Feature_Engineering_and_Model_Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4437a980",
   "metadata": {
    "id": "4437a980"
   },
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b4d7e0",
   "metadata": {
    "id": "74b4d7e0"
   },
   "source": [
    "#### Import Packages and CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5530cc59",
   "metadata": {
    "id": "5530cc59",
    "outputId": "5fa0529c-5adf-480a-b56f-116e14b8514e"
   },
   "outputs": [],
   "source": [
    "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",
    "pd.pandas.set_option(\"display.max_columns\", None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a14bb18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 50)\n"
     ]
    }
   ],
   "source": [
    "# Create Dataframe\n",
    "df = pd.read_csv(r\"data/telco.csv\")\n",
    "# Print shape of dataset\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27265bd",
   "metadata": {
    "id": "c27265bd"
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbad805a",
   "metadata": {
    "id": "dbad805a"
   },
   "source": [
    "### Handling Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0c1c0d",
   "metadata": {
    "id": "0a0c1c0d"
   },
   "source": [
    "* Handling Missing values \n",
    "* Handling Duplicates\n",
    "* Check data type\n",
    "* Understand the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b4a428",
   "metadata": {
    "id": "40b4a428"
   },
   "source": [
    "#### Check Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2b94aa8f",
   "metadata": {
    "id": "2b94aa8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offer 55.04756 % missing values\n",
      "Internet Type 21.6669 % missing values\n",
      "Churn Category 73.46301 % missing values\n",
      "Churn Reason 73.46301 % missing values\n"
     ]
    }
   ],
   "source": [
    "##these are the features with nan value\n",
    "features_with_na=[features for features in df.columns if df[features].isnull().sum()>=1]\n",
    "for feature in features_with_na:\n",
    "    print(feature,np.round(df[feature].isnull().mean()*100,5), '% missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f08d8e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Offer', 'Internet Type', 'Churn Category', 'Churn Reason']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "056376b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Offer E', 'Offer D', 'Offer C', 'Offer B', 'Offer A'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Offer.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28e5d06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Competitor', 'Dissatisfaction', 'Price', 'Other', 'Attitude', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b73afa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DSL', 'Fiber Optic', 'Cable', nan], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Internet Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8899ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Internet Type\n",
       "Fiber Optic    3035\n",
       "DSL            1652\n",
       "Cable           830\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Internet Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34621a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Competitor offered more data', 'Competitor made better offer',\n",
       "       'Limited range of services', 'Extra data charges',\n",
       "       'Competitor had better devices', \"Don't know\",\n",
       "       'Service dissatisfaction',\n",
       "       'Lack of affordable download/upload speed',\n",
       "       'Product dissatisfaction', 'Long distance charges',\n",
       "       'Poor expertise of online support', 'Attitude of support person',\n",
       "       'Network reliability', 'Competitor offered higher download speeds',\n",
       "       'Moved', 'Price too high', 'Attitude of service provider',\n",
       "       'Poor expertise of phone support', 'Deceased', nan,\n",
       "       'Lack of self-service on Website'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn Reason'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bb11b3",
   "metadata": {
    "id": "31bb11b3"
   },
   "source": [
    "* **There are missing values in the dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afe70b5",
   "metadata": {},
   "source": [
    "Handling missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08f92e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Internet Type'] = df['Internet Type'].fillna(df['Internet Type'].mode()[0])\n",
    "df['Churn Category'] = df['Churn Category'].fillna('Other')\n",
    "df['Churn Reason'] = df['Churn Reason'].fillna('No Reason')\n",
    "df['Offer'] = df['Offer'].fillna('No Offer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4acabaf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##these are the features with nan value\n",
    "features_with_na=[features for features in df.columns if df[features].isnull().sum()>=1]\n",
    "for feature in features_with_na:\n",
    "    print(feature,np.round(df[feature].isnull().mean()*100,5), '% missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d793225a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_with_na"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e731a8",
   "metadata": {},
   "source": [
    "- There is No null value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76eafe12",
   "metadata": {
    "id": "76eafe12"
   },
   "source": [
    "### 3.2 Other Data Cleaning steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471fd48f",
   "metadata": {
    "id": "471fd48f"
   },
   "source": [
    "**Handling Duplicates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8fa17e0",
   "metadata": {
    "id": "d8fa17e0",
    "outputId": "7f1d6a37-65e2-4b4f-f201-69b9c3a80150"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f574d4c",
   "metadata": {
    "id": "3f574d4c"
   },
   "source": [
    "* **No Duplicates in the dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf6d275",
   "metadata": {
    "id": "5cf6d275"
   },
   "source": [
    "**Remove Customer ID, Country, Quarter and State from the dataset as it cannot used in Model Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "828c0a89",
   "metadata": {
    "id": "828c0a89"
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Customer ID','Country','Quarter','State'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e72c29a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Under 30</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Citizen</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Dependents</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>Whittier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zip Code</th>\n",
       "      <td>90022</td>\n",
       "      <td>90063</td>\n",
       "      <td>90065</td>\n",
       "      <td>90303</td>\n",
       "      <td>90602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>34.02381</td>\n",
       "      <td>34.044271</td>\n",
       "      <td>34.108833</td>\n",
       "      <td>33.936291</td>\n",
       "      <td>33.972119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>-118.156582</td>\n",
       "      <td>-118.185237</td>\n",
       "      <td>-118.229715</td>\n",
       "      <td>-118.332639</td>\n",
       "      <td>-118.020188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>68701</td>\n",
       "      <td>55668</td>\n",
       "      <td>47534</td>\n",
       "      <td>27778</td>\n",
       "      <td>26265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Referred a Friend</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Referrals</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure in Months</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Offer</th>\n",
       "      <td>No Offer</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Offer C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phone Service</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <td>0.0</td>\n",
       "      <td>48.85</td>\n",
       "      <td>11.33</td>\n",
       "      <td>19.76</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Lines</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Service</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Type</th>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg Monthly GB Download</th>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Security</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Backup</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Device Protection Plan</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium Tech Support</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming TV</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming Movies</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming Music</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unlimited Data</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paperless Billing</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment Method</th>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly Charge</th>\n",
       "      <td>39.65</td>\n",
       "      <td>80.65</td>\n",
       "      <td>95.45</td>\n",
       "      <td>98.5</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Charges</th>\n",
       "      <td>39.65</td>\n",
       "      <td>633.3</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>2514.5</td>\n",
       "      <td>2868.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Refunds</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.61</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <td>0.0</td>\n",
       "      <td>390.8</td>\n",
       "      <td>203.94</td>\n",
       "      <td>494.0</td>\n",
       "      <td>234.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Revenue</th>\n",
       "      <td>59.65</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>1910.88</td>\n",
       "      <td>2995.07</td>\n",
       "      <td>3102.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Status</th>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Label</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Score</th>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLTV</th>\n",
       "      <td>5433</td>\n",
       "      <td>5302</td>\n",
       "      <td>3179</td>\n",
       "      <td>5337</td>\n",
       "      <td>2793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Category</th>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Dissatisfaction</td>\n",
       "      <td>Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Reason</th>\n",
       "      <td>Competitor offered more data</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "      <td>Limited range of services</td>\n",
       "      <td>Extra data charges</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              0  \\\n",
       "Gender                                                     Male   \n",
       "Age                                                          78   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                      No   \n",
       "Dependents                                                   No   \n",
       "Number of Dependents                                          0   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90022   \n",
       "Latitude                                               34.02381   \n",
       "Longitude                                           -118.156582   \n",
       "Population                                                68701   \n",
       "Referred a Friend                                            No   \n",
       "Number of Referrals                                           0   \n",
       "Tenure in Months                                              1   \n",
       "Offer                                                  No Offer   \n",
       "Phone Service                                                No   \n",
       "Avg Monthly Long Distance Charges                           0.0   \n",
       "Multiple Lines                                               No   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                               DSL   \n",
       "Avg Monthly GB Download                                       8   \n",
       "Online Security                                              No   \n",
       "Online Backup                                                No   \n",
       "Device Protection Plan                                      Yes   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                 No   \n",
       "Streaming Movies                                            Yes   \n",
       "Streaming Music                                              No   \n",
       "Unlimited Data                                               No   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                  Bank Withdrawal   \n",
       "Monthly Charge                                            39.65   \n",
       "Total Charges                                             39.65   \n",
       "Total Refunds                                               0.0   \n",
       "Total Extra Data Charges                                     20   \n",
       "Total Long Distance Charges                                 0.0   \n",
       "Total Revenue                                             59.65   \n",
       "Satisfaction Score                                            3   \n",
       "Customer Status                                         Churned   \n",
       "Churn Label                                                 Yes   \n",
       "Churn Score                                                  91   \n",
       "CLTV                                                       5433   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor offered more data   \n",
       "\n",
       "                                                              1  \\\n",
       "Gender                                                   Female   \n",
       "Age                                                          74   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                     Yes   \n",
       "Dependents                                                  Yes   \n",
       "Number of Dependents                                          1   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90063   \n",
       "Latitude                                              34.044271   \n",
       "Longitude                                           -118.185237   \n",
       "Population                                                55668   \n",
       "Referred a Friend                                           Yes   \n",
       "Number of Referrals                                           1   \n",
       "Tenure in Months                                              8   \n",
       "Offer                                                   Offer E   \n",
       "Phone Service                                               Yes   \n",
       "Avg Monthly Long Distance Charges                         48.85   \n",
       "Multiple Lines                                              Yes   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                       Fiber Optic   \n",
       "Avg Monthly GB Download                                      17   \n",
       "Online Security                                              No   \n",
       "Online Backup                                               Yes   \n",
       "Device Protection Plan                                       No   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                 No   \n",
       "Streaming Movies                                             No   \n",
       "Streaming Music                                              No   \n",
       "Unlimited Data                                              Yes   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                      Credit Card   \n",
       "Monthly Charge                                            80.65   \n",
       "Total Charges                                             633.3   \n",
       "Total Refunds                                               0.0   \n",
       "Total Extra Data Charges                                      0   \n",
       "Total Long Distance Charges                               390.8   \n",
       "Total Revenue                                            1024.1   \n",
       "Satisfaction Score                                            3   \n",
       "Customer Status                                         Churned   \n",
       "Churn Label                                                 Yes   \n",
       "Churn Score                                                  69   \n",
       "CLTV                                                       5302   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor made better offer   \n",
       "\n",
       "                                                              2  \\\n",
       "Gender                                                     Male   \n",
       "Age                                                          71   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                      No   \n",
       "Dependents                                                  Yes   \n",
       "Number of Dependents                                          3   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90065   \n",
       "Latitude                                              34.108833   \n",
       "Longitude                                           -118.229715   \n",
       "Population                                                47534   \n",
       "Referred a Friend                                            No   \n",
       "Number of Referrals                                           0   \n",
