Heart-Disease-Classification.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b2ba7dd",
   "metadata": {},
   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n",
    "\n",
    "There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).\n",
    "\n",
    "**Create data dictionary**\n",
    "\n",
    "1. age - age in years\n",
    "2. sex - (1 = male; 0 = female)\n",
    "3. cp - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl\n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        * can range from mild symptoms to severe problems\n",
    "        * signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        * Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved\n",
    "9. exang - exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy\n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising\n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0acbd3a",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "We are going to use pandas,matplotlib, and numpy for data analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c35847cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and ploting libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# We want our plots to appear inline\n",
    "%matplotlib inline\n",
    "\n",
    "# Models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27b972d",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8948507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0455af28",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter expert on the dataset you're working with. \n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14da6a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60d6a54b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cf68060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfe5cfc1",
   "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        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d45624d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e52ccab6",
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24d0b30",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency according to sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ed64c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fc70095",
   "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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f345dce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                    figsize=(10,6),\n",
    "                                    color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for sex\")\n",
    "plt.xlabel(\"0 = No Diesease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028391d6",
   "metadata": {},
   "source": [
    "## Age vs Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60903470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with positive examples\n",
    "# plt.scatter(df[\"age\"][df[\"target\"]==0],\n",
    "#            df[\"thalach\"][df[\"target\"]==0],\n",
    "#            color=\"lightblue\")\n",
    "\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],\n",
    "           color=\"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           color=\"lightblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bfec144e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00119823",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency per Chest Pain Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7ec0d59",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d37aed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                  figsize=(10,6),\n",
    "                                  color=[\"salmon\", \"lightblue\"])\n",
    "plt.title(\"Heart Disease Frequency per chest pain type\")\n",
    "plt.xlabel(\"Chest pain type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f48ba99",
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0897cc70",
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a corelation metrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e77a86ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A little pritter corelation metrix\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidths=0.5,\n",
    "                fmt=\"0.2f\",\n",
    "                cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919a20db",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c0c81c1",
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "432ae97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test\n",
    "x = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1bfa5e",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, it's time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test it (use the patterns) on the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models:\n",
    "1. Logistic Regression \n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01287890",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"Random Forest\": RandomForestClassifier()\n",
    "}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, x_train, x_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of differetn Scikit-Learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    \n",
    "    # set random seed\n",
    "    np.random.seed(51)\n",
    "    # Make a dict to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(x_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(x_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c51979a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.819672131147541,\n",
       " 'KNN': 0.7049180327868853,\n",
       " 'Random Forest': 0.819672131147541}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models = models,\n",
    "                            x_train = x_train,\n",
    "                            x_test = x_test,\n",
    "                            y_train = y_train,\n",
    "                            y_test = y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e43b5445",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f31c4b63",
   "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>Logistic Regression</th>\n",
       "      <th>KNN</th>\n",
       "      <th>Random Forest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.819672</td>\n",
       "      <td>0.704918</td>\n",
       "      <td>0.819672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Logistic Regression       KNN  Random Forest\n",
       "accuracy             0.819672  0.704918       0.819672"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model_scores, index=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b8b2ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_comparison = pd.DataFrame(model_scores, index=[\"accuracy\"]).T.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ca0ecc",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know a model's first predictions aren't always what we should based our next steps off. What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hypyterparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)\n",
    "\n",
    "### Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe78c01e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets tune Rnn\n",
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "# Create a list of differnt values for n_neighbors\n",
    "neighbors = range(1,21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors = i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(x_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_score.append(knn.score(x_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_score.append(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "349382d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.590164\n",
       "1     0.639344\n",
       "2     0.704918\n",
       "3     0.688525\n",
       "4     0.704918\n",
       "5     0.737705\n",
       "6     0.770492\n",
       "7     0.688525\n",
       "8     0.672131\n",
       "9     0.704918\n",
       "10    0.754098\n",
       "11    0.688525\n",
       "12    0.737705\n",
       "13    0.688525\n",
       "14    0.721311\n",
       "15    0.704918\n",
       "16    0.737705\n",
       "17    0.754098\n",
       "18    0.672131\n",
       "19    0.688525\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0daa2d18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 77.05%\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"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_score, label=\"Train score\")\n",
    "plt.plot(neighbors, test_score, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_score)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098ef0ca",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3f8fa68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for logistic regression\n",
    "log_re_grid = {\n",
    "    \"C\": np.logspace(-4, 4, 20),\n",
    "    \"solver\": [\"liblinear\"]\n",
    "}\n",
    "\n",
    "# Create a hyperparameter grid for Random Forest classifier\n",
    "rf_grid = {\n",
