttRdLliyhSZMmDB06lI022qhqnJ49e7LffvvRrVs3NtpoI8rKymjdunWtcey8884cfvjhTJ8+nQEDBlBWVgbAUUcdVdVGPHDgQLbZZpulmpQgaYM++uijq64euuSSS4DkJPD8+fPp2bMnTZo0oUmTJsu0k3fr1o1tttmGrl27sskmm1Q13cybN4/+/fuzaNEiIqJqGZxxxhm8++67RAR77LEH3bp14+mnn661fuussw4jRozg0EMPZfHixQAMHjyYzTffvMbx+vbty+GHHw7Avffey89+9rOqoyqA/v37c+aZZ7J48WL++te/cvzxx9OtWzcigj59+nDYYYcBsPXWW3PllVdy6KGHsmDBAiTRr1+/WuOuzZlnnsntt9/OggULaN++PQMHDmTQoEGMHTuWSZMmceGFF9K1a1cOOuggunTpQuPGjRk6dCiNGjUC4LrrruOoo45i4cKF9O3bl759+1ZN+/nnn6+6aGClRERBXsCBwI0Z3YcD12SVeRDYOaP7SaAsx7SOBSYBkzp06BArYtDYqTFo7NQVGteKb9q0afUdQkHNmzcvIiK++eab6NGjR0yePLmeI2rYfvnLX8Y777xT32EU1csvvxyHHXZYzmG5fj/ApKhme13II4JcZ2CyGzTzKUNEDAOGAZSVla3QRc71dcLRLJdjjz2WadOmsWjRIo488ki23danx1bGpZdeysyZM+nUqVN9h1I0s2fP5qKLLqqTaRUyEZQDG2Z0twc+WYEyZquc22+/vb5DWKV07tyZzp0713cYRbXnnnvW2bQKedXQS0AnSRtLWh04BBibVWYscER69dD2wNfh8wOWigL8w9VsVbciv5uCHRFERIWkk4BHgUbA8Ih4Q9Jx6fDrgXHA3sB0YAFwdKHisYaladOmzJkzx7eiNlsOkT6PoGnTpss1nhraXldZWVnU1Y2w7MfLTygzWzHVPaFM0uSIKMs1jv9ZbD9KTZo0Wa4nLJnZivPdR83MSpwTgZlZiXMiMDMrcQ3uZLGkWcCHtRbMrR0wu9ZSqxbXuTS4zqVhZeq8UUSsk2tAg0sEK0PSpOrOmq+qXOfS4DqXhkLV2U1DZmYlzonAzKzElVoiGFZ7kVWO61waXOfSUJA6l9Q5AjMzW1apHRGYmVkWJwIzsxK3SiYCSX0kvS1puqSzcwyXpKvT4a9JavBPBcmjzr9J6/qapBckdauPOOtSbXXOKNdT0veSDixmfIWQT50l7S5piqQ3JNX+jMgfuTy+260lPSDp1bTODfouxpKGS/pc0tRqhtf99qu6R5c11BfJLa//B2wCrA68CnTJKrM38DDJE9K2B16s77iLUOcdgbXSz31Loc4Z5f5DcsvzA+s77iKs5zYkzwXvkHavW99xF6HOfwb+nn5eB/gCWL2+Y1+JOu8KbAtMrWZ4nW+/VsUjgu2A6RHxXkR8C4wG+meV6Q/cEokJQBtJ6xU70DpUa50j4oWI+DLtnEDyNLiGLJ/1DHAycA/weTGDK5B86jwAuDciPgKIiIZe73zqHEBLJQ+uWJMkEVQUN8y6ExHPkNShOnW+/VoVE8EGwIyM7vK03/KWaUiWtz6/JdmjaMhqrbOkDYD9geuLGFch5bOeNwfWkjRe0mRJRxQtusLIp87XAFuSPOb2deAPEbGkOOHVizrffq2KzyPI9Tir7Gtk8ynTkORdH0m9SRLBzgWNqPDyqfOVwFkR8f0q8pSzfOrcGOgB7AE0A/4raUJEvFPo4Aoknzr/ApgC/AzYFHhc0rMRMbfAsdWXOt9+rYqJoBzYMKO7PcmewvKWaUjyqo+krYEbgb4RMadIsRVKPnUuA0anSaAdsLekiogYU5QI616+3+3ZEfEN8I2kZ4BuQENNBPnU+Wjg0kga0KdLeh/YAphYnBCLrs63X6ti09BLQCdJG0taHTgEGJtVZixwRHr2fXvg64iYWexA61CtdZbUAbgXOLwB7x1mqrXOEbFxRHSMiI7A3cAJDTgJQH7f7fuBXSQ1ltQc6AW8WeQ461I+df6I5AgIST8BOgPvFTXK4qrz7dcqd0QQERWSTgIeJbniYHhEvCHpuHT49SRXkOwNTAcWkOxRNFh51vk8oC1wbbqHXBEN+M6NedZ5lZJPnSPiTUmPAK8BS4AbIyLnZYgNQZ7r+SJghKTXSZpNzoqIBnt7akmjgN2BdpLKgfOBJlC47ZdvMWFmVuJWxaYhMzNbDk4EZmYlzonAzKzEORGYmZU4JwIzsxLnRGA/SundQqdkvDrWUHZ+HcxvhKT303m9LGmHFZjGjZK6pJ//nDXshZWNMZ1O5XKZmt5xs00t5btL2rsu5m2rLl8+aj9KkuZHxJp1XbaGaYwAHoyIuyXtBQyJiK1XYnorHVNt05X0b+CdiPhbDeWPAsoi4qS6jsVWHT4isAZB0pqSnkz31l+XtMydRiWtJ+mZjD3mXdL+e0n6bzruXZJq20A/A2yWjntaOq2pkk5N+7WQ9FB6//upkg5O+4+XVCbpUqBZGsfIdNj89P2OzD309EjkAEmNJF0m6SUl95j/fR6L5b+kNxuTtJ2S50y8kr53Tv+JeyFwcBrLwWnsw9P5vJJrOVoJqu97b/vlV64X8D3JjcSmAPeR/Au+VTqsHcm/KiuPaOen76cD56afGwEt07LPAC3S/mcB5+WY3wjS5xUAvwZeJLl52+tAC5LbG78BbAMcAPwrY9zW6ft4kr3vqpgyylTGuD/w7/Tz6iR3kWwGHAv8Je2/BjAJ2DhHnPMz6ncX0CftbgU0Tj//HLgn/XwUcE3G+BcDh6Wf25Dcg6hFfa9vv+r3tcrdYsJWGQsjontlh6QmwMWSdiW5dcIGwE+ATzPGeQkYnpYdExFTJO0GdAGeT2+tsTrJnnQul0n6CzCL5A6tewD3RXIDNyTdC+wCPAIMkfR3kuakZ5ejXg8DV0taA+gDPBMRC9PmqK31w1PUWgOdgPezxm8maQrQEZgMPJ5R/t+SOpHcibJJNfPfC9hP0p/S7qZABxr2/YhsJTkRWEPxG5KnT/WIiO8kfUCyEasSEc+kiaIfcKuky4Avgccj4tA85nFGRNxd2SHp57kKRcQ7knqQ3O/lEkmPRcSF+VQiIhZJGk9y6+SDgVGVswNOjohHa5nEwojoLqk18CBwInA1yf12noqI/dMT6+OrGV/AARHxdj7xWmnwOQJrKFoDn6dJoDewUXYBSRulZf4F3ETyuL8JwE6SKtv8m0vaPM95PgP8Mh2nBUmzzrOS1gcWRMRtwJB0Ptm+S49MchlNcqOwXUhupkb6fnzlOJI2T+eZU0R8DZwC/CkdpzXwcTr4qIyi80iayCo9Cpys9PBI0jbVzcNKhxOBNRQjgTJJk0iODt7KUWZ3YIqkV0ja8a+KiFkkG8ZRkl4jSQxb5DPDiHiZ5NzBRJJzBjdGxCvAT4GJaRPNucDgHKMPA16rPFmc5TGS59I+EcnjFyF5TsQ04GUlDy2/gVqO2NNYXiW5NfM/SI5Onic5f1DpKaBL5clikiOHJmlsU9NuK3G+fNTMrMT5iMDMrMQ5EZiZlTgnAjOzEudEYGZW4pwIzMxKnBOBmVmJcyIwMytx/x9M6zTruM8jMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Cross-Validation Evaluation\n",
      "Cross Validation Scores:  [1.         1.         1.         0.99938462 1.        ]\n",
      "Average CV Score:  0.9998769230769231\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation (Pipeline Function)\n",
    "master(knn=knn,X_test=X_test,y_test=y_test,scoresCV=scoresCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Result Interpretation\n",
    "### Classes: p , e\n",
    "## Train-Test Split Evaluation:\n",
    "\n",
    "#### For class 'p', the precision is 1.00, indicating that 100% of the instances classified as 'p' are actually 'p'.\n",
    "#### For class 'e', the precision is 1.00, indicating that 100% of the instances classified as 'e' are actually 'e'.\n",
    "#### For class 'p', the recall is 1.00, indicating that 100% of the actual 'p' instances were correctly identified by the classifier.\n",
    "#### For class 'e', the recall is 1.00, indicating that 100% of the actual 'e' instances were correctly identified by the classifier.\n",
    "#### For class 'p', the F1-score is 1.00, representing the overall performance in terms of both precision and recall.\n",
    "#### For class 'e', the F1-score is 1.00, also representing the overall performance in terms of both precision and recall.\n",
    "#### Accuracy measures the overall correctness of the classifier across all classes. The overall accuracy of the classifier is 1.00, or 100%.\n",
    "#### AUC (Area Under the ROC Curve): 1.00 - The AUC score of 1.00 represents the model's ability to discriminate between positive and negative instances, with a higher value indicating better discrimination capability.\n",
    "\n",
    "\n",
    "## Cross-Validation Evaluation:\n",
    "\n",
    "#### Cross-Validation Scores: [1.0, 1.0, 1.0, 0.9994, 1.0]\n",
    "#### Average CV Score: 0.9999\n",
    "#### Number of CV Scores used in Average: 5\n",
    "#### These metrics demonstrate exceptional performance across both evaluation methods. Specifically:\n",
    "\n",
    "#### Perfect Scores in Train-Test Split Evaluation:\n",
    "\n",
    "#### The model achieved perfect scores across all evaluation metrics (precision, recall, F1 score, accuracy, and AUC) in the train-test split evaluation. This indicates that the model correctly classified all instances in the test set with no errors.\n",
    "#### High Scores in Cross-Validation Evaluation:\n",
    "\n",
    "#### The cross-validation evaluation also yielded exceptionally high scores, with an average cross-validation score of 0.9999. This suggests that the model's performance is consistent across different folds of the dataset, further confirming its robustness and reliability.\n",
    "\n",
    "#### In summary, both evaluation methods demonstrate that the model performs exceptionally well, with perfect or near-perfect scores across all metrics. However we see that one instance on CV deviates from the perfect score which helps is understanding the 100% in test dataset. It seems that there's data leakage when we're splitting the dataset and there are identical and near identifcal data samples present in the test set which means that data samples in training with particular feature values are present as it is in the test set thereby rendering 100% accuracy in the test data set. So even though by definition this doesn't seem overfitting (as test accuracy is 100% and not train) but due to limited data we are indeed overfitting as there's not enough variety of new data to test the model. We can increase the test size but that will reduce our model's learning so a bigger dataset with varying data samples is required. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset - 4\n",
    "### Title: Bank Marketing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 1\n",
      "Please enter the UCI ID 222\n",
      "Successfuly Connected to the Data Source\n"
     ]
    }
   ],
   "source": [
    "# Dataset Fetching (Pipeline Function)\n",
    "df,label_column=master()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>duration</th>\n",
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       "      <th>pdays</th>\n",
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       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
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       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>blue-collar</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