       "Tenure in Months                                             18   \n",
       "Offer                                                   Offer D   \n",
       "Phone Service                                               Yes   \n",
       "Avg Monthly Long Distance Charges                         11.33   \n",
       "Multiple Lines                                              Yes   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                       Fiber Optic   \n",
       "Avg Monthly GB Download                                      52   \n",
       "Online Security                                              No   \n",
       "Online Backup                                                No   \n",
       "Device Protection Plan                                       No   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                Yes   \n",
       "Streaming Movies                                            Yes   \n",
       "Streaming Music                                             Yes   \n",
       "Unlimited Data                                              Yes   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                  Bank Withdrawal   \n",
       "Monthly Charge                                            95.45   \n",
       "Total Charges                                           1752.55   \n",
       "Total Refunds                                             45.61   \n",
       "Total Extra Data Charges                                      0   \n",
       "Total Long Distance Charges                              203.94   \n",
       "Total Revenue                                           1910.88   \n",
       "Satisfaction Score                                            2   \n",
       "Customer Status                                         Churned   \n",
       "Churn Label                                                 Yes   \n",
       "Churn Score                                                  81   \n",
       "CLTV                                                       3179   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor made better offer   \n",
       "\n",
       "                                                           3  \\\n",
       "Gender                                                Female   \n",
       "Age                                                       78   \n",
       "Under 30                                                  No   \n",
       "Senior Citizen                                           Yes   \n",
       "Married                                                  Yes   \n",
       "Dependents                                               Yes   \n",
       "Number of Dependents                                       1   \n",
       "City                                               Inglewood   \n",
       "Zip Code                                               90303   \n",
       "Latitude                                           33.936291   \n",
       "Longitude                                        -118.332639   \n",
       "Population                                             27778   \n",
       "Referred a Friend                                        Yes   \n",
       "Number of Referrals                                        1   \n",
       "Tenure in Months                                          25   \n",
       "Offer                                                Offer C   \n",
       "Phone Service                                            Yes   \n",
       "Avg Monthly Long Distance Charges                      19.76   \n",
       "Multiple Lines                                            No   \n",
       "Internet Service                                         Yes   \n",
       "Internet Type                                    Fiber Optic   \n",
       "Avg Monthly GB Download                                   12   \n",
       "Online Security                                           No   \n",
       "Online Backup                                            Yes   \n",
       "Device Protection Plan                                   Yes   \n",
       "Premium Tech Support                                      No   \n",
       "Streaming TV                                             Yes   \n",
       "Streaming Movies                                         Yes   \n",
       "Streaming Music                                           No   \n",
       "Unlimited Data                                           Yes   \n",
       "Contract                                      Month-to-Month   \n",
       "Paperless Billing                                        Yes   \n",
       "Payment Method                               Bank Withdrawal   \n",
       "Monthly Charge                                          98.5   \n",
       "Total Charges                                         2514.5   \n",
       "Total Refunds                                          13.43   \n",
       "Total Extra Data Charges                                   0   \n",
       "Total Long Distance Charges                            494.0   \n",
       "Total Revenue                                        2995.07   \n",
       "Satisfaction Score                                         2   \n",
       "Customer Status                                      Churned   \n",
       "Churn Label                                              Yes   \n",
       "Churn Score                                               88   \n",
       "CLTV                                                    5337   \n",
       "Churn Category                               Dissatisfaction   \n",
       "Churn Reason                       Limited range of services   \n",
       "\n",
       "                                                    4  \n",
       "Gender                                         Female  \n",
       "Age                                                80  \n",
       "Under 30                                           No  \n",
       "Senior Citizen                                    Yes  \n",
       "Married                                           Yes  \n",
       "Dependents                                        Yes  \n",
       "Number of Dependents                                1  \n",
       "City                                         Whittier  \n",
       "Zip Code                                        90602  \n",
       "Latitude                                    33.972119  \n",
       "Longitude                                 -118.020188  \n",
       "Population                                      26265  \n",
       "Referred a Friend                                 Yes  \n",
       "Number of Referrals                                 1  \n",
       "Tenure in Months                                   37  \n",
       "Offer                                         Offer C  \n",
       "Phone Service                                     Yes  \n",
       "Avg Monthly Long Distance Charges                6.33  \n",
       "Multiple Lines                                    Yes  \n",
       "Internet Service                                  Yes  \n",
       "Internet Type                             Fiber Optic  \n",
       "Avg Monthly GB Download                            14  \n",
       "Online Security                                    No  \n",
       "Online Backup                                      No  \n",
       "Device Protection Plan                             No  \n",
       "Premium Tech Support                               No  \n",
       "Streaming TV                                       No  \n",
       "Streaming Movies                                   No  \n",
       "Streaming Music                                    No  \n",
       "Unlimited Data                                    Yes  \n",
       "Contract                               Month-to-Month  \n",
       "Paperless Billing                                 Yes  \n",
       "Payment Method                        Bank Withdrawal  \n",
       "Monthly Charge                                   76.5  \n",
       "Total Charges                                 2868.15  \n",
       "Total Refunds                                     0.0  \n",
       "Total Extra Data Charges                            0  \n",
       "Total Long Distance Charges                    234.21  \n",
       "Total Revenue                                 3102.36  \n",
       "Satisfaction Score                                  2  \n",
       "Customer Status                               Churned  \n",
       "Churn Label                                       Yes  \n",
       "Churn Score                                        67  \n",
       "CLTV                                             2793  \n",
       "Churn Category                                  Price  \n",
       "Churn Reason                       Extra data charges  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d48a184",
   "metadata": {
    "id": "6d48a184"
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8453d379",
   "metadata": {
    "id": "8453d379"
   },
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66542de1",
   "metadata": {
    "id": "66542de1"
   },
   "source": [
    "### Type of Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c375c5b",
   "metadata": {},
   "source": [
    "### Split X and Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0392ce2f",
   "metadata": {},
   "source": [
    "* **Split Dataframe to X and y**\n",
    "* **Here we set a variable X i.e, independent columns, and a variable y i.e, dependent column as the “Churn Label” column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f78a46c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Churn Label', axis=1)\n",
    "y = df['Churn Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d705f97a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Yes\n",
       "1    Yes\n",
       "2    Yes\n",
       "3    Yes\n",
       "4    Yes\n",
       "Name: Churn Label, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91e184e5",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Under 30</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Citizen</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Dependents</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>Whittier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zip Code</th>\n",
       "      <td>90022</td>\n",
       "      <td>90063</td>\n",
       "      <td>90065</td>\n",
       "      <td>90303</td>\n",
       "      <td>90602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>34.02381</td>\n",
       "      <td>34.044271</td>\n",
       "      <td>34.108833</td>\n",
       "      <td>33.936291</td>\n",
       "      <td>33.972119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>-118.156582</td>\n",
       "      <td>-118.185237</td>\n",
       "      <td>-118.229715</td>\n",
       "      <td>-118.332639</td>\n",
       "      <td>-118.020188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>68701</td>\n",
       "      <td>55668</td>\n",
       "      <td>47534</td>\n",
       "      <td>27778</td>\n",
       "      <td>26265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Referred a Friend</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Referrals</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure in Months</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Offer</th>\n",
       "      <td>No Offer</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Offer C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phone Service</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <td>0.0</td>\n",
       "      <td>48.85</td>\n",
       "      <td>11.33</td>\n",
       "      <td>19.76</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Lines</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Service</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Type</th>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "      <td>Fiber Optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg Monthly GB Download</th>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Security</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online Backup</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Device Protection Plan</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium Tech Support</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming TV</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming Movies</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Streaming Music</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unlimited Data</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Month-to-Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paperless Billing</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment Method</th>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly Charge</th>\n",
       "      <td>39.65</td>\n",
       "      <td>80.65</td>\n",
       "      <td>95.45</td>\n",
       "      <td>98.5</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Charges</th>\n",
       "      <td>39.65</td>\n",
       "      <td>633.3</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>2514.5</td>\n",
       "      <td>2868.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Refunds</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.61</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <td>0.0</td>\n",
       "      <td>390.8</td>\n",
       "      <td>203.94</td>\n",
       "      <td>494.0</td>\n",
       "      <td>234.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Revenue</th>\n",
       "      <td>59.65</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>1910.88</td>\n",
       "      <td>2995.07</td>\n",
       "      <td>3102.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Status</th>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Churned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Score</th>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>88</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLTV</th>\n",
       "      <td>5433</td>\n",
       "      <td>5302</td>\n",
       "      <td>3179</td>\n",
       "      <td>5337</td>\n",
       "      <td>2793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Category</th>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Dissatisfaction</td>\n",
       "      <td>Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn Reason</th>\n",
       "      <td>Competitor offered more data</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "      <td>Limited range of services</td>\n",
       "      <td>Extra data charges</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              0  \\\n",
       "Gender                                                     Male   \n",
       "Age                                                          78   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                      No   \n",
       "Dependents                                                   No   \n",
       "Number of Dependents                                          0   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90022   \n",
       "Latitude                                               34.02381   \n",
       "Longitude                                           -118.156582   \n",
       "Population                                                68701   \n",
       "Referred a Friend                                            No   \n",
       "Number of Referrals                                           0   \n",
       "Tenure in Months                                              1   \n",
       "Offer                                                  No Offer   \n",
       "Phone Service                                                No   \n",
       "Avg Monthly Long Distance Charges                           0.0   \n",
       "Multiple Lines                                               No   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                               DSL   \n",
       "Avg Monthly GB Download                                       8   \n",
       "Online Security                                              No   \n",
       "Online Backup                                                No   \n",
       "Device Protection Plan                                      Yes   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                 No   \n",
       "Streaming Movies                                            Yes   \n",
       "Streaming Music                                              No   \n",
       "Unlimited Data                                               No   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                  Bank Withdrawal   \n",
       "Monthly Charge                                            39.65   \n",
       "Total Charges                                             39.65   \n",
       "Total Refunds                                               0.0   \n",
       "Total Extra Data Charges                                     20   \n",
       "Total Long Distance Charges                                 0.0   \n",
       "Total Revenue                                             59.65   \n",
       "Satisfaction Score                                            3   \n",
       "Customer Status                                         Churned   \n",
       "Churn Score                                                  91   \n",
       "CLTV                                                       5433   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor offered more data   \n",
       "\n",
       "                                                              1  \\\n",
       "Gender                                                   Female   \n",
