Task -3- CodSoft - Internship.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f000f533",
   "metadata": {},
   "outputs": [],
   "source": [
    "TASK 3:\n",
    "    \n",
    "CUSTOMER CHURNPREDICTION\n",
    "Develop a model to predict customer churn for a subscription-based service or business. \n",
    "Use historical customer data, includingfeatures like usage behavior and customer \n",
    "demographics, and try algorithms like Logistic Regression, Random Forests, or \n",
    "GradientBoosting to predict churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5a3432c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 14)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Churn_Modelling.csv')\n",
    "print(df.shape)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cccb9ef8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>5014</td>\n",
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       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RowNumber    CustomerId Surname   CreditScore Geography Gender  \\\n",
       "count   10000.00000  1.000000e+04   10000  10000.000000     10000  10000   \n",
       "unique          NaN           NaN    2932           NaN         3      2   \n",
       "top             NaN           NaN   Smith           NaN    France   Male   \n",
       "freq            NaN           NaN      32           NaN      5014   5457   \n",
       "mean     5000.50000  1.569094e+07     NaN    650.528800       NaN    NaN   \n",
       "std      2886.89568  7.193619e+04     NaN     96.653299       NaN    NaN   \n",
       "min         1.00000  1.556570e+07     NaN    350.000000       NaN    NaN   \n",
       "25%      2500.75000  1.562853e+07     NaN    584.000000       NaN    NaN   \n",
       "50%      5000.50000  1.569074e+07     NaN    652.000000       NaN    NaN   \n",
       "75%      7500.25000  1.575323e+07     NaN    718.000000       NaN    NaN   \n",
       "max     10000.00000  1.581569e+07     NaN    850.000000       NaN    NaN   \n",
       "\n",
       "                 Age        Tenure        Balance  NumOfProducts    HasCrCard  \\\n",
       "count   10000.000000  10000.000000   10000.000000   10000.000000  10000.00000   \n",
       "unique           NaN           NaN            NaN            NaN          NaN   \n",
       "top              NaN           NaN            NaN            NaN          NaN   \n",
       "freq             NaN           NaN            NaN            NaN          NaN   \n",
       "mean       38.921800      5.012800   76485.889288       1.530200      0.70550   \n",
       "std        10.487806      2.892174   62397.405202       0.581654      0.45584   \n",
       "min        18.000000      0.000000       0.000000       1.000000      0.00000   \n",
       "25%        32.000000      3.000000       0.000000       1.000000      0.00000   \n",
       "50%        37.000000      5.000000   97198.540000       1.000000      1.00000   \n",
       "75%        44.000000      7.000000  127644.240000       2.000000      1.00000   \n",
       "max        92.000000     10.000000  250898.090000       4.000000      1.00000   \n",
       "\n",
       "        IsActiveMember  EstimatedSalary        Exited  \n",
       "count     10000.000000     10000.000000  10000.000000  \n",
       "unique             NaN              NaN           NaN  \n",
       "top                NaN              NaN           NaN  \n",
       "freq               NaN              NaN           NaN  \n",
       "mean          0.515100    100090.239881      0.203700  \n",
       "std           0.499797     57510.492818      0.402769  \n",
       "min           0.000000        11.580000      0.000000  \n",
       "25%           0.000000     51002.110000      0.000000  \n",
       "50%           1.000000    100193.915000      0.000000  \n",
       "75%           1.000000    149388.247500      0.000000  \n",
       "max           1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e329091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() # Delete the duplicate, if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f005605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be4a09c",
   "metadata": {},
   "source": [
    "# Drop 'RowNumber and CustomerId' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d9056b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surname            0\n",
      "CreditScore        0\n",
      "Geography          0\n",
      "Gender             0\n",
      "Age                0\n",
      "Tenure             0\n",
      "Balance            0\n",
      "NumOfProducts      0\n",
      "HasCrCard          0\n",
      "IsActiveMember     0\n",
      "EstimatedSalary    0\n",
      "Exited             0\n",
      "dtype: int64\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0  Hargrave          619    France  Female   42       2       0.00   \n",
       "1      Hill          608     Spain  Female   41       1   83807.86   \n",
       "2      Onio          502    France  Female   42       8  159660.80   \n",
       "3      Boni          699    France  Female   39       1       0.00   \n",
       "4  Mitchell          850     Spain  Female   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0              1          1               1        101348.88       1  \n",
       "1              1          0               1        112542.58       0  \n",
       "2              3          1               0        113931.57       1  \n",
       "3              2          0               0         93826.63       0  \n",
       "4              1          1               1         79084.10       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['RowNumber','CustomerId'],axis='columns', inplace=True)\n",
    "print(df.isnull().sum())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3935d18d",
   "metadata": {},
   "source": [
    "# Encode column 'Surname'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b846f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname\n",
       "Smith       32\n",
       "Scott       29\n",
       "Martin      29\n",
       "Walker      28\n",
       "Brown       26\n",
       "            ..\n",
       "Izmailov     1\n",
       "Bold         1\n",