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       "      <td>no</td>\n",
       "      <td>231</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>447</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>entrepreneur</td>\n",
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       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>121</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>may</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>593</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>admin.</td>\n",
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       "      <td>yes</td>\n",
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       "      <th>12</th>\n",
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       "      <td>technician</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>may</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>71</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>162</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>229</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>353</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>13</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>52</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>60</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>219</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age           job   marital  education default  balance housing loan  \\\n",
       "0    58    management   married   tertiary      no     2143     yes   no   \n",
       "1    44    technician    single  secondary      no       29     yes   no   \n",
       "2    33  entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3    47   blue-collar   married        NaN      no     1506     yes   no   \n",
       "4    33           NaN    single        NaN      no        1      no   no   \n",
       "5    35    management   married   tertiary      no      231     yes   no   \n",
       "6    28    management    single   tertiary      no      447     yes  yes   \n",
       "7    42  entrepreneur  divorced   tertiary     yes        2     yes   no   \n",
       "8    58       retired   married    primary      no      121     yes   no   \n",
       "9    43    technician    single  secondary      no      593     yes   no   \n",
       "10   41        admin.  divorced  secondary      no      270     yes   no   \n",
       "11   29        admin.    single  secondary      no      390     yes   no   \n",
       "12   53    technician   married  secondary      no        6     yes   no   \n",
       "13   58    technician   married        NaN      no       71     yes   no   \n",
       "14   57      services   married  secondary      no      162     yes   no   \n",
       "15   51       retired   married    primary      no      229     yes   no   \n",
       "16   45        admin.    single        NaN      no       13     yes   no   \n",
       "17   57   blue-collar   married    primary      no       52     yes   no   \n",
       "18   60       retired   married    primary      no       60     yes   no   \n",
       "19   33      services   married  secondary      no        0     yes   no   \n",
       "\n",
       "   contact  day_of_week month  duration  campaign  pdays  previous poutcome  \\\n",
       "0      NaN            5   may       261         1     -1         0      NaN   \n",
       "1      NaN            5   may       151         1     -1         0      NaN   \n",
       "2      NaN            5   may        76         1     -1         0      NaN   \n",
       "3      NaN            5   may        92         1     -1         0      NaN   \n",
       "4      NaN            5   may       198         1     -1         0      NaN   \n",
       "5      NaN            5   may       139         1     -1         0      NaN   \n",
       "6      NaN            5   may       217         1     -1         0      NaN   \n",
       "7      NaN            5   may       380         1     -1         0      NaN   \n",
       "8      NaN            5   may        50         1     -1         0      NaN   \n",
       "9      NaN            5   may        55         1     -1         0      NaN   \n",
       "10     NaN            5   may       222         1     -1         0      NaN   \n",
       "11     NaN            5   may       137         1     -1         0      NaN   \n",
       "12     NaN            5   may       517         1     -1         0      NaN   \n",
       "13     NaN            5   may        71         1     -1         0      NaN   \n",
       "14     NaN            5   may       174         1     -1         0      NaN   \n",
       "15     NaN            5   may       353         1     -1         0      NaN   \n",
       "16     NaN            5   may        98         1     -1         0      NaN   \n",
       "17     NaN            5   may        38         1     -1         0      NaN   \n",
       "18     NaN            5   may       219         1     -1         0      NaN   \n",
       "19     NaN            5   may        54         1     -1         0      NaN   \n",
       "\n",
       "     y  \n",
       "0   no  \n",
       "1   no  \n",
       "2   no  \n",
       "3   no  \n",
       "4   no  \n",
       "5   no  \n",
       "6   no  \n",
       "7   no  \n",
       "8   no  \n",
       "9   no  \n",
       "10  no  \n",
       "11  no  \n",
       "12  no  \n",
       "13  no  \n",
       "14  no  \n",
       "15  no  \n",
       "16  no  \n",
       "17  no  \n",
       "18  no  \n",
       "19  no  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values are Present\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   age          45211 non-null  int64 \n",
      " 1   job          44923 non-null  object\n",
      " 2   marital      45211 non-null  object\n",
      " 3   education    43354 non-null  object\n",
      " 4   default      45211 non-null  object\n",
      " 5   balance      45211 non-null  int64 \n",
      " 6   housing      45211 non-null  object\n",
      " 7   loan         45211 non-null  object\n",
      " 8   contact      32191 non-null  object\n",
      " 9   day_of_week  45211 non-null  int64 \n",
      " 10  month        45211 non-null  object\n",
      " 11  duration     45211 non-null  int64 \n",
      " 12  campaign     45211 non-null  int64 \n",
      " 13  pdays        45211 non-null  int64 \n",
      " 14  previous     45211 non-null  int64 \n",
      " 15  poutcome     8252 non-null   object\n",
      " 16  y            45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n",
      "None\n",
      "\n",
      "['no' 'yes']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job   marital  education default  balance housing loan  \\\n",
       "0       58    management   married   tertiary      no     2143     yes   no   \n",
       "1       44    technician    single  secondary      no       29     yes   no   \n",
       "2       33  entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3       47   blue-collar   married        NaN      no     1506     yes   no   \n",
       "4       33           NaN    single        NaN      no        1      no   no   \n",
       "...    ...           ...       ...        ...     ...      ...     ...  ...   \n",
       "45206   51    technician   married   tertiary      no      825      no   no   \n",
       "45207   71       retired  divorced    primary      no     1729      no   no   \n",
       "45208   72       retired   married  secondary      no     5715      no   no   \n",
       "45209   57   blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37  entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
       "         contact  day_of_week month  duration  campaign  pdays  previous    y  \n",
       "0            NaN            5   may       261         1     -1         0   no  \n",
       "1            NaN            5   may       151         1     -1         0   no  \n",