       "Age                                                          74   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                     Yes   \n",
       "Dependents                                                  Yes   \n",
       "Number of Dependents                                          1   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90063   \n",
       "Latitude                                              34.044271   \n",
       "Longitude                                           -118.185237   \n",
       "Population                                                55668   \n",
       "Referred a Friend                                           Yes   \n",
       "Number of Referrals                                           1   \n",
       "Tenure in Months                                              8   \n",
       "Offer                                                   Offer E   \n",
       "Phone Service                                               Yes   \n",
       "Avg Monthly Long Distance Charges                         48.85   \n",
       "Multiple Lines                                              Yes   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                       Fiber Optic   \n",
       "Avg Monthly GB Download                                      17   \n",
       "Online Security                                              No   \n",
       "Online Backup                                               Yes   \n",
       "Device Protection Plan                                       No   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                 No   \n",
       "Streaming Movies                                             No   \n",
       "Streaming Music                                              No   \n",
       "Unlimited Data                                              Yes   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                      Credit Card   \n",
       "Monthly Charge                                            80.65   \n",
       "Total Charges                                             633.3   \n",
       "Total Refunds                                               0.0   \n",
       "Total Extra Data Charges                                      0   \n",
       "Total Long Distance Charges                               390.8   \n",
       "Total Revenue                                            1024.1   \n",
       "Satisfaction Score                                            3   \n",
       "Customer Status                                         Churned   \n",
       "Churn Score                                                  69   \n",
       "CLTV                                                       5302   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor made better offer   \n",
       "\n",
       "                                                              2  \\\n",
       "Gender                                                     Male   \n",
       "Age                                                          71   \n",
       "Under 30                                                     No   \n",
       "Senior Citizen                                              Yes   \n",
       "Married                                                      No   \n",
       "Dependents                                                  Yes   \n",
       "Number of Dependents                                          3   \n",
       "City                                                Los Angeles   \n",
       "Zip Code                                                  90065   \n",
       "Latitude                                              34.108833   \n",
       "Longitude                                           -118.229715   \n",
       "Population                                                47534   \n",
       "Referred a Friend                                            No   \n",
       "Number of Referrals                                           0   \n",
       "Tenure in Months                                             18   \n",
       "Offer                                                   Offer D   \n",
       "Phone Service                                               Yes   \n",
       "Avg Monthly Long Distance Charges                         11.33   \n",
       "Multiple Lines                                              Yes   \n",
       "Internet Service                                            Yes   \n",
       "Internet Type                                       Fiber Optic   \n",
       "Avg Monthly GB Download                                      52   \n",
       "Online Security                                              No   \n",
       "Online Backup                                                No   \n",
       "Device Protection Plan                                       No   \n",
       "Premium Tech Support                                         No   \n",
       "Streaming TV                                                Yes   \n",
       "Streaming Movies                                            Yes   \n",
       "Streaming Music                                             Yes   \n",
       "Unlimited Data                                              Yes   \n",
       "Contract                                         Month-to-Month   \n",
       "Paperless Billing                                           Yes   \n",
       "Payment Method                                  Bank Withdrawal   \n",
       "Monthly Charge                                            95.45   \n",
       "Total Charges                                           1752.55   \n",
       "Total Refunds                                             45.61   \n",
       "Total Extra Data Charges                                      0   \n",
       "Total Long Distance Charges                              203.94   \n",
       "Total Revenue                                           1910.88   \n",
       "Satisfaction Score                                            2   \n",
       "Customer Status                                         Churned   \n",
       "Churn Score                                                  81   \n",
       "CLTV                                                       3179   \n",
       "Churn Category                                       Competitor   \n",
       "Churn Reason                       Competitor made better offer   \n",
       "\n",
       "                                                           3  \\\n",
       "Gender                                                Female   \n",
       "Age                                                       78   \n",
       "Under 30                                                  No   \n",
       "Senior Citizen                                           Yes   \n",
       "Married                                                  Yes   \n",
       "Dependents                                               Yes   \n",
       "Number of Dependents                                       1   \n",
       "City                                               Inglewood   \n",
       "Zip Code                                               90303   \n",
       "Latitude                                           33.936291   \n",
       "Longitude                                        -118.332639   \n",
       "Population                                             27778   \n",
       "Referred a Friend                                        Yes   \n",
       "Number of Referrals                                        1   \n",
       "Tenure in Months                                          25   \n",
       "Offer                                                Offer C   \n",
       "Phone Service                                            Yes   \n",
       "Avg Monthly Long Distance Charges                      19.76   \n",
       "Multiple Lines                                            No   \n",
       "Internet Service                                         Yes   \n",
       "Internet Type                                    Fiber Optic   \n",
       "Avg Monthly GB Download                                   12   \n",
       "Online Security                                           No   \n",
       "Online Backup                                            Yes   \n",
       "Device Protection Plan                                   Yes   \n",
       "Premium Tech Support                                      No   \n",
       "Streaming TV                                             Yes   \n",
       "Streaming Movies                                         Yes   \n",
       "Streaming Music                                           No   \n",
       "Unlimited Data                                           Yes   \n",
       "Contract                                      Month-to-Month   \n",
       "Paperless Billing                                        Yes   \n",
       "Payment Method                               Bank Withdrawal   \n",
       "Monthly Charge                                          98.5   \n",
       "Total Charges                                         2514.5   \n",
       "Total Refunds                                          13.43   \n",
       "Total Extra Data Charges                                   0   \n",
       "Total Long Distance Charges                            494.0   \n",
       "Total Revenue                                        2995.07   \n",
       "Satisfaction Score                                         2   \n",
       "Customer Status                                      Churned   \n",
       "Churn Score                                               88   \n",
       "CLTV                                                    5337   \n",
       "Churn Category                               Dissatisfaction   \n",
       "Churn Reason                       Limited range of services   \n",
       "\n",
       "                                                    4  \n",
       "Gender                                         Female  \n",
       "Age                                                80  \n",
       "Under 30                                           No  \n",
       "Senior Citizen                                    Yes  \n",
       "Married                                           Yes  \n",
       "Dependents                                        Yes  \n",
       "Number of Dependents                                1  \n",
       "City                                         Whittier  \n",
       "Zip Code                                        90602  \n",
       "Latitude                                    33.972119  \n",
       "Longitude                                 -118.020188  \n",
       "Population                                      26265  \n",
       "Referred a Friend                                 Yes  \n",
       "Number of Referrals                                 1  \n",
       "Tenure in Months                                   37  \n",
       "Offer                                         Offer C  \n",
       "Phone Service                                     Yes  \n",
       "Avg Monthly Long Distance Charges                6.33  \n",
       "Multiple Lines                                    Yes  \n",
       "Internet Service                                  Yes  \n",
       "Internet Type                             Fiber Optic  \n",
       "Avg Monthly GB Download                            14  \n",
       "Online Security                                    No  \n",
       "Online Backup                                      No  \n",
       "Device Protection Plan                             No  \n",
       "Premium Tech Support                               No  \n",
       "Streaming TV                                       No  \n",
       "Streaming Movies                                   No  \n",
       "Streaming Music                                    No  \n",
       "Unlimited Data                                    Yes  \n",
       "Contract                               Month-to-Month  \n",
       "Paperless Billing                                 Yes  \n",
       "Payment Method                        Bank Withdrawal  \n",
       "Monthly Charge                                   76.5  \n",
       "Total Charges                                 2868.15  \n",
       "Total Refunds                                     0.0  \n",
       "Total Extra Data Charges                            0  \n",
       "Total Long Distance Charges                    234.21  \n",
       "Total Revenue                                 3102.36  \n",
       "Satisfaction Score                                  2  \n",
       "Customer Status                               Churned  \n",
       "Churn Score                                        67  \n",
       "CLTV                                             2793  \n",
       "Churn Category                                  Price  \n",
       "Churn Reason                       Extra data charges  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85df52e5",
   "metadata": {
    "id": "85df52e5"
   },
   "source": [
    "**Numeric Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d48aeaa4",
   "metadata": {
    "id": "d48aeaa4",
    "outputId": "3472d509-3613-408a-b2e0-7e04e3ba694a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of Numerical Features : 19\n"
     ]
    }
   ],
   "source": [
    "num_features = [feature for feature in X.columns if X[feature].dtype != 'O']\n",
    "print('Num of Numerical Features :', len(num_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1107060",
   "metadata": {
    "id": "e1107060"
   },
   "source": [
    "**Categorical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff997805",
   "metadata": {
    "id": "ff997805",
    "outputId": "3dd63e00-7af0-48de-d7bb-5d1f51c557de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of Categorical Features : 26\n"
     ]
    }
   ],
   "source": [
    "cat_features = [feature for feature in X.columns if X[feature].dtype == 'O']\n",
    "print('Num of Categorical Features :', len(cat_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2260600",
   "metadata": {
    "id": "f2260600"
   },
   "source": [
    "**Manual encoding target column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a9d7c95",
   "metadata": {
    "id": "7a9d7c95"
   },
   "outputs": [],
   "source": [
    "# If the target column has No it is encoded as 1 others as 0\n",
    "y= np.where(y=='No', 1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "398fad76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2d817d",
   "metadata": {
    "id": "ca2d817d"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ca6ac67f",
   "metadata": {
    "id": "ca6ac67f"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1786c0ad",
   "metadata": {
    "id": "1786c0ad"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "51a3853d",
   "metadata": {
    "id": "51a3853d"
   },
   "source": [
    "## Feature Encoding and Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29dbaf2d",
   "metadata": {
    "id": "29dbaf2d"
   },
   "source": [
    "**Label Encoding**\n",
    "* Label Encoding is a technique used to convert categorical data into numerical values, making it suitable for machine learning models. Each unique category in a column is assigned a unique integer value.\n",
    "\n",
    "**Power Transformer**\n",
    "* Power transforms are a technique for transforming numerical input or output variables to have a Gaussian or more-Gaussian-like probability distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ee3672c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Initialize LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "# cols = ['Offer','Churn Reason','Churn Category','City']\n",
    "cols = cat_features\n",
    "\n",
    "# Apply LabelEncoder to the columns\n",
    "for col in cols:\n",
    "    X[col] = label_encoder.fit_transform(X[col])\n",
    "\n",
    "# View transformed DataFrame\n",
    "# print(df[\"Churn Reason\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd46009c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x4000 with 45 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 40))\n",
    "for i, col in enumerate(X):\n",
    "    plt.subplot(23, 2, i+1)\n",
    "    sns.histplot(x=X[col], color='indianred')\n",
    "    plt.xlabel(col)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2524379",
   "metadata": {},
   "source": [
    "### Feature Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad85035d",
   "metadata": {},
   "source": [
    "**What is Skewness ?**\n",
    "\n",
    "* Skewness refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution. A normal distribution has a skew of zero\n",
    "\n",
    "- Positiviely Skewed : > 0\n",
    "- Negative Skew : < 0\n",
    "- We can handle outliers and then check the skewness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f6b21a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                              -0.019031\n",
       "Age                                  0.162186\n",
       "Under 30                             1.508777\n",
       "Senior Citizen                       1.833633\n",
       "Married                              0.067922\n",
       "Dependents                           1.276686\n",
       "Number of Dependents                 2.109932\n",
       "City                                -0.214024\n",
       "Zip Code                            -0.209615\n",
       "Latitude                             0.314804\n",
       "Longitude                           -0.091932\n",
       "Population                           0.912854\n",
       "Referred a Friend                    0.170753\n",
       "Number of Referrals                  1.446060\n",
       "Tenure in Months                     0.240543\n",