       "Bonham       1\n",
       "Poninski     1\n",
       "Burbidge     1\n",
       "Name: count, Length: 2932, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Surname'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d5ebe6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Surname'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3422ea0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess the column berfore applying label encoder\n",
    "\n",
    "df['Surname'] = df['Surname'].str.lower()\n",
    "\n",
    "df['Surname'] = df['Surname'].str.strip()\n",
    "\n",
    "df['Surname'] = df['Surname'].str.replace(\" \", \"_\")\n",
    "df['Surname'] = df['Surname'].str.replace(\".\", \"\")\n",
    "df['Surname'] = df['Surname'].str.replace(\",\", \"\")\n",
    "df['Surname'] = df['Surname'].str.replace(\"-\", \"_\")\n",
    "\n",
    "df['Surname'] = df['Surname'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1324ffc",
   "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>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0      hargrave          619    France  Female   42       2       0.00   \n",
       "1          hill          608     Spain  Female   41       1   83807.86   \n",
       "2          onio          502    France  Female   42       8  159660.80   \n",
       "3          boni          699    France  Female   39       1       0.00   \n",
       "4      mitchell          850     Spain  Female   43       2  125510.82   \n",
       "...         ...          ...       ...     ...  ...     ...        ...   \n",
       "9995   obijiaku          771    France    Male   39       5       0.00   \n",
       "9996  johnstone          516    France    Male   35      10   57369.61   \n",
       "9997        liu          709    France  Female   36       7       0.00   \n",
       "9998  sabbatini          772   Germany    Male   42       3   75075.31   \n",
       "9999     walker          792    France  Female   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0                 1          1               1        101348.88       1  \n",
       "1                 1          0               1        112542.58       0  \n",
       "2                 3          1               0        113931.57       1  \n",
       "3                 2          0               0         93826.63       0  \n",
       "4                 1          1               1         79084.10       0  \n",
       "...             ...        ...             ...              ...     ...  \n",
       "9995              2          1               0         96270.64       0  \n",
       "9996              1          1               1        101699.77       0  \n",
       "9997              1          0               1         42085.58       1  \n",
       "9998              2          1               0         92888.52       1  \n",
       "9999              1          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "346edfa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "df['Surname'] = label_encoder.fit_transform(df['Surname'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ba2608d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Surname          10000 non-null  int32  \n",
      " 1   CreditScore      10000 non-null  int64  \n",
      " 2   Geography        10000 non-null  object \n",
      " 3   Gender           10000 non-null  object \n",
      " 4   Age              10000 non-null  int64  \n",
      " 5   Tenure           10000 non-null  int64  \n",
      " 6   Balance          10000 non-null  float64\n",
      " 7   NumOfProducts    10000 non-null  int64  \n",
      " 8   HasCrCard        10000 non-null  int64  \n",
      " 9   IsActiveMember   10000 non-null  int64  \n",
      " 10  EstimatedSalary  10000 non-null  float64\n",
      " 11  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int32(1), int64(7), object(2)\n",
      "memory usage: 898.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "deb60d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1115</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2039</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>289</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0     1115          619    France  Female   42       2       0.00   \n",
       "1     1177          608     Spain  Female   41       1   83807.86   \n",
       "2     2039          502    France  Female   42       8  159660.80   \n",
       "3      289          699    France  Female   39       1       0.00   \n",
       "4     1821          850     Spain  Female   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0              1          1               1        101348.88       1  \n",
       "1              1          0               1        112542.58       0  \n",
       "2              3          1               0        113931.57       1  \n",
       "3              2          0               0         93826.63       0  \n",
       "4              1          1               1         79084.10       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e797a80f",
   "metadata": {},
   "source": [
    "# Geography and Gender Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "453aa147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography\n",
       "France     5014\n",
       "Germany    2509\n",
       "Spain      2477\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Geography'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49ea24c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Male      5457\n",
       "Female    4543\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d1e9a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Series name: Geography\n",
      "Non-Null Count  Dtype   \n",
      "--------------  -----   \n",
      "10000 non-null  category\n",
      "dtypes: category(1)\n",
      "memory usage: 10.0 KB\n"
     ]
    }
   ],
   "source": [
    "# first convet the column into 'category' data type\n",
    "\n",
    "df['Geography'] = df['Geography'].astype('category')\n",
    "df['Geography'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79b12d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Series name: Gender\n",
      "Non-Null Count  Dtype   \n",
      "--------------  -----   \n",