       "2            NaN            5   may        76         1     -1         0   no  \n",
       "3            NaN            5   may        92         1     -1         0   no  \n",
       "4            NaN            5   may       198         1     -1         0   no  \n",
       "...          ...          ...   ...       ...       ...    ...       ...  ...  \n",
       "45206   cellular           17   nov       977         3     -1         0  yes  \n",
       "45207   cellular           17   nov       456         2     -1         0  yes  \n",
       "45208   cellular           17   nov      1127         5    184         3  yes  \n",
       "45209  telephone           17   nov       508         4     -1         0   no  \n",
       "45210   cellular           17   nov       361         2    188        11   no  \n",
       "\n",
       "[45211 rows x 16 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Checking\n",
    "# No Manual Data Type Correction and Removal of Redundant Column Required\n",
    "print(df.info())\n",
    "\n",
    "# No Manual label Correction Required\n",
    "print(\"\")\n",
    "print(df[label_column].unique())\n",
    "\n",
    "# Manually drop column with more than 80% missing values\n",
    "df.drop(['poutcome'],axis=1,inplace=True)\n",
    "\n",
    "# No Manual Data Entry Error Correction Required\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 2\n",
      "Dataset is treated for missing values successfully\n",
      " \n",
      "age            0\n",
      "job            0\n",
      "marital        0\n",
      "education      0\n",
      "default        0\n",
      "balance        0\n",
      "housing        0\n",
      "loan           0\n",
      "contact        0\n",
      "day_of_week    0\n",
      "month          0\n",
      "duration       0\n",
      "campaign       0\n",
      "pdays          0\n",
      "previous       0\n",
      "y              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing Value Treatment (Pipeline Function)\n",
    "master(df)\n",
    "print(\" \")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>no</td>\n",
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       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
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       "      <td>1506</td>\n",
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       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married  secondary      no     1506     yes   no   \n",
       "4   33   blue-collar   single  secondary      no        1      no   no   \n",
       "\n",
       "    contact  day_of_week month  duration  campaign  pdays  previous   y  \n",
       "0  cellular            5   may       261         1     -1         0  no  \n",
       "1  cellular            5   may       151         1     -1         0  no  \n",
       "2  cellular            5   may        76         1     -1         0  no  \n",
       "3  cellular            5   may        92         1     -1         0  no  \n",
       "4  cellular            5   may       198         1     -1         0  no  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "      <td>45211.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.94</td>\n",
       "      <td>1362.27</td>\n",
       "      <td>15.81</td>\n",
       "      <td>258.16</td>\n",
       "      <td>2.76</td>\n",
       "      <td>40.20</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.62</td>\n",
       "      <td>3044.77</td>\n",
       "      <td>8.32</td>\n",
       "      <td>257.53</td>\n",
       "      <td>3.10</td>\n",
       "      <td>100.13</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.00</td>\n",
       "      <td>-8019.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.00</td>\n",
       "      <td>448.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.00</td>\n",
       "      <td>1428.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>319.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.00</td>\n",
       "      <td>102127.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>4918.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>871.00</td>\n",
       "      <td>275.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age   balance  day_of_week  duration  campaign    pdays  previous\n",
       "count 45211.00  45211.00     45211.00  45211.00  45211.00 45211.00  45211.00\n",
       "mean     40.94   1362.27        15.81    258.16      2.76    40.20      0.58\n",
       "std      10.62   3044.77         8.32    257.53      3.10   100.13      2.30\n",
       "min      18.00  -8019.00         1.00      0.00      1.00    -1.00      0.00\n",
       "25%      33.00     72.00         8.00    103.00      1.00    -1.00      0.00\n",
       "50%      39.00    448.00        16.00    180.00      2.00    -1.00      0.00\n",
       "75%      48.00   1428.00        21.00    319.00      3.00    -1.00      0.00\n",
       "max      95.00 102127.00        31.00   4918.00     63.00   871.00    275.00"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical Features Summary (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "no    88.30\n",
       "yes   11.70\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Feature Class Balance (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 5\n",
      "\n",
      "Box and Distribution Plot - duration\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 3.1442137770103913\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 18.151774935186822\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "695178.0017779735 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - balance\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 8.360030947252687\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 140.73584832577242\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "37838102.15611868 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - age\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.6847952047866451\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 0.31940232676995794\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "3725.755126488409 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - day_of_week\n"
     ]
    },
    {
     "data": {
      "image/png": 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cA4YpNvna0P9M8urMf3zsl5Kc393/PnQwwBEwTDlfGwpHKQGG6eZrQ+EoJcAw3Zb0taHAkSfAMN2W9LWhwJEnwDDdfG0oHKWOHT0AsHa6+9GqujDJP+X7Xxt68+CxgPgzJAAYwkvQADCAAAPAAAIMAAMIMAAMIMAAMIAAA8AAAgwAA/w/+pBA9mjP/uYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sXlV/leRo4GA6k9NcCXxmDuqTJEkP0Exd8R8B7gKoqguq6o1V9Zd0zuo/0t/SJEnSbJipG3+sqr43sbGqViQZ609Jw62Xq4ftuWgxq25ZOUcVSZJG3Uxhv/00y3aYzULaoperh13w2kPnqBpJkmbuxv9ukldObEzycuDq/pQkSZJm00xn9m8ALkzyYu4L92XAtnTmo5ckSfPctGFfVXcCT0tyKPdNM/vPVfW1vlcmSZJmRa/z2V8GzDyNlSRJmne8Cp4kSS1n2EuS1HKGvSRJLWfYS5LUcoa9JEktZ9hLktRyhr0kSS3X63z2kiTNiV4mFNuwccMcVdMOhr0kaV7pZUKxc48/eI6qaQe78SVJajnP7Adg46Z72Xa76WYP7nDee0nSbDDsB6Du3cTRp1wx43rOey9Jmg1968ZPcnqSdUlu6GrbNcklSX7U3O/SteztSVYmuTnJ4V3tByS5vln20STpV82SJLVRP7+zPwM4YkLb24BLq2of4NLmOUn2BY4BntBsc0qSBc02pwLHAfs0t4n7lCQNgbElS9l2u+1nvDnSfvb1rRu/qr6eZGxC85HAIc3jM4HLgbc27edU1T3ArUlWAgcmWQXsXFVXAiQ5CzgK+GK/6pYk9Ucvo+zBkfb9MNej8XevqrUAzf0jmvZFwO1d661u2hY1jye2S5KkHs2Xn95N9j18TdM++U6S45KsSLJi/fr1s1acJEnDbK7D/s4kewA09+ua9tXAXl3rLQbWNO2LJ2mfVFWdVlXLqmrZwoULZ7VwSZKG1VyH/cXA8ubxcuCirvZjkmyXZG86A/Guarr670rylGYU/ku7tpEkST3o2wC9JGfTGYy3W5LVwHuA9wPnJXk5cBvwAoCqujHJecD3gY3ACVW1qdnVq+mM7N+BzsA8B+dJkrQF+jka/9gpFh02xfonAidO0r4C2G8WS5OkodTLBDHg1Td1f15BT5KGRK8/XfPqm5povozGlyRJfeKZvSQNWK+TY3llOW0tw16SBqzXybG8spy2lt34kiS1nGEvSVLLGfaSJLWc39lLkqbkb/vbwbCXJE3J3/a3g934kiS1nGEvSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nGEvSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nLPeSZI0izZuupdtt9t+xvXmclpgw16SpFlU927i6FOumHG9uZwW2G58SZJazrCXJKnlDHtJklrOsJckqeUMe0mSWm4gYZ9kVZLrk1ybZEXTtmuSS5L8qLnfpWv9tydZmeTmJIcPomZJkobVIM/sD62q/atqWfP8bcClVbUPcGnznCT7AscATwCOAE5JsmAQBas9xn8HO9NtbMnSQZcqSQ/YfPqd/ZHAIc3jM4HLgbc27edU1T3ArUlWAgcCVw6gRrXEfPwdrCT1y6DO7Av4SpKrkxzXtO1eVWsBmvtHNO2LgNu7tl3dtN1PkuOSrEiyYv369X0qXZKk4TKoM/uDqmpNkkcAlyT5wTTrZpK2mmzFqjoNOA1g2bJlk64jSdKoGciZfVWtae7XARfS6Za/M8keAM39umb11cBeXZsvBtbMXbWSJA23OQ/7JA9NstP4Y+CZwA3AxcDyZrXlwEXN44uBY5Jsl2RvYB/gqrmtWpKk4TWIbvzdgQuTjL/+Z6vqS0m+C5yX5OXAbcALAKrqxiTnAd8HNgInVNWmAdQtSa0xtmQpa+5YPeN6GzZumINq1G9zHvZVdQvwxEnafwocNsU2JwIn9rk0SRoZa+5YzdEnXzbjeucef/AcVKN+8wp6kiS1nGEvSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nGEvSVLLzacpbuc1rzYlSRpWhn2PvNqUJGlY2Y0vSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nGEvSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nGEvSVLLGfaSJLWcYS9JUssZ9pIktZxhL0lSyxn2kiS1nGEvSVLLDU3YJzkiyc1JViZ526DrkSRpWAxF2CdZAPwD8CxgX+DYJPsOtipJkobDUIQ9cCCwsqpuqarfAecARw64JkmShkKqatA1zCjJ84EjquoVzfOXAP+pql4zYb3jgOOap48Fbp5kd7sBP+ljucPG47E5j8d9PBab83hszuNxn/lyLB5VVQsnW/Dgua5kK2WStvt9Sqmq04DTpt1RsqKqls1WYcPO47E5j8d9PBab83hszuNxn2E4FsPSjb8a2Kvr+WJgzYBqkSRpqAxL2H8X2CfJ3km2BY4BLh5wTZIkDYWh6Mavqo1JXgN8GVgAnF5VN27l7qbt5h9BHo/NeTzu47HYnMdjcx6P+8z7YzEUA/QkSdLWG5ZufEmStJUMe0mSWm6kwt5L7m4uyaok1ye5NsmKQdczl5KcnmRdkhu62nZNckmSHzX3uwyyxrk0xfF4b5I7mvfHtUmePcga50qSvZJcluSmJDcmeX3TPpLvj2mOx6i+P7ZPclWS65rj8ddN+7x+f4zMd/bNJXd/CPwXOj/l+y5wbFV9f6CFDVCSVcCyqpoPF4OYU0n+M3A3cFZV7de0fQD4WVW9v/kwuEtVvXWQdc6VKY7He4G7q+qDg6xtriXZA9ijqq5JshNwNXAU8DJG8P0xzfF4IaP5/gjw0Kq6O8k2wDeB1wNHM4/fH6N0Zu8ld/V7VfV14GcTmo8Ezmwen0nnD9pImOJ4jKSqWltV1zSP7wJuAhYxou+PaY7HSKqOu5un2zS3Yp6/P0Yp7BcBt3c9X80Iv2EbBXwlydXNpYZH3e5VtRY6f+CARwy4nvngNUm+13Tzz6tuybmQZAx4EvAdfH9MPB4wou+PJAuSXAusAy6pqnn//hilsO/pkrsj5qCq+iM6swme0HTlSuNOBR4N7A+sBT400GrmWJIdgfOBN1TVLwddz6BNcjxG9v1RVZuqan86V3M9MMl+Ay5pRqMU9l5yd4KqWtPcrwMupPNVxyi7s/l+cvx7ynUDrmegqurO5o/avcDHGaH3R/Nd7PnAZ6rqgqZ5ZN8fkx2PUX5/jKuqnwOXA0cwz98foxT2XnK3S5KHNoNtSPJQ4JnADdNv1XoXA8ubx8uBiwZYy8CN/+FqPI8ReX80A7A+CdxUVSd1LRrJ98dUx2OE3x8Lkzy8ebwD8AzgB8zz98fIjMYHaH4a8hHuu+TuiYOtaHCSLKFzNg+dyyZ/dpSOR5KzgUPoTE15J/Ae4P8C5wGPBG4DXlBVIzFobYrjcQidLtoCVgGvGv9Oss2SHAx8A7geuLdpfged76lH7v0xzfE4ltF8f/whnQF4C+icMJ9XVe9L8gfM4/fHSIW9JEmjaJS68SVJGkmGvSRJLWfYS5LUcoa9JEktZ9hLktRyhr0kSS1n2Est10xF+uY+v8bjmmlO/zXJo/v5Wl2veXmSZXPxWtKwM+wlzYajgIuq6klV9W+DLkbS5gx7qYWSvDPJzUm+Cjy2aXtlku8muS7J+UkekmSnJLc21z4nyc5JVo0/n2S/+yf5djPT2YVJdmmuTPkG4BVJLptiu7ckeV3z+MNJvtY8PizJp5vHz0xyZZJrknyumXiFJAckuaKZnfHLEy7TSpIHJTkzyd/OxrGT2siwl1omyQF05n54EnA08ORm0QVV9eSqeiKdOclf3sxPfjnwnGadY4Dzq2rDFLs/C3hrVf0hncunvqeqvgB8DPhwVR06xXZfB57ePF4G7Nh8oDgY+EaS3YB3Ac9oZmJcAbyxWedk4PlVdQBwOtB9WecHA58BflhV7+rh8Egj6cGDLkDSrHs6cGFV/RogyfiET/s1Z78PB3YEvty0fwJ4C525Af4ceOVkO03yMODhVXVF03Qm8Lkea7oaOKCZfOke4Bo6of904HXAU4B9gX/pzLvCtsCVdHol9gMuadoX0JlOddz/pnNt8pGZ10HaGoa91E6TTXpxBnBUVV2X5GV0Jrqhqv4lyViSPwYWVNWsz15WVRuSrKLzYeJbwPeAQ+nMh35Tc39JVR3bvV2S/wjcWFVPnWLX3wIOTfKhqvrtbNcttYXd+FL7fB14XpIdmjPp5zbtOwFrm67xF0/Y5izgbOD/TLXTqvoF8P+SjHfHvwS4Yqr1p6jrzc39N4DjgWurMxvXt4GDkiwFaMYTPAa4GViY5KlN+zZJntC1z08CXwA+l8STF2kKhr3UMlV1DXAucC1wPp1gBXg3nWlaL6Ez/3a3zwC70An86SwH/i7J9+hMb/q+LSjtG8AewJVVdSfw2/Haqmo98DLg7Gbf3wYeV1W/A54P/M8k1zX/pqdN+PeeROdrgU8l8W+aNAmnuJVEkucDR1bVSwZdi6TZZ7eXNOKSnAw8C3j2oGuR1B+e2Uu6nyT/ABw0ofl/VdWU3+k32/0BcOkkiw6rqp/OVn2StoxhL0lSyzmYRZKkljPsJUlqOcNekqSWM+wlSWq5/w8O55gfnnb0YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.09307592583897241\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: -1.0599128678304488\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "2181.558469710561 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - pdays\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 2.615628689259389\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 6.934295545084991\n",
      "Leptokurtic \n",
      "\n",
      "JB Test for Normality\n",
      "142133.0390956953 0.0\n",
      "Distribution is not normal \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Histograms and Boxplots of important numerical variables (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA Interpretation\n",
    "\n",
    "## 1. Duration (last contact duration, in seconds (numeric))\n",
    "\n",
    "####   According to box plot 50% of values lie between 103 to 319 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### Right-skewness, with a substantial concentration of data towards lower values and a tail towards higher values.\n",
    "#### Leptokurtosis, indicating heavier tails and a sharper peak compared to a normal distribution, suggestive of a significant number of extreme values.\n",
    "#### A profound deviation from normality, as indicated by both the skewness, kurtosis values, and the Jarque-Bera test. This underscores a significant departure from the expected characteristics of a normal distribution, necessitating careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 2. Balance (average yearly balance)\n",
    "\n",
    "#### According to box plot 50% of values lie between 72 to 1428 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### Strong right-skewness, with a significant concentration of data towards lower values and a long tail extending towards higher values.\n",
    "#### High leptokurtosis, indicating heavier tails and a sharper peak compared to a normal distribution, suggestive of a large number of extreme values.\n",
    "#### A profound departure from normality, as evidenced by the extremely large skewness, kurtosis values, and JB test statistic. This highlights a significant deviation from the expected characteristics of a normal distribution, necessitating careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "\n",
    "\n",
    "## 3. Age\n",
    "\n",
    "#### According to box plot 50% of values lie between 33 to 48 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### Moderate right-skewness, with a moderate concentration of data towards lower values and a tail towards higher values.\n",
    "#### Platykurtosis, indicating lighter tails and a flatter peak compared to a normal distribution.\n",
    "#### A significant departure from normality, as indicated by both the skewness, kurtosis values, and JB test statistic. This suggests a deviation from the expected characteristics of a normal distribution and highlights the need for careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 4. day_of_week\n",
    "\n",
    "#### According to box plot 50% of values lie between 8 to 21 the median line is towards to top box hence negatively skewed. which is opposite to histogram but from analysis it can be due to so much random distribution of values\n",
    "\n",
    "#### Slight right-skewness, with a slight concentration of data towards lower values and a tail towards higher values.\n",
    "#### Platykurtosis, indicating lighter tails and a flatter peak compared to a normal distribution.\n",
    "#### A significant deviation from normality, as indicated by both the skewness, kurtosis values, and JB test statistic. This suggests a departure from the expected characteristics of a normal distribution and underscores the need for careful consideration in subsequent analyses or modeling.\n",
    "\n",
    "## 5. p_days (number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted))\n",
    "\n",
    "#### According to box plot 50% of values lie between -1 to -1 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### Right-skewness, with a significant concentration of data towards lower values and a long tail towards higher values.\n",
    "#### Leptokurtosis, indicating heavier tails and a sharper peak compared to a normal distribution, suggestive of a significant number of extreme values.\n",
    "#### A substantial deviation from normality, as indicated by both the skewness, kurtosis values, and JB test statistic. This suggests a notable departure from the expected characteristics of a normal distribution, underscoring the need for careful consideration in subsequent analyses or modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 6\n"
     ]
    }
   ],
   "source": [
    "# Categorical Encoding (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 7\n",
      "1- Standard Scaler\n",