       "Offer                                0.917537\n",
       "Phone Service                       -2.727153\n",
       "Avg Monthly Long Distance Charges    0.049176\n",
       "Multiple Lines                       0.316610\n",
       "Internet Service                    -1.375769\n",
       "Internet Type                       -1.153788\n",
       "Avg Monthly GB Download              1.216584\n",
       "Online Security                      0.943722\n",
       "Online Backup                        0.652817\n",
       "Device Protection Plan               0.657450\n",
       "Premium Tech Support                 0.924630\n",
       "Streaming TV                         0.475581\n",
       "Streaming Movies                     0.460199\n",
       "Streaming Music                      0.614135\n",
       "Unlimited Data                      -0.741196\n",
       "Contract                             0.489779\n",
       "Paperless Billing                   -0.375396\n",
       "Payment Method                       0.762882\n",
       "Monthly Charge                      -0.220524\n",
       "Total Charges                        0.963791\n",
       "Total Refunds                        4.328517\n",
       "Total Extra Data Charges             4.091209\n",
       "Total Long Distance Charges          1.238282\n",
       "Total Revenue                        0.919410\n",
       "Satisfaction Score                  -0.367401\n",
       "Customer Status                     -0.883315\n",
       "Churn Score                         -0.156852\n",
       "CLTV                                -0.311602\n",
       "Churn Category                      -1.647483\n",
       "Churn Reason                        -1.551194\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.skew(axis=0, skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e7197dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "pt = PowerTransformer(method='yeo-johnson')\n",
    "X_clean = pt.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d791f12e",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>Referred a Friend</th>\n",
       "      <th>Number of Referrals</th>\n",
       "      <th>Tenure in Months</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Phone Service</th>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <th>Multiple Lines</th>\n",
       "      <th>Internet Service</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Avg Monthly GB Download</th>\n",
       "      <th>Online Security</th>\n",
       "      <th>Online Backup</th>\n",
       "      <th>Device Protection Plan</th>\n",
       "      <th>Premium Tech Support</th>\n",
       "      <th>Streaming TV</th>\n",
       "      <th>Streaming Movies</th>\n",
       "      <th>Streaming Music</th>\n",
       "      <th>Unlimited Data</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Total Refunds</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>554</td>\n",
       "      <td>90022</td>\n",
       "      <td>34.023810</td>\n",
       "      <td>-118.156582</td>\n",
       "      <td>68701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>39.65</td>\n",
       "      <td>39.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554</td>\n",
       "      <td>90063</td>\n",
       "      <td>34.044271</td>\n",
       "      <td>-118.185237</td>\n",
       "      <td>55668</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>48.85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.65</td>\n",
       "      <td>633.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>390.80</td>\n",
       "      <td>1024.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>5302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>554</td>\n",
       "      <td>90065</td>\n",
       "      <td>34.108833</td>\n",
       "      <td>-118.229715</td>\n",
       "      <td>47534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>45.61</td>\n",
       "      <td>0</td>\n",
       "      <td>203.94</td>\n",
       "      <td>1910.88</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>3179</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>448</td>\n",
       "      <td>90303</td>\n",
       "      <td>33.936291</td>\n",
       "      <td>-118.332639</td>\n",
       "      <td>27778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2514.50</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0</td>\n",
       "      <td>494.00</td>\n",
       "      <td>2995.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>5337</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1072</td>\n",
       "      <td>90602</td>\n",
       "      <td>33.972119</td>\n",
       "      <td>-118.020188</td>\n",
       "      <td>26265</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76.50</td>\n",
       "      <td>2868.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>234.21</td>\n",
       "      <td>3102.36</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>2793</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>514</td>\n",
       "      <td>92285</td>\n",
       "      <td>34.341737</td>\n",
       "      <td>-116.539416</td>\n",
       "      <td>2182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.15</td>\n",
       "      <td>1419.40</td>\n",
       "      <td>19.31</td>\n",
       "      <td>0</td>\n",
       "      <td>1639.44</td>\n",
       "      <td>3039.53</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>5306</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>92301</td>\n",
       "      <td>34.667815</td>\n",
       "      <td>-117.536183</td>\n",
       "      <td>18980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>48.23</td>\n",
       "      <td>0</td>\n",
       "      <td>865.20</td>\n",
       "      <td>2807.47</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2140</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>92304</td>\n",
       "      <td>34.559882</td>\n",
       "      <td>-115.637164</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.66</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>45.38</td>\n",
       "      <td>0</td>\n",
       "      <td>2135.52</td>\n",
       "      <td>9453.04</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>5560</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>92305</td>\n",
       "      <td>34.167800</td>\n",
       "      <td>-116.864330</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>319.21</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2793</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>92308</td>\n",
       "      <td>34.424926</td>\n",
       "      <td>-117.184503</td>\n",
       "      <td>28819</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2043.36</td>\n",
       "      <td>8887.86</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>5097</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Age  Under 30  Senior Citizen  Married  Dependents  \\\n",
       "0          1   78         0               1        0           0   \n",
       "1          0   74         0               1        1           1   \n",
       "2          1   71         0               1        0           1   \n",
       "3          0   78         0               1        1           1   \n",
       "4          0   80         0               1        1           1   \n",
       "...      ...  ...       ...             ...      ...         ...   \n",
       "7038       0   30         0               0        0           0   \n",
       "7039       1   38         0               0        1           1   \n",
       "7040       0   30         0               0        1           1   \n",
       "7041       0   32         0               0        1           1   \n",
       "7042       1   44         0               0        0           0   \n",
       "\n",
       "      Number of Dependents  City  Zip Code   Latitude   Longitude  Population  \\\n",
       "0                        0   554     90022  34.023810 -118.156582       68701   \n",
       "1                        1   554     90063  34.044271 -118.185237       55668   \n",
       "2                        3   554     90065  34.108833 -118.229715       47534   \n",
       "3                        1   448     90303  33.936291 -118.332639       27778   \n",
       "4                        1  1072     90602  33.972119 -118.020188       26265   \n",
       "...                    ...   ...       ...        ...         ...         ...   \n",
       "7038                     0   514     92285  34.341737 -116.539416        2182   \n",
       "7039                     2     2     92301  34.667815 -117.536183       18980   \n",
       "7040                     2    22     92304  34.559882 -115.637164          42   \n",
       "7041                     2    26     92305  34.167800 -116.864330         301   \n",
       "7042                     0    32     92308  34.424926 -117.184503       28819   \n",
       "\n",
       "      Referred a Friend  Number of Referrals  Tenure in Months  Offer  \\\n",
       "0                     0                    0                 1      0   \n",
       "1                     1                    1                 8      5   \n",
       "2                     0                    0                18      4   \n",
       "3                     1                    1                25      3   \n",
       "4                     1                    1                37      3   \n",
       "...                 ...                  ...               ...    ...   \n",
       "7038                  0                    0                72      0   \n",
       "7039                  1                    1                24      3   \n",
       "7040                  1                    4                72      0   \n",
       "7041                  1                    1                11      0   \n",
       "7042                  0                    0                66      0   \n",
       "\n",
       "      Phone Service  Avg Monthly Long Distance Charges  Multiple Lines  \\\n",
       "0                 0                               0.00               0   \n",
       "1                 1                              48.85               1   \n",
       "2                 1                              11.33               1   \n",
       "3                 1                              19.76               0   \n",
       "4                 1                               6.33               1   \n",
       "...             ...                                ...             ...   \n",
       "7038              1                              22.77               0   \n",
       "7039              1                              36.05               1   \n",
       "7040              1                              29.66               1   \n",
       "7041              0                               0.00               0   \n",
       "7042              1                              30.96               0   \n",
       "\n",
       "      Internet Service  Internet Type  Avg Monthly GB Download  \\\n",
       "0                    1              1                        8   \n",
       "1                    1              2                       17   \n",
       "2                    1              2                       52   \n",
       "3                    1              2                       12   \n",
       "4                    1              2                       14   \n",
       "...                ...            ...                      ...   \n",
       "7038                 0              2                        0   \n",
       "7039                 1              0                       24   \n",
       "7040                 1              2                       59   \n",
       "7041                 1              1                       17   \n",
       "7042                 1              2                       11   \n",
       "\n",
       "      Online Security  Online Backup  Device Protection Plan  \\\n",
       "0                   0              0                       1   \n",
       "1                   0              1                       0   \n",
       "2                   0              0                       0   \n",
       "3                   0              1                       1   \n",
       "4                   0              0                       0   \n",
       "...               ...            ...                     ...   \n",
       "7038                0              0                       0   \n",
       "7039                1              0                       1   \n",
       "7040                0              1                       1   \n",
       "7041                1              0                       0   \n",
       "7042                1              0                       1   \n",
       "\n",
       "      Premium Tech Support  Streaming TV  Streaming Movies  Streaming Music  \\\n",
       "0                        0             0                 1                0   \n",
       "1                        0             0                 0                0   \n",
       "2                        0             1                 1                1   \n",
       "3                        0             1                 1                0   \n",
       "4                        0             0                 0                0   \n",
       "...                    ...           ...               ...              ...   \n",
       "7038                     0             0                 0                0   \n",
       "7039                     1             1                 1                1   \n",
       "7040                     0             1                 1                1   \n",
       "7041                     0             0                 0                0   \n",
       "7042                     1             1                 1                1   \n",
       "\n",
       "      Unlimited Data  Contract  Paperless Billing  Payment Method  \\\n",
       "0                  0         0                  1               0   \n",
       "1                  1         0                  1               1   \n",
       "2                  1         0                  1               0   \n",
       "3                  1         0                  1               0   \n",
       "4                  1         0                  1               0   \n",
       "...              ...       ...                ...             ...   \n",
       "7038               0         2                  1               0   \n",
       "7039               1         1                  1               2   \n",
       "7040               1         1                  1               1   \n",
       "7041               1         0                  1               0   \n",
       "7042               1         2                  1               0   \n",
       "\n",
       "      Monthly Charge  Total Charges  Total Refunds  Total Extra Data Charges  \\\n",
       "0              39.65          39.65           0.00                        20   \n",
       "1              80.65         633.30           0.00                         0   \n",
       "2              95.45        1752.55          45.61                         0   \n",
       "3              98.50        2514.50          13.43                         0   \n",
       "4              76.50        2868.15           0.00                         0   \n",
       "...              ...            ...            ...                       ...   \n",
       "7038           21.15        1419.40          19.31                         0   \n",
       "7039           84.80        1990.50          48.23                         0   \n",
       "7040          103.20        7362.90          45.38                         0   \n",
       "7041           29.60         346.45          27.24                         0   \n",
       "7042          105.65        6844.50           0.00                         0   \n",
       "\n",
       "      Total Long Distance Charges  Total Revenue  Satisfaction Score  \\\n",
       "0                            0.00          59.65                   3   \n",
       "1                          390.80        1024.10                   3   \n",
       "2                          203.94        1910.88                   2   \n",
       "3                          494.00        2995.07                   2   \n",
       "4                          234.21        3102.36                   2   \n",
       "...                           ...            ...                 ...   \n",
       "7038                      1639.44        3039.53                   5   \n",
       "7039                       865.20        2807.47                   3   \n",
       "7040                      2135.52        9453.04                   4   \n",
       "7041                         0.00         319.21                   4   \n",
       "7042                      2043.36        8887.86                   4   \n",
       "\n",
       "      Customer Status  Churn Score  CLTV  Churn Category  Churn Reason  \n",
       "0                   0           91  5433               1             5  \n",
       "1                   0           69  5302               1             3  \n",
       "2                   0           81  3179               1             3  \n",
       "3                   0           88  5337               2            11  \n",
       "4                   0           67  2793               4             8  \n",
       "...               ...          ...   ...             ...           ...  \n",
       "7038                2           45  5306               3            15  \n",
       "7039                2           59  2140               3            15  \n",
