Drug Rec.ipynb

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   "cell_type": "markdown",
   "id": "710a3bac-2fed-4c0e-8e62-d7f8774e861e",
   "metadata": {},
   "source": [
    "<h3> Import required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d671ac91-df4f-4e6a-b16f-f56c9b031d16",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from matplotlib import style\n",
    "style.use('grayscale')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bb0214-d31d-4edb-8849-58d54cc65154",
   "metadata": {},
   "source": [
    "<h4> Read in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a70cc8a-d1a0-40d5-9633-59d92708077a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('/Users/Brenden/Downloads/Dataset/CSV/drug200.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b563fa4a-ed00-453c-89b6-71894ecef718",
   "metadata": {},
   "source": [
    "<h4> Display the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75a2b5f5-fdea-40fb-b93c-2686a9122854",
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   "outputs": [
    {
     "data": {
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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>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  DrugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  DrugY"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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   "execution_count": 5,
   "id": "9c6431c9-e05d-479b-b1ab-9f36fdc4adbe",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>56</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>11.567</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>16</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>12.006</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>52</td>\n",
       "      <td>M</td>\n",
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       "      <td>9.894</td>\n",
       "      <td>drugX</td>\n",
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       "      <th>198</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>14.020</td>\n",
       "      <td>drugX</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>11.349</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "195   56   F     LOW        HIGH   11.567  drugC\n",
       "196   16   M     LOW        HIGH   12.006  drugC\n",
       "197   52   M  NORMAL        HIGH    9.894  drugX\n",
       "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
       "199   40   F     LOW      NORMAL   11.349  drugX"
      ]
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     "execution_count": 5,
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab090167-8f63-452a-b263-b34948f37878",
   "metadata": {},
   "source": [
    "<h4> Check the number of rows & columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d45b42d-98bd-493c-8f74-5e0605b22c03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ee000b-5b17-4fdf-aa75-bc7b14f22d63",
   "metadata": {},
   "source": [
    "<h4> Get a more detailed overview of the dataset"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "0342a712-36da-4d48-ba6b-ea4b294be914",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Age          200 non-null    int64  \n",
      " 1   Sex          200 non-null    object \n",
      " 2   BP           200 non-null    object \n",
      " 3   Cholesterol  200 non-null    object \n",
      " 4   Na_to_K      200 non-null    float64\n",
      " 5   Drug         200 non-null    object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6071d4f-51a8-44b5-9c17-1feb7f112f8d",
   "metadata": {},
   "source": [
    "<h3> Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f1ad202-03ad-49af-af56-cf900201448d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing the columns names to lowercase beacuse its easier for me to work with...Optional\n",
    "df.columns= df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f63f8a-5325-4a5c-bafb-f2ea3eda3d96",
   "metadata": {},
   "source": [
    "<h4> Check the dataset for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9935e3e-504b-4f13-85ec-91becc21d1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            0\n",
       "sex            0\n",
       "bp             0\n",
       "cholesterol    0\n",
       "na_to_k        0\n",
       "drug           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9882c77b-b457-48be-97d9-51950ab9e34c",
   "metadata": {},
   "source": [
    "<h4> Check for any duplicate values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6383135c-7fa6-4909-b241-d8edefd7c332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf18cd3c-e746-4234-a8b1-b17e23ac68f7",
   "metadata": {},
   "source": [
    "<h4> Use Descriptive Statistics  to get a  sense of the numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5800460-bf5d-484c-aba8-f3f9eea8a603",
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>200.0</td>\n",
       "      <td>44.315000</td>\n",
       "      <td>16.544315</td>\n",
       "      <td>15.000</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>58.00</td>\n",
       "      <td>74.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na_to_k</th>\n",
       "      <td>200.0</td>\n",
       "      <td>16.084485</td>\n",
       "      <td>7.223956</td>\n",
       "      <td>6.269</td>\n",
       "      <td>10.4455</td>\n",
       "      <td>13.9365</td>\n",
       "      <td>19.38</td>\n",
       "      <td>38.247</td>\n",
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      ],
      "text/plain": [
       "         count       mean        std     min      25%      50%    75%     max\n",
       "age      200.0  44.315000  16.544315  15.000  31.0000  45.0000  58.00  74.000\n",
       "na_to_k  200.0  16.084485   7.223956   6.269  10.4455  13.9365  19.38  38.247"
      ]
     },
     "execution_count": 12,
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   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd5bb10-75a1-433e-8d80-590d014c1b91",
   "metadata": {},
   "source": [
    "<h3> Perform EDA to observe how the data is distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73510ec2-32fb-4b1b-ab00-b758a3ac49c6",
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   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat= ['sex','bp','cholesterol','drug'] #categorical features\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "for i, col in enumerate(cat):\n",
    "    plt.subplot(2,2, i+1)\n",
    "    sns.countplot(df,x=col)\n",
    "    plt.title(f'Distribution of {col}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3105341-425b-4ff1-87a8-ced50798f03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num= ['age','na_to_k',] #numerical data\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "\n",
    "for i, col in enumerate(num):\n",
    "    plt.subplot(1,2,i+1)\n",
    "    sns.distplot(df[col])\n",
    "    plt.title(f'Distribution of {col}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4babde0b-5413-4415-912c-02ae2195239c",
   "metadata": {},
   "source": [
    "<h3> Encode Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4650d621-488b-406c-8532-87228c384d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sex']= df['sex'].map({'F':0,'M':1})\n",