      "2- Min-Max Scaler\n",
      "Which type of Scaler? 1\n"
     ]
    }
   ],
   "source": [
    "# Standardization of Numerical Features (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.61</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>1.31</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.29</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.29</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.57</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0  1.61 -0.09    -0.28       1.31    -0.14     0.26     0.89 -0.44    -0.26   \n",
       "1  0.29  1.46     1.37      -0.22    -0.14    -0.44     0.89 -0.44    -0.26   \n",
       "2 -0.75 -0.70    -0.28      -0.22    -0.14    -0.45     0.89  2.29    -0.26   \n",
       "3  0.57 -1.01    -0.28      -0.22    -0.14     0.05     0.89 -0.44    -0.26   \n",
       "4 -0.75 -1.01     1.37      -0.22    -0.14    -0.45    -1.12 -0.44    -0.26   \n",
       "\n",
       "   day_of_week  month  duration  campaign  pdays  previous   y  \n",
       "0        -1.30   0.82      0.01     -0.57  -0.41     -0.25  no  \n",
       "1        -1.30   0.82     -0.42     -0.57  -0.41     -0.25  no  \n",
       "2        -1.30   0.82     -0.71     -0.57  -0.41     -0.25  no  \n",
       "3        -1.30   0.82     -0.65     -0.57  -0.41     -0.25  no  \n",
       "4        -1.30   0.82     -0.23     -0.57  -0.41     -0.25  no  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier Detection & Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Outlier detection and treatment functionality under progress!'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection & Dimensionality Reduction not to be done in this submission as per the instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 9\n",
      "Random State? 42\n",
      "What % of data to be left for test? 20\n",
      "How many folds required? 5\n"
     ]
    }
   ],
   "source": [
    "# Train-Test Split and CV Folds (Pipeline Function)\n",
    "X_train,X_test,y_train,y_test,sk_folds=master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Selection (Pipeline Function)\n",
    "#master(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)\n",
    "# Due to limited RAM and data size, lazy classifier cannot be executed as it tests 29 different classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this submission, our model of choice is KNN regardless:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 11\n",
      "What is the value of K? 3\n"
     ]
    }
   ],
   "source": [
    "# Model Training (Pipeline Function)\n",
    "knn,scoresCV=master(data=df,X_train=X_train, X_test=X_test, y_train=y_train,sk_folds=sk_folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 12\n",
      "\n",
      "\n",
      "\n",
      "Train-Test Split Evaluation\n",
      "              precision  recall  f1-score  support\n",
      "no                 0.91    0.96      0.94  7952.00\n",
      "yes                0.54    0.34      0.42  1091.00\n",
      "accuracy           0.89    0.89      0.89     0.89\n",
      "macro avg          0.73    0.65      0.68  9043.00\n",
      "weighted avg       0.87    0.89      0.87  9043.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Cross-Validation Evaluation\n",
      "Cross Validation Scores:  [0.88654208 0.88243751 0.88254811 0.88852024 0.88509179]\n",
      "Average CV Score:  0.885027946426084\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation (Pipeline Function)\n",
    "master(knn=knn,X_test=X_test,y_test=y_test,scoresCV=scoresCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Result Interpretation\n",
    "### Classes: no , yes\n",
    "## Train-Test Split Evaluation:\n",
    "\n",
    "#### For the class 'no', the precision is 0.91, indicating that 91% of the instances classified as 'no' are actually 'no'.\n",
    "#### For the class 'yes', the precision is 0.54, indicating that 54% of the instances classified as 'yes' are actually 'yes'.\n",
    "#### For the class 'no', the recall is 0.96, indicating that 96% of the actual 'no' instances were correctly identified by the classifier.\n",
    "#### For the class 'yes', the recall is 0.34, indicating that 34% of the actual 'yes' instances were correctly identified by the classifier.\n",
    "#### For the class 'no', the F1-score is 0.94, representing the overall performance in terms of both precision and recall.\n",
    "#### For the class 'yes', the F1-score is 0.42, also representing the overall performance in terms of both precision and recall.\n",
    "#### Accuracy measures the overall correctness of the classifier across all classes. The overall accuracy of the classifier is 0.89, or 89%.\n",
    "#### AUC: 0.76 - The AUC score of 0.76 represents the model's ability to discriminate between positive and negative instances, with a higher value indicating better discrimination capability.\n",
    "\n",
    "\n",
    "## Cross-Validation Evaluation:\n",
    "\n",
    "#### Average CV Score: 0.8850\n",
    "#### The average cross-validation score provides a robust estimate of the model's generalization performance, indicating that the model performs well on unseen data.\n",
    "#### The individual cross-validation scores are also relatively high, ranging from approximately 0.8824 to 0.8885.\n",
    "#### In summary, both evaluation methods suggest that the model performs reasonably well. The Train-Test Split Evaluation provides insights into the model's performance on a specific test set, while the Cross-Validation Evaluation offers a more robust estimate of its generalization performance. The model demonstrates good accuracy and predictive capability, although there is room for improvement in precision, recall, and AUC\n",
    "#### While both methods show good results, the Cross-Validation Evaluation method provides a more robust estimate of the model's generalization performance and is therefore slightly preferable in this context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset - 5\n",
    "### Title: Heart Disease"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 1\n",
      "Please enter the UCI ID 45\n",
      "Successfuly Connected to the Data Source\n"
     ]
    }
   ],
   "source": [
    "# Dataset Fetching (Pipeline Function)\n",
    "df,label_column=master()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>342</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "283   35    1   2       122   192    0        0      174      0     0.00   \n",
       "284   61    1   4       148   203    0        0      161      0     0.00   \n",
       "285   58    1   4       114   318    0        1      140      0     4.40   \n",
       "286   58    0   4       170   225    1        2      146      1     2.80   \n",
       "287   58    1   2       125   220    0        0      144      0     0.40   \n",
       "288   56    1   2       130   221    0        2      163      0     0.00   \n",
       "289   56    1   2       120   240    0        0      169      0     0.00   \n",
       "290   67    1   3       152   212    0        2      150      0     0.80   \n",
       "291   55    0   2       132   342    0        0      166      0     1.20   \n",
       "292   44    1   4       120   169    0        0      144      1     2.80   \n",
       "293   63    1   4       140   187    0        2      144      1     4.00   \n",
       "294   63    0   4       124   197    0        0      136      1     0.00   \n",
       "295   41    1   2       120   157    0        0      182      0     0.00   \n",
       "296   59    1   4       164   176    1        2       90      0     1.00   \n",
       "297   57    0   4       140   241    0        0      123      1     0.20   \n",
       "298   45    1   1       110   264    0        0      132      0     1.20   \n",
       "299   68    1   4       144   193    1        0      141      0     3.40   \n",
       "300   57    1   4       130   131    0        0      115      1     1.20   \n",