       "7040                2           71  5560               3            15  \n",
       "7041                2           59  2793               3            15  \n",
       "7042                2           38  5097               3            15  \n",
       "\n",
       "[7043 rows x 45 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2c0485",
   "metadata": {
    "id": "8a2c0485"
   },
   "source": [
    "### Train Test Split\n",
    "- The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.\n",
    "\n",
    "- It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0f5858e",
   "metadata": {
    "id": "e0f5858e",
    "outputId": "d91dcb53-e0ae-4571-ef70-745f4c2ed1a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5634, 45), (1409, 45))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "# separate dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_clean,y,test_size=0.2,random_state=42)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4cdb60f1",
   "metadata": {
    "id": "4cdb60f1"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report,ConfusionMatrixDisplay, \\\n",
    "                            precision_score, recall_score, f1_score, roc_auc_score,roc_curve \n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b4da40e7",
   "metadata": {
    "id": "b4da40e7"
   },
   "outputs": [],
   "source": [
    "def evaluate_clf(true, predicted):\n",
    "    acc = accuracy_score(true, predicted) # Calculate Accuracy\n",
    "    f1 = f1_score(true, predicted) # Calculate F1-score\n",
    "    precision = precision_score(true, predicted) # Calculate Precision\n",
    "    recall = recall_score(true, predicted)  # Calculate Recall\n",
    "    roc_auc = roc_auc_score(true, predicted) #Calculate Roc\n",
    "    return acc, f1 , precision, recall, roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91cebd1a",
   "metadata": {
    "id": "91cebd1a",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "     \"K-Neighbors Classifier\": KNeighborsClassifier(),\n",
    "     \"Support Vector Classifier\": SVC()\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9aa69756",
   "metadata": {
    "id": "9aa69756"
   },
   "outputs": [],
   "source": [
    "# Create a function which can evaluate models and return a report \n",
    "def evaluate_models(X, y, models):\n",
    "    '''\n",
    "    This function takes in X and y and models dictionary as input\n",
    "    It splits the data into Train Test split\n",
    "    Iterates through the given model dictionary and evaluates the metrics\n",
    "    Returns: Dataframe which contains report of all models metrics with cost\n",
    "    '''\n",
    "    # separate dataset into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "    \n",
    "    models_list = []\n",
    "    accuracy_list = []\n",
    "    auc= []\n",
    "    \n",
    "    for i in range(len(list(models))):\n",
    "        model = list(models.values())[i]\n",
    "        model.fit(X_train, y_train) # Train model\n",
    "\n",
    "        # Make predictions\n",
    "        y_train_pred = model.predict(X_train)\n",
    "        y_test_pred = model.predict(X_test)\n",
    "\n",
    "        # Training set performance\n",
    "        model_train_accuracy, model_train_f1,model_train_precision,\\\n",
    "        model_train_recall,model_train_rocauc_score=evaluate_clf(y_train ,y_train_pred)\n",
    "\n",
    "\n",
    "        # Test set performance\n",
    "        model_test_accuracy,model_test_f1,model_test_precision,\\\n",
    "        model_test_recall,model_test_rocauc_score=evaluate_clf(y_test, y_test_pred)\n",
    "\n",
    "        print(list(models.keys())[i])\n",
    "        models_list.append(list(models.keys())[i])\n",
    "\n",
    "        print('Model performance for Training set')\n",
    "        print(\"- Accuracy: {:.4f}\".format(model_train_accuracy))\n",
    "        print('- F1 score: {:.4f}'.format(model_train_f1)) \n",
    "        print('- Precision: {:.4f}'.format(model_train_precision))\n",
    "        print('- Recall: {:.4f}'.format(model_train_recall))\n",
    "        print('- Roc Auc Score: {:.4f}'.format(model_train_rocauc_score))\n",
    "\n",
    "        print('----------------------------------')\n",
    "\n",
    "        print('Model performance for Test set')\n",
    "        print('- Accuracy: {:.4f}'.format(model_test_accuracy))\n",
    "        accuracy_list.append(model_test_accuracy)\n",
    "        print('- F1 score: {:.4f}'.format(model_test_f1))\n",
    "        print('- Precision: {:.4f}'.format(model_test_precision))\n",
    "        print('- Recall: {:.4f}'.format(model_test_recall))\n",
    "        print('- Roc Auc Score: {:.4f}'.format(model_test_rocauc_score))\n",
    "        auc.append(model_test_rocauc_score)\n",
    "        print('='*35)\n",
    "        print('\\n')\n",
    "        \n",
    "    report=pd.DataFrame(list(zip(models_list, accuracy_list)), columns=['Model Name', 'Accuracy']).sort_values(by=['Accuracy'], ascending=False)\n",
    "        \n",
    "    return report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e010280",
   "metadata": {
    "id": "0e010280"
   },
   "source": [
    "## Model Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b08c217d",
   "metadata": {
    "id": "b08c217d",
    "outputId": "f8290c30-975a-4e44-80bd-9e120036e956",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Model performance for Training set\n",
      "- Accuracy: 0.8923\n",
      "- F1 score: 0.9276\n",
      "- Precision: 0.9212\n",
      "- Recall: 0.9342\n",
      "- Roc Auc Score: 0.8538\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 0.8886\n",
      "- F1 score: 0.9229\n",
      "- Precision: 0.9144\n",
      "- Recall: 0.9316\n",
      "- Roc Auc Score: 0.8558\n",
      "===================================\n",
      "\n",
      "\n",
      "K-Neighbors Classifier\n",
      "Model performance for Training set\n",
      "- Accuracy: 0.7980\n",
      "- F1 score: 0.8709\n",
      "- Precision: 0.8257\n",
      "- Recall: 0.9212\n",
      "- Roc Auc Score: 0.6849\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 0.6934\n",
      "- F1 score: 0.8024\n",
      "- Precision: 0.7451\n",
      "- Recall: 0.8692\n",
      "- Roc Auc Score: 0.5596\n",
      "===================================\n",
      "\n",
      "\n",
      "Support Vector Classifier\n",
      "Model performance for Training set\n",
      "- Accuracy: 0.7393\n",
      "- F1 score: 0.8501\n",
      "- Precision: 0.7393\n",
      "- Recall: 1.0000\n",
      "- Roc Auc Score: 0.5000\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 0.7161\n",
      "- F1 score: 0.8346\n",
      "- Precision: 0.7161\n",
      "- Recall: 1.0000\n",
      "- Roc Auc Score: 0.5000\n",
      "===================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_model_report =evaluate_models(X=X, y=y, models=models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac46818",
   "metadata": {
    "id": "8ac46818"
   },
   "source": [
    "**Results of All Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd686f5e",
   "metadata": {
    "id": "fd686f5e",
    "outputId": "bf5b1cc5-5eab-4a63-c765-0a23cdc94ab0"
   },
   "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>Model Name</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.888573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classifier</td>\n",
       "      <td>0.716111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K-Neighbors Classifier</td>\n",
       "      <td>0.693400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model Name  Accuracy\n",
       "0        Logistic Regression  0.888573\n",
       "2  Support Vector Classifier  0.716111\n",
       "1     K-Neighbors Classifier  0.693400"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f86e1dd7",
   "metadata": {},
   "source": [
    "### Hyper-parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c381c176",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9af0977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize parameters for Hyperparameter tuning\n",
    "logreg_params = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100],            # Regularization strength\n",
    "    'solver': ['lbfgs', 'liblinear', 'saga'],        # Solver algorithm\n",
    "    'max_iter': [100, 200, 300]                      # Maximum iterations for convergence\n",
    "}\n",
    "\n",
    "knn_params = {\n",
    "    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],    # Algorithm to compute nearest neighbors\n",
    "    'weights': ['uniform', 'distance'],                       # Weight function used in prediction\n",
    "    'n_neighbors': [3, 4, 5, 7, 9],                          # Number of neighbors to use\n",
    "    'p': [1, 2]                                              # Power parameter for the Minkowski distance (1=Manhattan, 2=Euclidean)\n",
    "}\n",
    "\n",
    "svc_params = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100],            # Regularization parameter\n",
    "    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],  # Kernel type to be used in the algorithm\n",
    "    'gamma': ['scale', 'auto'],                      # Kernel coefficient for 'rbf', 'poly', 'sigmoid'\n",
    "    'degree': [3, 4, 5]                               # Degree for the polynomial kernel function (used only for 'poly' kernel)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "79770d68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ............C=0.001, max_iter=100, solver=liblinear; total time=   0.0s\n",
      "[CV] END ............C=0.001, max_iter=100, solver=liblinear; total time=   0.0s\n",
      "[CV] END ............C=0.001, max_iter=100, solver=liblinear; total time=   0.0s\n",
      "[CV] END .................C=0.001, max_iter=100, solver=saga; total time=   0.1s\n",
      "[CV] END .................C=0.001, max_iter=100, solver=saga; total time=   0.1s\n",
      "[CV] END ................C=0.001, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.001, max_iter=100, solver=saga; total time=   0.1s\n",
      "[CV] END ................C=0.001, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=0.001, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=0.001, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=0.001, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ............C=0.001, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ............C=0.001, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ............C=0.001, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END .................C=0.001, max_iter=200, solver=saga; total time=   0.1s\n",
      "[CV] END ................C=0.001, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=0.001, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=0.001, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.001, max_iter=200, solver=saga; total time=   0.2s\n",
      "[CV] END ............C=0.001, max_iter=300, solver=liblinear; total time=   0.0s\n",
      "[CV] END .................C=0.001, max_iter=200, solver=saga; total time=   0.2s\n",
      "[CV] END ................C=0.001, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ............C=0.001, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ............C=0.001, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END .................C=0.001, max_iter=300, solver=saga; total time=   0.1s\n",
      "[CV] END .................C=0.01, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.01, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.01, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .............C=0.01, max_iter=100, solver=liblinear; total time=   0.0s\n",
      "[CV] END .................C=0.001, max_iter=300, solver=saga; total time=   0.1s\n",
      "[CV] END .................C=0.001, max_iter=300, solver=saga; total time=   0.2s\n",
      "[CV] END .............C=0.01, max_iter=100, solver=liblinear; total time=   0.0s\n",
      "[CV] END .............C=0.01, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END .................C=0.01, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.01, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=0.01, max_iter=100, solver=saga; total time=   0.1s\n",
      "[CV] END ..................C=0.01, max_iter=100, solver=saga; total time=   0.2s\n",
      "[CV] END .................C=0.01, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=0.01, max_iter=100, solver=saga; total time=   0.2s\n",
      "[CV] END .............C=0.01, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END .............C=0.01, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END .............C=0.01, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END .................C=0.01, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .................C=0.01, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .............C=0.01, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..................C=0.01, max_iter=200, solver=saga; total time=   0.1s\n",
      "[CV] END ..................C=0.01, max_iter=200, solver=saga; total time=   0.2s\n",
      "[CV] END .................C=0.01, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .............C=0.01, max_iter=300, solver=liblinear; total time=   0.0s\n",
      "[CV] END ..................C=0.01, max_iter=200, solver=saga; total time=   0.2s\n",
      "[CV] END ..................C=0.1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .............C=0.01, max_iter=300, solver=liblinear; total time=   0.0s\n",
      "[CV] END ..................C=0.1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=0.1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=0.1, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=0.1, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..................C=0.01, max_iter=300, solver=saga; total time=   0.1s\n",
      "[CV] END ..................C=0.1, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=0.01, max_iter=300, solver=saga; total time=   0.2s\n",
      "[CV] END ..................C=0.1, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=0.1, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=0.1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..................C=0.01, max_iter=300, solver=saga; total time=   0.1s\n",
      "[CV] END ..............C=0.1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=0.1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..................C=0.1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=0.1, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..................C=0.1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=0.1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=0.1, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=0.1, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...................C=0.1, max_iter=300, solver=saga; total time=   0.2s\n",
      "[CV] END ....................C=1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ....................C=1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=0.1, max_iter=100, solver=saga; total time=   0.7s\n",
      "[CV] END ....................C=1, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=0.1, max_iter=300, solver=saga; total time=   0.4s\n",
      "[CV] END ................C=1, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ................C=1, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...................C=0.1, max_iter=200, solver=saga; total time=   0.7s\n",
      "[CV] END ................C=1, max_iter=100, solver=liblinear; total time=   0.1s[CV] END ...................C=0.1, max_iter=300, solver=saga; total time=   0.2s\n",
      "\n",
      "[CV] END ...................C=0.1, max_iter=100, solver=saga; total time=   0.3s\n",
      "[CV] END ...................C=0.1, max_iter=200, solver=saga; total time=   0.3s\n",
      "[CV] END ...................C=0.1, max_iter=100, solver=saga; total time=   0.3s\n",
      "[CV] END ..................C=0.1, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ....................C=1, max_iter=200, solver=lbfgs; total time=   0.1s\n",
      "[CV] END ................C=1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...................C=0.1, max_iter=200, solver=saga; total time=   0.3s\n",
      "[CV] END ................C=1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ................C=1, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ....................C=1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END .....................C=1, max_iter=100, solver=saga; total time=   0.8s\n",
      "[CV] END ....................C=1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ................C=1, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END .....................C=1, max_iter=100, solver=saga; total time=   0.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ................C=1, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ................C=1, max_iter=300, solver=liblinear; total time=   0.2s\n",
      "[CV] END ...................C=10, max_iter=100, solver=lbfgs; total time=   0.1s\n",
      "[CV] END ...................C=10, max_iter=100, solver=lbfgs; total time=   0.1s\n",
      "[CV] END ...................C=10, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...............C=10, max_iter=100, solver=liblinear; total time=   0.2s\n",
      "[CV] END .....................C=1, max_iter=100, solver=saga; total time=   0.8s\n",
      "[CV] END ....................C=1, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...............C=10, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ....................C=1, max_iter=200, solver=lbfgs; total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...............C=10, max_iter=100, solver=liblinear; total time=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END .....................C=1, max_iter=200, solver=saga; total time=   1.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=100, solver=saga; total time=   1.1s\n",