    "\n",
    "df['bp']= df['bp'].map({'LOW':0, 'NORMAL':1, 'HIGH':2})\n",
    "\n",
    "df['cholesterol']= df['cholesterol'].map({'NORMAL':0, 'HIGH':1})\n",
    "\n",
    "df['drug']= df['drug'].map({'drugA':0, 'drugB':1, 'drugC':2, 'drugX':3, 'DrugY':4})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834b2dc2-2f74-422e-98ec-9fcab99680bb",
   "metadata": {},
   "source": [
    "<h3> Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c403b63-def7-4e62-8ae5-ea36680e0989",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc= StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a54d5cd-ec2a-4fd6-8e71-32857ad9710f",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = ['age','na_to_k']\n",
    "for i in num:\n",
    "    df[i]= sc.fit_transform(df[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0222fc73-1744-412d-89e6-9edda6f2bc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop('drug',axis=1)\n",
    "y= df['drug']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bceff25-2e4f-4718-ad15-f08c0e69dce2",
   "metadata": {},
   "source": [
    "<h3> Split the dataset into Training & Testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "708194b0-3b3b-4fac-9dcb-ad4036ae9fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((140, 5), (60, 5), (140,), (60,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d02c6e-c581-46eb-932e-b7864d5200ec",
   "metadata": {},
   "source": [
    "<h3> Import the models to be trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50f8d5b0-8289-4fc7-917b-1ebe39c48da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e87ce9-e865-4fe9-b5c3-a306a799ed37",
   "metadata": {},
   "source": [
    "<h2> Train the Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ebe9e0-e319-4548-a9cb-f39185d49641",
   "metadata": {},
   "source": [
    "<h4> Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64a4d18c-63b7-48bf-a581-4b61727d69ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf= RandomForestClassifier() #Create instance of RandomForest\n",
    "\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "56bb0b3c-498e-4c15-8528-3b1656b33d37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         7\n",
      "           1       1.00      1.00      1.00         3\n",
      "           2       1.00      1.00      1.00         6\n",
      "           3       1.00      1.00      1.00        18\n",
      "           4       1.00      1.00      1.00        26\n",
      "\n",
      "    accuracy                           1.00        60\n",
      "   macro avg       1.00      1.00      1.00        60\n",
      "weighted avg       1.00      1.00      1.00        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred= rf.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "acc1=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3b0bfd-b749-42cc-8d64-fe6dd1304528",
   "metadata": {},
   "source": [
    "<h4> KNeighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "11a89fd7-06a4-4267-bc7d-5f30ed62fc8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn= KNeighborsClassifier()\n",
    "\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7f32c465-5efe-40d6-9c21-9fa014b4e19c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         7\n",
      "           1       0.50      1.00      0.67         3\n",
      "           2       1.00      0.33      0.50         6\n",
      "           3       0.78      1.00      0.88        18\n",
      "           4       1.00      0.85      0.92        26\n",
      "\n",
      "    accuracy                           0.87        60\n",
      "   macro avg       0.86      0.84      0.79        60\n",
      "weighted avg       0.91      0.87      0.86        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred= knn.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "acc2=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dc9756-6caf-4342-b2e2-d443761734fe",
   "metadata": {},
   "source": [
    "<h4> SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0453d37e-d38f-4061-8e26-0d4cbd3da86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm= SVC()\n",
    "\n",
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b7f7ac40-e99c-4ea6-ba24-26f1f2a10f10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.71      0.83         7\n",
      "           1       0.43      1.00      0.60         3\n",
      "           2       1.00      1.00      1.00         6\n",
      "           3       1.00      1.00      1.00        18\n",
      "           4       1.00      0.92      0.96        26\n",
      "\n",
      "    accuracy                           0.93        60\n",
      "   macro avg       0.89      0.93      0.88        60\n",
      "weighted avg       0.97      0.93      0.94        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred= svm.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "acc3=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2ca7c1-d265-4ccf-bc1a-d6ac072ed05e",
   "metadata": {},
   "source": [
    "<H4> Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "69388ead-ca4c-463b-b2da-7e86d31a5ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr= LogisticRegression()\n",
    "\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a998b1c7-ca3d-4932-b9d9-bf1a576116eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.93         7\n",
      "           1       0.75      1.00      0.86         3\n",
      "           2       1.00      0.17      0.29         6\n",
      "           3       0.78      1.00      0.88        18\n",
      "           4       1.00      0.92      0.96        26\n",
      "\n",
      "    accuracy                           0.88        60\n",
      "   macro avg       0.88      0.82      0.78        60\n",
      "weighted avg       0.91      0.88      0.86        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred= lr.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "acc4=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ab362e-07d1-4193-935e-86a0f0ef32d2",
   "metadata": {},
   "source": [
    "<h3> Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7a5ec88a-8666-4419-91cc-4742acbbe246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score= pd.Series([acc1,acc2,acc3,acc4],['RF','KNN','SVM','LOG_REG'])\n",
    "score.plot(kind='bar',figsize=(10,5))\n",
    "plt.xticks(rotation=0,ha='center')\n",
    "plt.title('MODEL VS PERFORMANCE')\n",
    "for i,v in enumerate(score):\n",
    "    plt.text(i,v +0.01, str(round(v,2)),ha='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659663d8-7997-4243-8a30-afbcbc9db69e",
   "metadata": {},
   "source": [
    "We can see that Random Forest model out performs the rest of the models ,attaining a perfect 100% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f284634b-fd50-4b3f-a3bb-628091d403fb",
   "metadata": {},
   "source": [
    "<h4> Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fec4c86e-bc0a-4665-9109-5290dcec1d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(rf,open('./drug_rf.sav','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ce8e9a8-8ff7-4c8c-9aa8-fd308a1659eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}