       "301   57    0   2       130   236    0        2      174      0     0.00   \n",
       "302   38    1   3       138   175    0        0      173      0     0.00   \n",
       "\n",
       "     slope   ca  thal  num  \n",
       "283      1 0.00  3.00    0  \n",
       "284      1 1.00  7.00    2  \n",
       "285      3 3.00  6.00    4  \n",
       "286      2 2.00  6.00    2  \n",
       "287      2  nan  7.00    0  \n",
       "288      1 0.00  7.00    0  \n",
       "289      3 0.00  3.00    0  \n",
       "290      2 0.00  7.00    1  \n",
       "291      1 0.00  3.00    0  \n",
       "292      3 0.00  6.00    2  \n",
       "293      1 2.00  7.00    2  \n",
       "294      2 0.00  3.00    1  \n",
       "295      1 0.00  3.00    0  \n",
       "296      2 2.00  6.00    3  \n",
       "297      2 0.00  7.00    1  \n",
       "298      2 0.00  7.00    1  \n",
       "299      2 2.00  7.00    2  \n",
       "300      2 1.00  7.00    3  \n",
       "301      2 1.00  3.00    1  \n",
       "302      1  nan  3.00    0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values are Present\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        299 non-null    float64\n",
      " 12  thal      301 non-null    float64\n",
      " 13  num       303 non-null    int64  \n",
      "dtypes: float64(3), int64(11)\n",
      "memory usage: 33.3 KB\n",
      "None\n",
      "\n",
      "[0 1]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   1       145   233    1        2      150      0     2.30   \n",
       "1     67    1   4       160   286    0        2      108      1     1.50   \n",
       "2     67    1   4       120   229    0        2      129      1     2.60   \n",
       "3     37    1   3       130   250    0        0      187      0     3.50   \n",
       "4     41    0   2       130   204    0        2      172      0     1.40   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   45    1   1       110   264    0        0      132      0     1.20   \n",
       "299   68    1   4       144   193    1        0      141      0     3.40   \n",
       "300   57    1   4       130   131    0        0      115      1     1.20   \n",
       "301   57    0   2       130   236    0        2      174      0     0.00   \n",
       "302   38    1   3       138   175    0        0      173      0     0.00   \n",
       "\n",
       "     slope   ca  thal  num  \n",
       "0        3 0.00  6.00    0  \n",
       "1        2 3.00  3.00    1  \n",
       "2        2 2.00  7.00    1  \n",
       "3        3 0.00  3.00    0  \n",
       "4        1 0.00  3.00    0  \n",
       "..     ...  ...   ...  ...  \n",
       "298      2 0.00  7.00    1  \n",
       "299      2 2.00  7.00    1  \n",
       "300      2 1.00  7.00    1  \n",
       "301      2 1.00  3.00    1  \n",
       "302      1  nan  3.00    0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual Checking\n",
    "# No Manual Data Type Correction and Removal of Redundant Column Required\n",
    "print(df.info())\n",
    "\n",
    "# Manual label Correction Required\n",
    "print(\"\")\n",
    "df[label_column] = df[label_column].map({0: 0, 1: 1, 2: 1, 3: 1, 4: 1})\n",
    "print(df[label_column].unique())\n",
    "\n",
    "# No Manual Data Entry Error Correction Required\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 2\n",
      "Dataset is treated for missing values successfully\n",
      " \n",
      "age         0\n",
      "sex         0\n",
      "cp          0\n",
      "trestbps    0\n",
      "chol        0\n",
      "fbs         0\n",
      "restecg     0\n",
      "thalach     0\n",
      "exang       0\n",
      "oldpeak     0\n",
      "slope       0\n",
      "ca          0\n",
      "thal        0\n",
      "num         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing Value Treatment (Pipeline Function)\n",
    "master(df)\n",
    "print(\" \")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   1       145   233    1        2      150      0     2.30      3   \n",
       "1   67    1   4       160   286    0        2      108      1     1.50      2   \n",
       "2   67    1   4       120   229    0        2      129      1     2.60      2   \n",
       "3   37    1   3       130   250    0        0      187      0     3.50      3   \n",
       "4   41    0   2       130   204    0        2      172      0     1.40      1   \n",
       "\n",
       "    ca  thal  num  \n",
       "0 0.00  6.00    0  \n",
       "1 3.00  3.00    1  \n",
       "2 2.00  7.00    1  \n",
       "3 0.00  3.00    0  \n",
       "4 0.00  3.00    0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "      <td>303.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.44</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.16</td>\n",
       "      <td>131.69</td>\n",
       "      <td>246.69</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.99</td>\n",
       "      <td>149.61</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.73</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.04</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.96</td>\n",
       "      <td>17.60</td>\n",
       "      <td>51.78</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.99</td>\n",
       "      <td>22.88</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>133.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>241.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>275.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>166.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>564.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age    sex     cp  trestbps   chol    fbs  restecg  thalach  exang  \\\n",
       "count 303.00 303.00 303.00    303.00 303.00 303.00   303.00   303.00 303.00   \n",
       "mean   54.44   0.68   3.16    131.69 246.69   0.15     0.99   149.61   0.33   \n",
       "std     9.04   0.47   0.96     17.60  51.78   0.36     0.99    22.88   0.47   \n",
       "min    29.00   0.00   1.00     94.00 126.00   0.00     0.00    71.00   0.00   \n",
       "25%    48.00   0.00   3.00    120.00 211.00   0.00     0.00   133.50   0.00   \n",
       "50%    56.00   1.00   3.00    130.00 241.00   0.00     1.00   153.00   0.00   \n",
       "75%    61.00   1.00   4.00    140.00 275.00   0.00     2.00   166.00   1.00   \n",
       "max    77.00   1.00   4.00    200.00 564.00   1.00     2.00   202.00   1.00   \n",
       "\n",
       "       oldpeak  slope     ca   thal    num  \n",
       "count   303.00 303.00 303.00 303.00 303.00  \n",
       "mean      1.04   1.60   0.66   4.73   0.46  \n",
       "std       1.16   0.62   0.93   1.93   0.50  \n",
       "min       0.00   1.00   0.00   3.00   0.00  \n",
       "25%       0.00   1.00   0.00   3.00   0.00  \n",
       "50%       0.80   2.00   0.00   3.00   0.00  \n",
       "75%       1.60   2.00   1.00   7.00   1.00  \n",
       "max       6.20   3.00   3.00   7.00   1.00  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical Features Summary (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0   54.13\n",
       "1   45.87\n",
       "Name: num, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Feature Class Balance (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 5\n",
      "\n",
      "Box and Distribution Plot - cp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.8375810336955507\n",
      "Left Skewed \n",
      "\n",
      "Kurtosis: -0.41381067241945146\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "37.589766211007635 6.878377090124843e-09\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - thal\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.24403409291164038\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: -1.8985636326793576\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "48.514774569486526 2.918443264832149e-11\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - ca\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.2027985414541353\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 0.282799716113598\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "74.06927168472522 1.1102230246251565e-16\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - oldpeak\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.263425524589159\n",