      "[CV] END ...................C=10, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=10, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ....................C=10, max_iter=100, solver=saga; total time=   0.9s\n",
      "[CV] END ...................C=10, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...............C=10, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...............C=10, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...............C=10, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END .....................C=1, max_iter=200, solver=saga; total time=   1.1s\n",
      "[CV] END ....................C=10, max_iter=100, solver=saga; total time=   0.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END .....................C=1, max_iter=300, solver=saga; total time=   2.7s\n",
      "[CV] END .....................C=1, max_iter=200, solver=saga; total time=   1.0s\n",
      "[CV] END ....................C=1, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=10, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...............C=10, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...............C=10, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ...............C=10, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ....................C=10, max_iter=200, solver=saga; total time=   1.7s\n",
      "[CV] END .....................C=1, max_iter=300, solver=saga; total time=   1.1s\n",
      "[CV] END ..................C=100, max_iter=100, solver=lbfgs; total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ..................C=100, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=100, max_iter=100, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=100, max_iter=100, solver=liblinear; total time=   0.2s\n",
      "[CV] END ..............C=100, max_iter=100, solver=liblinear; total time=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=200, solver=saga; total time=   2.1s\n",
      "[CV] END .....................C=1, max_iter=300, solver=saga; total time=   0.8s\n",
      "[CV] END ..................C=100, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=100, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=100, max_iter=200, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..............C=100, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=100, max_iter=200, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=100, max_iter=200, solver=liblinear; total time=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=100, solver=saga; total time=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=200, solver=saga; total time=   1.6s\n",
      "[CV] END ...................C=10, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=10, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ...................C=100, max_iter=100, solver=saga; total time=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=300, solver=saga; total time=   3.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=200, solver=saga; total time=   2.2s\n",
      "[CV] END ...................C=100, max_iter=100, solver=saga; total time=   1.1s\n",
      "[CV] END ..............C=100, max_iter=300, solver=liblinear; total time=   0.2s\n",
      "[CV] END ..............C=100, max_iter=300, solver=liblinear; total time=   0.1s\n",
      "[CV] END ..............C=100, max_iter=300, solver=liblinear; total time=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=200, solver=saga; total time=   1.8s\n",
      "[CV] END ..................C=100, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=100, max_iter=300, solver=lbfgs; total time=   0.0s\n",
      "[CV] END ..................C=100, max_iter=300, solver=lbfgs; total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=200, solver=saga; total time=   2.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=300, solver=saga; total time=   2.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=300, solver=saga; total time=   3.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=300, solver=saga; total time=   3.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...................C=100, max_iter=300, solver=saga; total time=   2.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/highfrezh/anaconda3/envs/ml_env/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ....................C=10, max_iter=300, solver=saga; total time=   1.9s\n",
      "[CV] END ..................C=100, max_iter=100, solver=lbfgs; total time=   0.0s\n",
      "Fitting 3 folds for each of 80 candidates, totalling 240 fits\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=distance; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=uniform; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=uniform; total time=   1.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=uniform; total time=   1.3s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=distance; total time=   0.9s\n",
      "[CV] END algorithm=auto, n_neighbors=3, p=1, weights=distance; total time=   1.3s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=uniform; total time=   0.9s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=uniform; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=uniform; total time=   1.2s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=uniform; total time=   0.3s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=distance; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=uniform; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=distance; total time=   0.8s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=4, p=1, weights=distance; total time=   1.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=uniform; total time=   0.8s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=uniform; total time=   0.8s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=uniform; total time=   1.4s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=distance; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=distance; total time=   0.8s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=1, weights=distance; total time=   1.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=5, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=uniform; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=uniform; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=uniform; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=distance; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=uniform; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=uniform; total time=   0.3s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=distance; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=distance; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=7, p=1, weights=distance; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=uniform; total time=   0.9s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=uniform; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=uniform; total time=   1.0s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=uniform; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=distance; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=uniform; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=distance; total time=   1.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=distance; total time=   0.1s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=auto, n_neighbors=9, p=1, weights=distance; total time=   1.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=uniform; total time=   2.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=uniform; total time=   2.4s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=uniform; total time=   3.1s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=distance; total time=   2.7s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=distance; total time=   2.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=1, weights=distance; total time=   3.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=uniform; total time=   3.6s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=uniform; total time=   3.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=uniform; total time=   3.7s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=distance; total time=   3.2s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=distance; total time=   3.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=3, p=2, weights=distance; total time=   4.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=uniform; total time=   5.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=uniform; total time=   3.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=uniform; total time=   4.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=distance; total time=   4.2s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=distance; total time=   3.1s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=uniform; total time=   2.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=1, weights=distance; total time=   3.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=uniform; total time=   2.6s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=distance; total time=   2.2s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=distance; total time=   3.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=distance; total time=   3.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=4, p=2, weights=uniform; total time=   4.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=uniform; total time=   3.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=distance; total time=   2.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=uniform; total time=   3.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=uniform; total time=   3.4s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=distance; total time=   2.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=1, weights=distance; total time=   2.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=uniform; total time=   2.4s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=uniform; total time=   2.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=uniform; total time=   2.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=distance; total time=   2.1s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=distance; total time=   2.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=5, p=2, weights=distance; total time=   2.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=uniform; total time=   3.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=uniform; total time=   3.6s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=distance; total time=   2.6s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=uniform; total time=   3.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=distance; total time=   2.4s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=uniform; total time=   2.7s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=1, weights=distance; total time=   4.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=uniform; total time=   3.7s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=uniform; total time=   4.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=distance; total time=   1.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=distance; total time=   2.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=uniform; total time=   2.2s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=7, p=2, weights=distance; total time=   3.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=uniform; total time=   3.6s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=uniform; total time=   2.9s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=distance; total time=   3.3s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=uniform; total time=   2.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=distance; total time=   4.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=1, weights=distance; total time=   4.5s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=uniform; total time=   3.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=uniform; total time=   2.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=distance; total time=   2.8s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=distance; total time=   3.0s\n",
      "[CV] END algorithm=ball_tree, n_neighbors=9, p=2, weights=distance; total time=   2.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=uniform; total time=   4.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=uniform; total time=   4.7s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=uniform; total time=   4.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=distance; total time=   4.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=uniform; total time=   2.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=distance; total time=   6.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=1, weights=distance; total time=   6.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=uniform; total time=   6.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=uniform; total time=   6.2s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=distance; total time=   4.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=distance; total time=   6.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=3, p=2, weights=distance; total time=   8.8s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=uniform; total time=   8.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=distance; total time=   5.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=uniform; total time=   9.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=distance; total time=   5.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=uniform; total time=  12.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=uniform; total time=   5.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=uniform; total time=   7.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=uniform; total time=   7.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=1, weights=distance; total time=  11.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=distance; total time=   5.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=distance; total time=   6.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=uniform; total time=   5.7s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=4, p=2, weights=distance; total time=   9.2s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=uniform; total time=   7.8s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=uniform; total time=   6.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=distance; total time=   4.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=distance; total time=   7.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=uniform; total time=   4.7s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=1, weights=distance; total time=   6.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=uniform; total time=   2.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=uniform; total time=   4.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=distance; total time=   2.7s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=distance; total time=   3.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=5, p=2, weights=distance; total time=   3.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=uniform; total time=   3.8s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=uniform; total time=   4.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=uniform; total time=   4.5s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=distance; total time=   4.5s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=distance; total time=   3.5s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=1, weights=distance; total time=   3.7s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=uniform; total time=   4.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=uniform; total time=   4.8s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=uniform; total time=   4.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=distance; total time=   5.5s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=distance; total time=   5.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=uniform; total time=   5.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=7, p=2, weights=distance; total time=   6.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=uniform; total time=   3.5s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=distance; total time=   3.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=uniform; total time=   4.3s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=distance; total time=   4.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=1, weights=distance; total time=   3.8s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=uniform; total time=   4.4s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=uniform; total time=   7.1s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=distance; total time=   4.9s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=uniform; total time=   7.3s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=uniform; total time=   1.2s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=distance; total time=   3.2s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=uniform; total time=   0.9s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=uniform; total time=   2.5s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=distance; total time=   1.0s\n",