    "    \"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe17afe",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a3cef6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_re_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "17e8bf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6b8a6107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392dafed",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "93b01990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=50,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=50, # Controls the numbre of iterations\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8776e7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a12c4926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "595bf969",
   "metadata": {},
   "source": [
    "## Hyperparamter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the same scores so far, we'll try and improve them again using GridSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e5ee57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\n",
    "    'C' : np.logspace(-4, 4, 20),\n",
    "    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
    "    }\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d76fc71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'lbfgs'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32137d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "58d5ce5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    }
   ],
   "source": [
    "# Create a hyperparameter grid for RandomForestclassifier\n",
    "rf_grid = {\n",
    "    \"n_estimators\": [100, 160, 210],\n",
    "           \"max_depth\": [3, 5],\n",
    "           \"min_samples_split\": [16,18,20],\n",
    "           \"min_samples_leaf\": [11, 13, 15]\n",
    "}\n",
    "\n",
    "# Setup grid hyperparameter search for RandomForestClassifier\n",
    "gs_rf = GridSearchCV(RandomForestClassifier(),\n",
    "                          param_grid=rf_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_rf.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fe3435ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 3,\n",
       "  'min_samples_leaf': 15,\n",
       "  'min_samples_split': 20,\n",
       "  'n_estimators': 160},\n",
       " 0.8524590163934426)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.best_params_, gs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59300770",
   "metadata": {},
   "source": [
    "**After all the training experiments, RandomForestClassifier() in RandomizedSearchCV has the best accuracy. So we will use it for further evaluation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2cb92a",
   "metadata": {},
   "source": [
    "## Evaluting our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7bc9724b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with tune modle\n",
    "y_preds = rs_rf.predict(x_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3fa66f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(rs_rf, x_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "38e17f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[24  4]\n",
      " [ 4 29]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "68944a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 1.5)\n",
    "\n",
    "def plt_cof_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(4,4))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                    annot = True,\n",
    "                    cbar = False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "plt_cof_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888da42b",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "78b6bf31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86        28\n",
      "           1       0.88      0.88      0.88        33\n",
      "\n",
      "    accuracy                           0.87        61\n",
      "   macro avg       0.87      0.87      0.87        61\n",
      "weighted avg       0.87      0.87      0.87        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc3da55",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dbe1ac3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best hyperparameter\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8346d70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier\n",
    "clf = RandomForestClassifier(n_estimators = 160,\n",
    "                             min_samples_split = 18,\n",
    "                             min_samples_leaf = 13, \n",
    "                             max_depth = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bf8fdf3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85245902, 0.90163934, 0.81967213, 0.86666667, 0.75      ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf, x, y, cv=5, scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8a3fd3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8380874316939891"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60191367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8146999236782518"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf, x, y, cv=5, scoring=\"precision\")\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b5f5cbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.909090909090909"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf, x, y, cv=5, scoring=\"recall\")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "064b4388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8635013589689079"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1\n",
    "cv_f1 = cross_val_score(clf, x, y, cv=5, scoring=\"f1\")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b42db739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\n",
    "    \"Accuracy\": cv_acc,\n",
    "    \"Precision\": cv_precision,\n",
    "    \"Recall\": cv_recall,\n",
    "    \"F1\": cv_f1},\n",
    "    index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aee8037",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another as asking, \"which features contributed most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f73f62ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, min_samples_leaf=13, min_samples_split=18,\n",
       "                       n_estimators=160)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model to the training set\n",
    "\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "18ddd08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp          0.173046\n",
       "thal        0.160397\n",
       "oldpeak     0.158223\n",
       "ca          0.126782\n",
       "thalach     0.107456\n",
       "exang       0.091308\n",
       "age         0.051206\n",
       "slope       0.042866\n",
       "sex         0.037420\n",
       "chol        0.024984\n",
       "trestbps    0.019090\n",
       "restecg     0.007224\n",
       "fbs         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the feature scores\n",
    "\n",
    "feature_scores = pd.Series(clf.feature_importances_, index=x_train.columns).sort_values(ascending=False)\n",
    "\n",
    "feature_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "696b5218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a seaborn bar plot\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax = sns.barplot(x=feature_scores, y=feature_scores.index)\n",
    "ax.set_title(\"Visualize feature scores of the features\")\n",
    "ax.set_yticklabels(feature_scores.index)\n",
    "ax.set_xlabel(\"Feature importance score\")\n",
    "ax.set_ylabel(\"Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c759cfda",
   "metadata": {},
   "source": [
    "## 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself...\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric) how would you export it and share it with others?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9531e1e4",
   "metadata": {},
   "source": [
    "##  Saving and loading Machine learning models\n",
    "Two ways to save and load machine learning models:\n",
    "\n",
    "    1. With Python's pickle Module\n",
    "    2. With the joblib module\n",
    "    \n",
    "  **Joblib Module**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6fe0bd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['randomized_heart_disease_saved_model.joblib']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "#save model to a file\n",
    "dump(rs_rf, filename=\"randomized_heart_disease_saved_model.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d706fc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import a saved joblib model\n",
    "load_joblib_saved_model = load(filename=\"randomized_heart_disease_saved_model.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "48029778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction of joblib model\n",
    "joblib_y_preds = load_joblib_saved_model.predict(x_test)\n",
    "joblib_y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed60c43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
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}