      "Right Skewed \n",
      "\n",
      "Kurtosis: 1.5301925815010025\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "110.17162771033014 0.0\n",
      "Distribution is not normal \n",
      "\n",
      "\n",
      "Box and Distribution Plot - thalach\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.5347843657141508\n",
      "Left Skewed \n",
      "\n",
      "Kurtosis: -0.0723983595428126\n",
      "Platykurtic \n",
      "\n",
      "JB Test for Normality\n",
      "14.508887270634663 0.0007070256339871683\n",
      "Distribution is not normal \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Histograms and Boxplots of important numerical variables (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation\n",
    "\n",
    "\n",
    "## 1. thalach (maximum heart rate achieved)\n",
    "\n",
    "####   According to box plot 50% of values lie between 133.5 to 166 the median line is closer to top box hence negatively skewed\n",
    "\n",
    "####        The data distribution is left-skewed, indicating that it is asymmetric with a longer tail on the left side.\n",
    "####    The distribution has lighter tails and is less peaked than a normal distribution, suggesting fewer extreme values.\n",
    "####    The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot.\n",
    "####    This implies that the assumptions of normality may not hold for this dataset\n",
    "    \n",
    "## 2. oldpeak (ST depression induced by exercise relative to rest)\n",
    "\n",
    "####   According to box plot 50% of values lie between 0 to 1.6 the median line is slightly towards to bottom box hence slightly postively skewed\n",
    "\n",
    "#### The data distribution is right-skewed, indicating that it is asymmetric with a longer tail on the right side.\n",
    "#### The distribution has lighter tails and is less peaked than a normal distribution, suggesting fewer extreme values.\n",
    "#### The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot. This implies that the assumptions of normality may not hold for this dataset.\n",
    "\n",
    "## 3. thal (thal: 3 = normal; 6 = fixed defect; 7 = reversable defect based on heart rate)\n",
    "\n",
    "####   According to box plot 50% of values lie between 3 to 7 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### The data distribution is right-skewed, indicating that it is asymmetric with a longer tail on the right side.\n",
    "#### The distribution has heavier tails and a sharper peak than a normal distribution, suggesting more extreme values and higher kurtosis.\n",
    "#### The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot. This implies that the assumptions of normality may not hold for this dataset.\n",
    "\n",
    "\n",
    "## 4. cp (chest pain type(0-4))\n",
    "\n",
    "####   According to box plot 50% of values lie between 3 to 4 the median line is closer to top box hence negatively skewed\n",
    "\n",
    "#### The data distribution is left-skewed, indicating that it is asymmetric with a longer tail on the left side.\n",
    "#### The distribution has lighter tails and is less peaked than a normal distribution, suggesting fewer extreme values.\n",
    "#### The data doesn't follow a normal distribution, as indicated by the Jarque-Bera test as well as indicated from platykurtic and negatively skewed distribution shape.\n",
    "\n",
    "\n",
    "## 5. ca (number of major vessels (0-3) colored by flourosopy)\n",
    "\n",
    "####   According to box plot 50% of values lie between 0 to 1 the median line is towards to bottom box hence postively skewed\n",
    "\n",
    "#### The data distribution is right-skewed, indicating that it is asymmetric with a longer tail on the right side.\n",
    "#### The distribution has lighter tails and is less peaked than a normal distribution, suggesting fewer extreme values.\n",
    "#### The data does not follow a normal distribution, as indicated by both statistical tests and visual inspection of the histogram and box plot. This implies that the assumptions of normality may not hold for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 6\n"
     ]
    }
   ],
   "source": [
    "# Categorical Encoding (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 7\n",
      "1- Standard Scaler\n",
      "2- Min-Max Scaler\n",
      "Which type of Scaler? 1\n"
     ]
    }
   ],
   "source": [
    "# Standardization of Numerical Features (Pipeline Function)\n",
    "master(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>2.39</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.27</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.39</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.39</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.64</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.27</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.98</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   sex    cp  trestbps  chol   fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  0.95  0.69 -2.25      0.76 -0.26  2.39     1.02     0.02  -0.70     1.09   \n",
       "1  1.39  0.69  0.88      1.61  0.76 -0.42     1.02    -1.82   1.44     0.40   \n",
       "2  1.39  0.69  0.88     -0.67 -0.34 -0.42     1.02    -0.90   1.44     1.35   \n",
       "3 -1.93  0.69 -0.17     -0.10  0.06 -0.42    -1.00     1.64  -0.70     2.12   \n",
       "4 -1.49 -1.46 -1.21     -0.10 -0.83 -0.42     1.02     0.98  -0.70     0.31   \n",
       "\n",
       "   slope    ca  thal  num  \n",
       "0   2.27 -0.71  0.66    0  \n",
       "1   0.65  2.50 -0.90    1  \n",
       "2   0.65  1.43  1.17    1  \n",
       "3   2.27 -0.71 -0.90    0  \n",
       "4  -0.98 -0.71 -0.90    0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier Detection & Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Outlier detection and treatment functionality under progress!'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection & Dimensionality Reduction not to be done in this submission as per the instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Splitting and Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 9\n",
      "Random State? 42\n",
      "What % of data to be left for test? 20\n",
      "How many folds required? 5\n"
     ]
    }
   ],
   "source": [
    "# Train-Test Split and CV Folds (Pipeline Function)\n",
    "X_train,X_test,y_train,y_test,sk_folds=master(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 25.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 107, number of negative: 135\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000100 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 246\n",
      "[LightGBM] [Info] Number of data points in the train set: 242, number of used features: 13\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.442149 -> initscore=-0.232446\n",
      "[LightGBM] [Info] Start training from score -0.232446\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
      "Model                                                                           \n",
      "KNeighborsClassifier               0.92               0.92     0.92      0.92   \n",
      "NuSVC                              0.90               0.90     0.90      0.90   \n",
      "BernoulliNB                        0.90               0.90     0.90      0.90   \n",
      "LabelSpreading                     0.90               0.90     0.90      0.90   \n",
      "LabelPropagation                   0.90               0.90     0.90      0.90   \n",
      "SVC                                0.90               0.90     0.90      0.90   \n",
      "Perceptron                         0.90               0.90     0.90      0.90   \n",