      "[CV] END algorithm=kd_tree, n_neighbors=9, p=2, weights=distance; total time=   5.8s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=uniform; total time=   0.5s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=distance; total time=   0.1s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=distance; total time=   1.9s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=1, weights=distance; total time=   2.2s\n",
      "[CV] END algorithm=brute, n_neighbors=3, p=2, weights=distance; total time=   0.8s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=uniform; total time=   0.8s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=distance; total time=   0.9s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=uniform; total time=   2.4s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=uniform; total time=   2.1s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=uniform; total time=   0.1s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=uniform; total time=   0.3s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=distance; total time=   2.4s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=uniform; total time=   0.7s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=distance; total time=   0.4s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=2, weights=distance; total time=   0.4s\n",
      "[CV] END algorithm=brute, n_neighbors=4, p=1, weights=distance; total time=   1.5s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=uniform; total time=   1.2s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=uniform; total time=   1.3s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=uniform; total time=   1.6s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=uniform; total time=   0.4s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=uniform; total time=   0.4s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=distance; total time=   2.2s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=uniform; total time=   0.3s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=distance; total time=   0.5s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=distance; total time=   2.1s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=2, weights=distance; total time=   0.5s\n",
      "[CV] END algorithm=brute, n_neighbors=5, p=1, weights=distance; total time=   2.9s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=uniform; total time=   1.1s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=uniform; total time=   1.0s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=distance; total time=   0.9s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=distance; total time=   1.8s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=distance; total time=   0.1s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=distance; total time=   2.7s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=2, weights=distance; total time=   0.6s\n",
      "[CV] END algorithm=brute, n_neighbors=7, p=1, weights=uniform; total time=   3.4s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=uniform; total time=   1.8s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=uniform; total time=   1.7s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=uniform; total time=   1.6s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=uniform; total time=   0.6s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=distance; total time=   1.0s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=uniform; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=uniform; total time=   0.6s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=distance; total time=   0.2s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=distance; total time=   2.7s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=2, weights=distance; total time=   0.4s\n",
      "[CV] END algorithm=brute, n_neighbors=9, p=1, weights=distance; total time=   2.0s\n",
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] END ...........C=1, degree=5, gamma=auto, kernel=linear; total time=   0.5s\n",
      "[CV] END ...........C=1, degree=5, gamma=auto, kernel=linear; total time=   0.4s\n",
      "[CV] END ...........C=1, degree=5, gamma=auto, kernel=linear; total time=   0.4s\n",
      "[CV] END ..........C=0.01, degree=5, gamma=auto, kernel=poly; total time=   3.5s\n",
      "[CV] END ..........C=0.01, degree=5, gamma=auto, kernel=poly; total time=   5.5s\n",
      "[CV] END .........C=0.01, degree=3, gamma=scale, kernel=poly; total time=   2.2s\n",
      "[CV] END .........C=100, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ..........C=0.01, degree=5, gamma=auto, kernel=poly; total time=   6.0s\n",
      "[CV] END .........C=100, degree=5, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=100, degree=5, gamma=auto, kernel=linear; total time=   0.3s\n",
      "[CV] END .........C=0.01, degree=3, gamma=scale, kernel=poly; total time=   5.7s\n",
      "[CV] END ...........C=10, degree=3, gamma=scale, kernel=poly; total time=   1.3s\n",
      "[CV] END ...........C=10, degree=3, gamma=scale, kernel=poly; total time=   0.6s\n",
      "[CV] END ...........C=10, degree=3, gamma=scale, kernel=poly; total time=   1.8s\n",
      "[CV] END .........C=0.01, degree=3, gamma=scale, kernel=poly; total time=   4.4s\n",
      "[CV] END ........C=0.01, degree=4, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.01, degree=4, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.01, degree=4, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .......C=0.01, degree=5, gamma=scale, kernel=linear; total time=   0.4s\n",
      "[CV] END .......C=0.01, degree=5, gamma=scale, kernel=linear; total time=   0.5s\n",
      "[CV] END .......C=0.01, degree=5, gamma=scale, kernel=linear; total time=   0.8s\n",
      "[CV] END .....C=0.001, degree=4, gamma=scale, kernel=sigmoid; total time=   4.7s\n",
      "[CV] END .....C=0.001, degree=4, gamma=scale, kernel=sigmoid; total time=   5.0s\n",
      "[CV] END ...........C=0.1, degree=3, gamma=scale, kernel=rbf; total time=   1.0s\n",
      "[CV] END .....C=0.001, degree=4, gamma=scale, kernel=sigmoid; total time=   6.3s\n",
      "[CV] END ...........C=0.1, degree=3, gamma=scale, kernel=rbf; total time=   2.0s\n",
      "[CV] END ...........C=0.1, degree=3, gamma=scale, kernel=rbf; total time=   1.6s\n",
      "[CV] END ............C=10, degree=5, gamma=auto, kernel=poly; total time=   1.4s\n",
      "[CV] END ...........C=100, degree=5, gamma=auto, kernel=poly; total time=   1.8s\n",
      "[CV] END ............C=10, degree=5, gamma=auto, kernel=poly; total time=   3.0s\n",
      "[CV] END ...........C=100, degree=5, gamma=auto, kernel=poly; total time=   2.3s\n",
      "[CV] END ...........C=100, degree=5, gamma=auto, kernel=poly; total time=   1.6s\n",
      "[CV] END ............C=10, degree=5, gamma=auto, kernel=poly; total time=   3.7s\n",
      "[CV] END ..............C=1, degree=3, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ..............C=1, degree=3, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ..............C=1, degree=3, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ............C=1, degree=5, gamma=scale, kernel=poly; total time=   2.4s\n",
      "[CV] END ............C=1, degree=5, gamma=scale, kernel=poly; total time=   2.1s\n",
      "[CV] END .............C=1, degree=5, gamma=auto, kernel=poly; total time=   1.5s\n",
      "[CV] END ............C=1, degree=5, gamma=scale, kernel=poly; total time=   4.6s\n",
      "[CV] END .............C=1, degree=5, gamma=auto, kernel=poly; total time=   2.6s\n",
      "[CV] END .........C=0.001, degree=5, gamma=auto, kernel=poly; total time=   3.0s\n",
      "[CV] END .............C=1, degree=5, gamma=auto, kernel=poly; total time=   4.0s\n",
      "[CV] END ...........C=100, degree=5, gamma=scale, kernel=rbf; total time=   0.6s\n",
      "[CV] END ...........C=100, degree=5, gamma=scale, kernel=rbf; total time=   1.1s\n",
      "[CV] END ...........C=100, degree=5, gamma=scale, kernel=rbf; total time=   0.8s\n",
      "[CV] END .........C=0.001, degree=5, gamma=auto, kernel=poly; total time=   4.8s\n",
      "[CV] END .........C=0.001, degree=5, gamma=auto, kernel=poly; total time=   4.8s\n",
      "[CV] END ............C=10, degree=5, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END ............C=10, degree=5, gamma=scale, kernel=rbf; total time=   0.4s\n",
      "[CV] END ............C=10, degree=5, gamma=scale, kernel=rbf; total time=   0.4s\n",
      "[CV] END .........C=0.001, degree=5, gamma=scale, kernel=rbf; total time=   3.4s\n",
      "[CV] END ............C=100, degree=4, gamma=auto, kernel=rbf; total time=   0.4s\n",
      "[CV] END ............C=100, degree=4, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END .........C=0.001, degree=5, gamma=scale, kernel=rbf; total time=   3.4s\n",
      "[CV] END ............C=100, degree=4, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END .........C=0.001, degree=5, gamma=scale, kernel=rbf; total time=   3.0s\n",
      "[CV] END ............C=10, degree=3, gamma=auto, kernel=poly; total time=   0.5s\n",
      "[CV] END ............C=10, degree=3, gamma=auto, kernel=poly; total time=   0.8s\n",
      "[CV] END ............C=10, degree=3, gamma=auto, kernel=poly; total time=   0.6s\n",
      "[CV] END ..............C=1, degree=5, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ......C=0.001, degree=3, gamma=auto, kernel=sigmoid; total time=   3.1s\n",
      "[CV] END ..............C=1, degree=5, gamma=auto, kernel=rbf; total time=   0.7s\n",
      "[CV] END ......C=0.001, degree=3, gamma=auto, kernel=sigmoid; total time=   3.3s\n",
      "[CV] END ..............C=1, degree=5, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ......C=0.001, degree=3, gamma=auto, kernel=sigmoid; total time=   3.6s\n",
      "[CV] END .......C=0.001, degree=3, gamma=auto, kernel=linear; total time=   1.2s\n",
      "[CV] END .......C=0.001, degree=3, gamma=auto, kernel=linear; total time=   1.0s\n",
      "[CV] END .......C=0.001, degree=3, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END .........C=0.001, degree=3, gamma=scale, kernel=rbf; total time=   3.8s\n",
      "[CV] END .........C=0.001, degree=3, gamma=scale, kernel=rbf; total time=   4.2s\n",
      "[CV] END .........C=0.001, degree=3, gamma=scale, kernel=rbf; total time=   4.3s\n",
      "[CV] END .............C=10, degree=4, gamma=auto, kernel=rbf; total time=   0.8s\n",
      "[CV] END .............C=10, degree=4, gamma=auto, kernel=rbf; total time=   0.7s\n",
      "[CV] END ......C=0.01, degree=5, gamma=scale, kernel=sigmoid; total time=   1.7s\n",
      "[CV] END ......C=0.01, degree=5, gamma=scale, kernel=sigmoid; total time=   3.1s\n",
      "[CV] END .............C=10, degree=4, gamma=auto, kernel=rbf; total time=   0.5s\n",
      "[CV] END ...........C=100, degree=4, gamma=scale, kernel=rbf; total time=   0.7s\n",
      "[CV] END ...........C=100, degree=4, gamma=scale, kernel=rbf; total time=   0.7s\n",
      "[CV] END ...........C=100, degree=4, gamma=scale, kernel=rbf; total time=   0.7s\n",
      "[CV] END .......C=0.1, degree=5, gamma=scale, kernel=sigmoid; total time=   0.8s\n",
      "[CV] END ........C=100, degree=5, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=100, degree=5, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ......C=0.01, degree=5, gamma=scale, kernel=sigmoid; total time=   3.9s\n",
      "[CV] END .......C=0.1, degree=5, gamma=scale, kernel=sigmoid; total time=   0.9s\n",
      "[CV] END ........C=100, degree=5, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END .......C=0.1, degree=5, gamma=scale, kernel=sigmoid; total time=   1.0s\n",
      "[CV] END ......C=0.001, degree=3, gamma=scale, kernel=linear; total time=   0.7s\n",
      "[CV] END ......C=0.001, degree=3, gamma=scale, kernel=linear; total time=   1.0s\n",
      "[CV] END ......C=0.001, degree=3, gamma=scale, kernel=linear; total time=   1.2s\n",
      "[CV] END ...........C=10, degree=5, gamma=scale, kernel=poly; total time=   2.3s\n",
      "[CV] END ..........C=0.001, degree=5, gamma=auto, kernel=rbf; total time=   3.8s\n",
      "[CV] END ..........C=0.001, degree=5, gamma=auto, kernel=rbf; total time=   4.8s\n",
      "[CV] END ..........C=0.001, degree=5, gamma=auto, kernel=rbf; total time=   4.4s\n",
      "[CV] END ..........C=100, degree=3, gamma=scale, kernel=poly; total time=   0.7s\n",
      "[CV] END ...........C=10, degree=5, gamma=scale, kernel=poly; total time=   2.2s\n",
      "[CV] END ........C=0.1, degree=3, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=100, degree=3, gamma=scale, kernel=poly; total time=   0.5s\n",
      "[CV] END ........C=0.1, degree=3, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.1, degree=3, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=100, degree=3, gamma=scale, kernel=poly; total time=   0.6s\n",
      "[CV] END ...........C=10, degree=5, gamma=scale, kernel=poly; total time=   1.8s\n",
      "[CV] END ........C=10, degree=3, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .......C=0.001, degree=5, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END .......C=0.001, degree=5, gamma=auto, kernel=linear; total time=   0.7s\n",
      "[CV] END ........C=10, degree=3, gamma=scale, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=10, degree=3, gamma=scale, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END .......C=0.001, degree=5, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END ......C=0.001, degree=5, gamma=auto, kernel=sigmoid; total time=   4.2s\n",
      "[CV] END ......C=0.001, degree=5, gamma=auto, kernel=sigmoid; total time=   4.8s\n",
      "[CV] END ...........C=0.01, degree=4, gamma=auto, kernel=rbf; total time=   5.0s\n",
      "[CV] END ......C=0.001, degree=5, gamma=scale, kernel=linear; total time=   0.7s\n",
      "[CV] END ......C=0.001, degree=5, gamma=scale, kernel=linear; total time=   0.6s\n",
      "[CV] END ......C=0.001, degree=5, gamma=auto, kernel=sigmoid; total time=   6.6s\n",
      "[CV] END ......C=0.001, degree=5, gamma=scale, kernel=linear; total time=   1.0s\n",
      "[CV] END ...........C=0.01, degree=4, gamma=auto, kernel=rbf; total time=   3.2s\n",
      "[CV] END ..........C=0.1, degree=3, gamma=scale, kernel=poly; total time=   1.0s\n",
      "[CV] END ..........C=0.1, degree=3, gamma=scale, kernel=poly; total time=   1.6s\n",
      "[CV] END ..........C=0.1, degree=3, gamma=scale, kernel=poly; total time=   2.9s\n",
      "[CV] END ..........C=100, degree=5, gamma=scale, kernel=poly; total time=   1.3s\n",
      "[CV] END ...........C=0.01, degree=4, gamma=auto, kernel=rbf; total time=   6.1s\n",
      "[CV] END ..........C=100, degree=5, gamma=scale, kernel=poly; total time=   2.0s\n",
      "[CV] END .............C=1, degree=5, gamma=scale, kernel=rbf; total time=   0.7s\n",
      "[CV] END .............C=1, degree=5, gamma=scale, kernel=rbf; total time=   0.8s\n",
      "[CV] END .......C=0.01, degree=4, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END .......C=0.01, degree=4, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END .............C=1, degree=5, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END .......C=0.01, degree=4, gamma=scale, kernel=linear; total time=   0.3s\n",
      "[CV] END ..........C=100, degree=5, gamma=scale, kernel=poly; total time=   2.1s\n",
      "[CV] END .......C=0.1, degree=4, gamma=scale, kernel=sigmoid; total time=   0.7s\n",
      "[CV] END .......C=0.1, degree=4, gamma=scale, kernel=sigmoid; total time=   0.6s\n",
      "[CV] END .......C=0.1, degree=4, gamma=scale, kernel=sigmoid; total time=   0.7s\n",
      "[CV] END ............C=1, degree=3, gamma=scale, kernel=poly; total time=   0.8s\n",
      "[CV] END ............C=1, degree=3, gamma=scale, kernel=poly; total time=   0.7s\n",
      "[CV] END ..........C=0.01, degree=3, gamma=auto, kernel=poly; total time=   2.2s\n",
      "[CV] END ..........C=0.01, degree=3, gamma=auto, kernel=poly; total time=   2.1s\n",
      "[CV] END ............C=1, degree=3, gamma=scale, kernel=poly; total time=   0.6s\n",
      "[CV] END ..........C=0.01, degree=3, gamma=auto, kernel=poly; total time=   2.3s\n",
      "[CV] END .......C=0.001, degree=4, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END ........C=0.01, degree=3, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.01, degree=3, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.01, degree=3, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .......C=0.001, degree=4, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END .......C=0.001, degree=4, gamma=auto, kernel=linear; total time=   0.6s\n",
      "[CV] END ..........C=10, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=10, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=10, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ............C=10, degree=3, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END ............C=10, degree=3, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END ............C=10, degree=3, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END ........C=0.1, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=0.1, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=0.1, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ...........C=0.01, degree=3, gamma=auto, kernel=rbf; total time=   2.6s\n",
      "[CV] END ...........C=0.01, degree=3, gamma=auto, kernel=rbf; total time=   2.9s\n",
      "[CV] END ...........C=0.01, degree=3, gamma=auto, kernel=rbf; total time=   2.8s\n",
      "[CV] END ........C=0.01, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ...........C=0.1, degree=4, gamma=scale, kernel=rbf; total time=   0.9s\n",
      "[CV] END ........C=0.01, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ........C=0.01, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .........C=1, degree=3, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=1, degree=3, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ...........C=0.1, degree=4, gamma=scale, kernel=rbf; total time=   1.0s\n",
      "[CV] END ...........C=0.1, degree=4, gamma=scale, kernel=rbf; total time=   0.9s\n",
      "[CV] END ..........C=10, degree=4, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=10, degree=4, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .........C=1, degree=3, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ..........C=10, degree=4, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=10, degree=3, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=10, degree=3, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ............C=10, degree=4, gamma=scale, kernel=rbf; total time=   0.4s\n",
      "[CV] END ............C=10, degree=4, gamma=scale, kernel=rbf; total time=   0.6s\n",
      "[CV] END .........C=10, degree=3, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ............C=10, degree=4, gamma=scale, kernel=rbf; total time=   0.7s\n",
      "[CV] END ............C=0.1, degree=4, gamma=auto, kernel=rbf; total time=   1.2s\n",
      "[CV] END ............C=0.1, degree=4, gamma=auto, kernel=rbf; total time=   1.1s\n",
      "[CV] END ............C=0.1, degree=4, gamma=auto, kernel=rbf; total time=   1.4s\n",
      "[CV] END .........C=0.1, degree=4, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.1, degree=4, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.1, degree=4, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=100, degree=4, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END .............C=1, degree=4, gamma=auto, kernel=poly; total time=   1.7s\n",
      "[CV] END ........C=100, degree=4, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=100, degree=4, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=0.1, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=0.1, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=0.1, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=10, degree=5, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END .........C=10, degree=5, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=10, degree=5, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END .............C=1, degree=4, gamma=auto, kernel=poly; total time=   1.9s\n",
      "[CV] END .............C=1, degree=4, gamma=auto, kernel=poly; total time=   2.0s\n",
      "[CV] END .............C=1, degree=3, gamma=auto, kernel=poly; total time=   0.7s\n",
      "[CV] END .............C=1, degree=3, gamma=auto, kernel=poly; total time=   0.6s\n",
      "[CV] END .............C=1, degree=3, gamma=auto, kernel=poly; total time=   0.7s\n",
      "[CV] END ........C=100, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=0.001, degree=3, gamma=auto, kernel=rbf; total time=   3.1s\n",
      "[CV] END ........C=100, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=100, degree=4, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=0.001, degree=3, gamma=auto, kernel=rbf; total time=   3.6s\n",
      "[CV] END ..........C=0.001, degree=3, gamma=auto, kernel=rbf; total time=   3.1s\n",
      "[CV] END ...........C=0.1, degree=5, gamma=scale, kernel=rbf; total time=   0.9s\n",
      "[CV] END ...........C=0.1, degree=5, gamma=scale, kernel=rbf; total time=   1.0s\n",
      "[CV] END ...........C=0.1, degree=5, gamma=scale, kernel=rbf; total time=   1.0s\n",
      "[CV] END .............C=10, degree=3, gamma=auto, kernel=rbf; total time=   0.4s\n",
      "[CV] END .............C=10, degree=3, gamma=auto, kernel=rbf; total time=   0.6s\n",
      "[CV] END .........C=0.01, degree=5, gamma=scale, kernel=poly; total time=   2.3s\n",
      "[CV] END .............C=10, degree=3, gamma=auto, kernel=rbf; total time=   0.4s\n",
      "[CV] END .........C=0.01, degree=5, gamma=scale, kernel=poly; total time=   2.5s\n",
      "[CV] END .............C=1, degree=4, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END .............C=1, degree=4, gamma=scale, kernel=rbf; total time=   0.6s[CV] END .........C=0.01, degree=5, gamma=scale, kernel=poly; total time=   2.5s\n",
      "\n",
      "[CV] END ........C=10, degree=5, gamma=scale, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=10, degree=5, gamma=scale, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=10, degree=5, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ...........C=1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ...........C=1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ...........C=1, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .............C=1, degree=4, gamma=scale, kernel=rbf; total time=   0.5s\n",
      "[CV] END ............C=0.1, degree=5, gamma=auto, kernel=rbf; total time=   1.1s\n",
      "[CV] END ......C=0.01, degree=4, gamma=scale, kernel=sigmoid; total time=   1.6s\n",
      "[CV] END ......C=0.01, degree=4, gamma=scale, kernel=sigmoid; total time=   1.7s\n",
      "[CV] END ......C=0.01, degree=4, gamma=scale, kernel=sigmoid; total time=   1.8s\n",
      "[CV] END ..........C=1, degree=4, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ..........C=1, degree=4, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=10, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=1, degree=4, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ............C=0.1, degree=5, gamma=auto, kernel=rbf; total time=   1.1s\n",
      "[CV] END .........C=10, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=10, degree=5, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END .........C=100, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=100, degree=3, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .........C=100, degree=3, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ............C=0.1, degree=5, gamma=auto, kernel=rbf; total time=   1.0s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=scale, kernel=rbf; total time=   2.8s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=scale, kernel=rbf; total time=   2.8s\n",
      "[CV] END ...........C=0.01, degree=5, gamma=auto, kernel=rbf; total time=   2.7s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=scale, kernel=rbf; total time=   2.9s\n",
      "[CV] END ..........C=1, degree=5, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ..........C=1, degree=5, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ..........C=1, degree=5, gamma=auto, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .......C=0.01, degree=4, gamma=auto, kernel=sigmoid; total time=   1.8s\n",
      "[CV] END .......C=0.01, degree=4, gamma=auto, kernel=sigmoid; total time=   1.7s\n",
      "[CV] END ........C=100, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=100, degree=5, gamma=scale, kernel=linear; total time=   0.1s\n",
      "[CV] END ........C=100, degree=5, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END ...........C=0.01, degree=5, gamma=auto, kernel=rbf; total time=   2.8s\n",
      "[CV] END ...........C=0.01, degree=5, gamma=auto, kernel=rbf; total time=   3.0s\n",
      "[CV] END .......C=0.01, degree=4, gamma=auto, kernel=sigmoid; total time=   1.7s\n",
      "[CV] END ........C=0.001, degree=5, gamma=scale, kernel=poly; total time=   2.4s\n",
      "[CV] END ........C=0.001, degree=5, gamma=scale, kernel=poly; total time=   2.3s\n",
      "[CV] END ........C=0.001, degree=5, gamma=scale, kernel=poly; total time=   2.4s\n",
      "[CV] END .......C=0.01, degree=3, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END .......C=0.01, degree=3, gamma=scale, kernel=linear; total time=   0.2s\n",
      "[CV] END .......C=0.01, degree=3, gamma=scale, kernel=linear; total time=   0.3s\n",
      "[CV] END ..............C=1, degree=4, gamma=auto, kernel=rbf; total time=   0.6s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=auto, kernel=poly; total time=   3.1s\n",
      "[CV] END ..............C=1, degree=4, gamma=auto, kernel=rbf; total time=   0.6s\n",
      "[CV] END ........C=100, degree=3, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ........C=100, degree=3, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ..............C=1, degree=4, gamma=auto, kernel=rbf; total time=   0.6s\n",
      "[CV] END ........C=100, degree=3, gamma=auto, kernel=sigmoid; total time=   0.1s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=auto, kernel=poly; total time=   2.7s\n",
      "[CV] END ..........C=0.01, degree=4, gamma=auto, kernel=poly; total time=   2.8s\n",
      "[CV] END .......C=0.1, degree=3, gamma=scale, kernel=sigmoid; total time=   0.9s\n",
      "[CV] END .......C=0.1, degree=3, gamma=scale, kernel=sigmoid; total time=   1.1s\n",
      "[CV] END ........C=0.001, degree=3, gamma=scale, kernel=poly; total time=   2.9s\n",
      "[CV] END ........C=0.001, degree=3, gamma=scale, kernel=poly; total time=   3.0s\n",
      "[CV] END .......C=0.1, degree=3, gamma=scale, kernel=sigmoid; total time=   1.0s\n",
      "[CV] END ........C=0.001, degree=3, gamma=scale, kernel=poly; total time=   3.2s\n",
      "[CV] END ..........C=10, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END ..........C=10, degree=5, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END ..........C=10, degree=5, gamma=auto, kernel=linear; total time=   0.1s\n",
      "[CV] END .........C=0.01, degree=4, gamma=scale, kernel=poly; total time=   3.5s\n",
      "[CV] END .........C=0.01, degree=4, gamma=scale, kernel=poly; total time=   3.4s\n",
      "[CV] END .........C=0.01, degree=4, gamma=scale, kernel=poly; total time=   3.7s\n",
      "[CV] END .........C=0.1, degree=5, gamma=auto, kernel=linear; total time=   0.3s\n",
      "[CV] END ..........C=0.1, degree=4, gamma=scale, kernel=poly; total time=   3.4s\n",
      "[CV] END .........C=0.1, degree=5, gamma=auto, kernel=linear; total time=   0.2s\n",
      "[CV] END .........C=0.1, degree=5, gamma=auto, kernel=linear; total time=   0.3s\n",
      "[CV] END .......C=100, degree=5, gamma=scale, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END .......C=100, degree=5, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .......C=100, degree=5, gamma=scale, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END .......C=100, degree=4, gamma=scale, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END .......C=100, degree=4, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .......C=100, degree=4, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END ..........C=1, degree=3, gamma=auto, kernel=sigmoid; total time=   0.5s\n",
      "[CV] END ..........C=1, degree=3, gamma=auto, kernel=sigmoid; total time=   0.5s\n",
      "[CV] END ..........C=1, degree=3, gamma=auto, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END ..........C=0.1, degree=4, gamma=scale, kernel=poly; total time=   3.4s\n",
      "[CV] END ..........C=0.1, degree=4, gamma=scale, kernel=poly; total time=   4.7s\n",
      "[CV] END .........C=1, degree=5, gamma=scale, kernel=sigmoid; total time=   0.3s\n",
      "[CV] END .........C=1, degree=5, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=1, degree=5, gamma=scale, kernel=sigmoid; total time=   0.2s\n",
      "[CV] END .........C=0.001, degree=4, gamma=auto, kernel=poly; total time=   3.4s\n",
      "[CV] END .........C=0.001, degree=4, gamma=auto, kernel=poly; total time=   4.3s\n",
      "[CV] END .........C=0.001, degree=4, gamma=auto, kernel=poly; total time=   3.6s\n"
     ]
    }
   ],
   "source": [
    "# Models list for Hyperparameter tuning\n",
    "randomcv_models = [\n",
    "    ('Logistic Regression', LogisticRegression(), logreg_params),\n",
    "    (\"KNN\", KNeighborsClassifier(), knn_params),\n",
    "    (\"SVC\", SVC(), svc_params)\n",
    "]\n",
    "\n",
    "model_param = {}\n",
    "\n",
    "# Perform RandomizedSearchCV for each model\n",
    "for name, model, params in randomcv_models:\n",
    "    random = RandomizedSearchCV(estimator=model,\n",
    "                                param_distributions=params,\n",
    "                                n_iter=100,\n",
    "                                cv=3,\n",
    "                                verbose=2, \n",
    "                                n_jobs=-1)\n",
    "    random.fit(X_clean, y)  \n",
    "    model_param[name] = random.best_params_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "caafc5dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------- Best Params for Logistic Regression -------------------\n",
      "{'solver': 'lbfgs', 'max_iter': 100, 'C': 1}\n",
      "---------------- Best Params for KNN -------------------\n",
      "{'weights': 'uniform', 'p': 1, 'n_neighbors': 4, 'algorithm': 'auto'}\n",
      "---------------- Best Params for SVC -------------------\n",
      "{'kernel': 'linear', 'gamma': 'auto', 'degree': 5, 'C': 1}\n"
     ]
    }
   ],
   "source": [
    "# Print out the best parameters for each model\n",
    "for model_name in model_param:\n",
    "    print(f\"---------------- Best Params for {model_name} -------------------\")\n",
    "    print(model_param[model_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "67c1c1d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': {'solver': 'lbfgs', 'max_iter': 100, 'C': 1},\n",
       " 'KNN': {'weights': 'uniform', 'p': 1, 'n_neighbors': 4, 'algorithm': 'auto'},\n",
       " 'SVC': {'kernel': 'linear', 'gamma': 'auto', 'degree': 5, 'C': 1}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_param"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4571b73c",
   "metadata": {},
   "source": [
    "### Retraining the Model with best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c6705b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Model performance for Training set\n",
      "- Accuracy: 1.0000\n",
      "- F1 score: 1.0000\n",
      "- Precision: 1.0000\n",
      "- Recall: 1.0000\n",
      "- Roc Auc Score: 1.0000\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 0.9993\n",
      "- F1 score: 0.9995\n",
      "- Precision: 1.0000\n",
      "- Recall: 0.9990\n",
      "- Roc Auc Score: 0.9995\n",
      "===================================\n",
      "\n",
      "\n",
      "K-Neighbors Classifier\n",
      "Model performance for Training set\n",
      "- Accuracy: 0.9956\n",
      "- F1 score: 0.9970\n",
      "- Precision: 0.9950\n",
      "- Recall: 0.9990\n",
      "- Roc Auc Score: 0.9924\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 0.9901\n",
      "- F1 score: 0.9931\n",
      "- Precision: 0.9873\n",
      "- Recall: 0.9990\n",
      "- Roc Auc Score: 0.9833\n",
      "===================================\n",
      "\n",
      "\n",
      "Support Vector Classifier\n",
      "Model performance for Training set\n",
      "- Accuracy: 1.0000\n",
      "- F1 score: 1.0000\n",
      "- Precision: 1.0000\n",
      "- Recall: 1.0000\n",
      "- Roc Auc Score: 1.0000\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Accuracy: 1.0000\n",
      "- F1 score: 1.0000\n",
      "- Precision: 1.0000\n",
      "- Recall: 1.0000\n",
      "- Roc Auc Score: 1.0000\n",
      "===================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "best_models = {\n",
    "    \"Logistic Regression\": LogisticRegression(**model_param['Logistic Regression']),\n",
    "    \"K-Neighbors Classifier\": KNeighborsClassifier(**model_param['KNN']),\n",
    "    \"Support Vector Classifier\": SVC(**model_param['SVC'])\n",
    "}\n",
    "tuned_report =evaluate_models(X=X_clean, y=y, models=best_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "53c87341",
   "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>Model Name</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classifier</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.999290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K-Neighbors Classifier</td>\n",
       "      <td>0.990064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model Name  Accuracy\n",
       "2  Support Vector Classifier  1.000000\n",
       "0        Logistic Regression  0.999290\n",
       "1     K-Neighbors Classifier  0.990064"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57339f3e",
   "metadata": {},
   "source": [
    "General Observations:\n",
    "- Logistic Regression and SVC show near-perfect performance, both on the training and test sets, suggesting that these models generalize very well.\n",
    "- K-Neighbors Classifier shows a minor performance drop on the test set, which is a sign of mild overfitting, but it is still performing very well overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ab564102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FINAL MODEL 'KNN'\n",
      "Accuracy Score value: 1.0000\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       400\n",
      "           1       1.00      1.00      1.00      1009\n",
      "\n",
      "    accuracy                           1.00      1409\n",
      "   macro avg       1.00      1.00      1.00      1409\n",
      "weighted avg       1.00      1.00      1.00      1409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_model = SVC(**model_param['SVC'])\n",
    "best_model = best_model.fit(X_train,y_train)\n",
    "y_pred = best_model.predict(X_test)\n",
    "score = accuracy_score(y_test,y_pred)\n",
    "cr = classification_report(y_test,y_pred)\n",
    "\n",
    "print(\"FINAL MODEL 'KNN'\")\n",
    "print (\"Accuracy Score value: {:.4f}\".format(score))\n",
    "print (cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4b2d411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x70a535e0ef00>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "ConfusionMatrixDisplay.from_estimator(best_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d34671",
   "metadata": {
    "id": "84d34671"
   },
   "source": [
    "## Best Model is Support Vector Classifier with Accuracy 100%"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "ml_env",
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}