      "RandomForestClassifier             0.89               0.89     0.89      0.89   \n",
      "RidgeClassifier                    0.89               0.88     0.88      0.89   \n",
      "RidgeClassifierCV                  0.89               0.88     0.88      0.89   \n",
      "CalibratedClassifierCV             0.89               0.88     0.88      0.89   \n",
      "LinearDiscriminantAnalysis         0.89               0.88     0.88      0.89   \n",
      "LogisticRegression                 0.89               0.88     0.88      0.89   \n",
      "LinearSVC                          0.89               0.88     0.88      0.89   \n",
      "XGBClassifier                      0.87               0.87     0.87      0.87   \n",
      "NearestCentroid                    0.87               0.87     0.87      0.87   \n",
      "QuadraticDiscriminantAnalysis      0.85               0.85     0.85      0.85   \n",
      "LGBMClassifier                     0.85               0.85     0.85      0.85   \n",
      "BaggingClassifier                  0.85               0.85     0.85      0.85   \n",
      "ExtraTreesClassifier               0.85               0.85     0.85      0.85   \n",
      "SGDClassifier                      0.85               0.85     0.85      0.85   \n",
      "GaussianNB                         0.84               0.84     0.84      0.84   \n",
      "AdaBoostClassifier                 0.84               0.84     0.84      0.84   \n",
      "PassiveAggressiveClassifier        0.82               0.82     0.82      0.82   \n",
      "DecisionTreeClassifier             0.72               0.72     0.72      0.72   \n",
      "ExtraTreeClassifier                0.70               0.70     0.70      0.70   \n",
      "DummyClassifier                    0.48               0.50     0.50      0.31   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "KNeighborsClassifier                 0.02  \n",
      "NuSVC                                0.02  \n",
      "BernoulliNB                          0.01  \n",
      "LabelSpreading                       0.02  \n",
      "LabelPropagation                     0.02  \n",
      "SVC                                  0.02  \n",
      "Perceptron                           0.02  \n",
      "RandomForestClassifier               0.25  \n",
      "RidgeClassifier                      0.02  \n",
      "RidgeClassifierCV                    0.02  \n",
      "CalibratedClassifierCV               0.06  \n",
      "LinearDiscriminantAnalysis           0.02  \n",
      "LogisticRegression                   0.02  \n",
      "LinearSVC                            0.03  \n",
      "XGBClassifier                        0.09  \n",
      "NearestCentroid                      0.01  \n",
      "QuadraticDiscriminantAnalysis        0.02  \n",
      "LGBMClassifier                       0.08  \n",
      "BaggingClassifier                    0.04  \n",
      "ExtraTreesClassifier                 0.14  \n",
      "SGDClassifier                        0.01  \n",
      "GaussianNB                           0.01  \n",
      "AdaBoostClassifier                   0.12  \n",
      "PassiveAggressiveClassifier          0.02  \n",
      "DecisionTreeClassifier               0.01  \n",
      "ExtraTreeClassifier                  0.01  \n",
      "DummyClassifier                      0.01  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Selection (Pipeline Function)\n",
    "master(X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this submission, our model of choice is KNN:\n",
    "\n",
    "# Model                            Accuracy  Balanced Accuracy  ROC AUC  F1 Score                                                                       \n",
    "# KNeighborsClassifier               0.92               0.92     0.92      0.92 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 11\n",
      "What is the value of K? 3\n"
     ]
    }
   ],
   "source": [
    "# Model Training (Pipeline Function)\n",
    "knn,scoresCV=master(data=df,X_train=X_train, X_test=X_test, y_train=y_train,sk_folds=sk_folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1- Fetch Data\n",
      "2- Missing Values Treatment\n",
      "3- EDA: Numerical Summary\n",
      "4- EDA: Class Balance(%)\n",
      "5- EDA: Histogram & Box Plot of Important Numerical Features\n",
      "6- Categorical Encoding\n",
      "7- Standardization of Numerical Features\n",
      "8- Outliers Detection & Treatment\n",
      "9-  Data Splitting & Cross-Validation\n",
      "10- Model Selection\n",
      "11- Model Training(KNN)\n",
      "12- Model Evaluation\n",
      "13- More Functions to be Added\n",
      "What do you want to do? 12\n",
      "\n",
      "\n",
      "\n",
      "Train-Test Split Evaluation\n",
      "              precision  recall  f1-score  support\n",
      "0                  0.90    0.93      0.92    29.00\n",
      "1                  0.94    0.91      0.92    32.00\n",
      "accuracy           0.92    0.92      0.92     0.92\n",
      "macro avg          0.92    0.92      0.92    61.00\n",
      "weighted avg       0.92    0.92      0.92    61.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Cross-Validation Evaluation\n",
      "Cross Validation Scores:  [0.86885246 0.78688525 0.83606557 0.85       0.8       ]\n",
      "Average CV Score:  0.828360655737705\n",
      "Number of CV Scores used in Average:  5\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation (Pipeline Function)\n",
    "master(knn=knn,X_test=X_test,y_test=y_test,scoresCV=scoresCV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Result Interpretation\n",
    "\n",
    "### Classes: 0 , 1\n",
    "## Train-Test Split Evaluation:\n",
    "\n",
    "#### For class '0', the precision is 0.90, indicating that 90% of the instances classified as '0' are actually '0'.\n",
    "#### For class '1', the precision is 0.94, indicating that 94% of the instances classified as '1' are actually '1'.\n",
    "#### For class '0', the recall is 0.93, indicating that 93% of the actual '0' instances were correctly identified by the classifier.\n",
    "#### For class '1', the recall is 0.91, indicating that 91% of the actual '1' instances were correctly identified by the classifier.\n",
    "#### For class '0', the F1-score is 0.92, representing the overall performance in terms of both precision and recall.\n",
    "#### For class '1', the F1-score is 0.92, also representing the overall performance in terms of both precision and recall.\n",
    "#### Accuracy measures the overall correctness of the classifier across all classes. The overall accuracy of the classifier is 0.92, or 92%.\n",
    "#### AUC: 0.94 - The AUC score of 0.94 represents the model's ability to discriminate between positive and negative instances, with a higher value indicating better discrimination capability.\n",
    "\n",
    "\n",
    "## Cross-Validation Evaluation:\n",
    "\n",
    "#### Average CV Score: 0.8284\n",
    "#### The average cross-validation score provides a robust estimate of the model's generalization performance, indicating that the model performs well on unseen data.\n",
    "#### The individual cross-validation scores are also relatively high, ranging from approximately 0.8 to 0.8689.\n",
    "#### In summary, both evaluation methods suggest that the model performs reasonably well. The Train-Test Split Evaluation provides insights into the model's performance on a specific test set, while the Cross-Validation Evaluation offers a more robust estimate of its generalization performance.\n",
    "#### While both methods show good results, the Cross-Validation Evaluation method provides a more robust estimate of the model's generalization performance and is therefore slightly preferable in this context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiled results are posted at the following github repo:\n",
    "#### https://github.com/hase3b/KNN-Pipeline"
   ]
  }
 ],
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