      "10000 non-null  category\n",
      "dtypes: category(1)\n",
      "memory usage: 10.0 KB\n"
     ]
    }
   ],
   "source": [
    "# first convet the column into 'category' data type\n",
    "\n",
    "df['Gender'] = df['Gender'].astype('category')\n",
    "df['Gender'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b8e069e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess the column berfore conveting into machine laearning language\n",
    "\n",
    "df['Geography'] = df['Geography'].str.lower()\n",
    "df['Gender'] = df['Gender'].str.lower()\n",
    "\n",
    "df['Geography'] = df['Geography'].str.strip()\n",
    "df['Gender'] = df['Gender'].str.strip()\n",
    "\n",
    "df['Geography'] = df['Geography'].str.replace(\" \", \"_\")\n",
    "df['Gender'] = df['Gender'].str.replace(\" \", \"_\")\n",
    "\n",
    "df['Geography'] = df['Geography'].str.replace(\".\", \"\")\n",
    "df['Gender'] = df['Gender'].str.replace(\".\", \"\")\n",
    "\n",
    "df['Geography'] = df['Geography'].str.replace(\",\", \"\")\n",
    "df['Gender'] = df['Gender'].str.replace(\",\", \"\")\n",
    "\n",
    "df['Geography'] = df['Geography'].str.replace(\"-\", \"_\")\n",
    "df['Gender'] = df['Gender'].str.replace(\"-\", \"_\")\n",
    "\n",
    "df['Geography'] = df['Geography'].str.strip()\n",
    "df['Gender'] = df['Gender'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fac638e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1115</td>\n",
       "      <td>619</td>\n",
       "      <td>france</td>\n",
       "      <td>female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177</td>\n",
       "      <td>608</td>\n",
       "      <td>spain</td>\n",
       "      <td>female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2039</td>\n",
       "      <td>502</td>\n",
       "      <td>france</td>\n",
       "      <td>female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>289</td>\n",
       "      <td>699</td>\n",
       "      <td>france</td>\n",
       "      <td>female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>850</td>\n",
       "      <td>spain</td>\n",
       "      <td>female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0     1115          619    france  female   42       2       0.00   \n",
       "1     1177          608     spain  female   41       1   83807.86   \n",
       "2     2039          502    france  female   42       8  159660.80   \n",
       "3      289          699    france  female   39       1       0.00   \n",
       "4     1821          850     spain  female   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0              1          1               1        101348.88       1  \n",
       "1              1          0               1        112542.58       0  \n",
       "2              3          1               0        113931.57       1  \n",
       "3              2          0               0         93826.63       0  \n",
       "4              1          1               1         79084.10       0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3e3d992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      france  germany  spain\n",
      "0       True    False  False\n",
      "1      False    False   True\n",
      "2       True    False  False\n",
      "3       True    False  False\n",
      "4      False    False   True\n",
      "...      ...      ...    ...\n",
      "9995    True    False  False\n",
      "9996    True    False  False\n",
      "9997    True    False  False\n",
      "9998   False     True  False\n",
      "9999    True    False  False\n",
      "\n",
      "[10000 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Apply Pd.get_dummies function on geography column\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df_Geography = pd.get_dummies(df['Geography'])\n",
    "print(df_Geography)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "287d1518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      female   male\n",
       "0       True  False\n",
       "1       True  False\n",
       "2       True  False\n",
       "3       True  False\n",
       "4       True  False\n",
       "...      ...    ...\n",
       "9995   False   True\n",
       "9996   False   True\n",
       "9997    True  False\n",
       "9998   False   True\n",
       "9999    True  False\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Pd.get_dummies function on gender column\n",
    "\n",
    "df_Gender = pd.get_dummies(df['Gender'])\n",
    "df_Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "793d2f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 12)\n",
      "\n",
      "(10000, 15)\n",
      "\n",
      "(10000, 17)\n"
     ]
    }
   ],
   "source": [
    "# Concatenate data \n",
    "print(df.shape)\n",
    "print()\n",
    "df = pd.concat([df,df_Geography], axis =1)\n",
    "print(df.shape)\n",
    "print()\n",
    "df = pd.concat([df,df_Gender], axis =1)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55712915",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
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       "      <th>france</th>\n",
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       "      <th>0</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2039</td>\n",
       "      <td>502</td>\n",
       "      <td>france</td>\n",
       "      <td>female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>289</td>\n",
       "      <td>699</td>\n",
       "      <td>france</td>\n",
       "      <td>female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>850</td>\n",
       "      <td>spain</td>\n",
       "      <td>female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0     1115          619    france  female   42       2       0.00   \n",
       "1     1177          608     spain  female   41       1   83807.86   \n",
       "2     2039          502    france  female   42       8  159660.80   \n",
       "3      289          699    france  female   39       1       0.00   \n",
       "4     1821          850     spain  female   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  france  \\\n",
       "0              1          1               1        101348.88       1    True   \n",
       "1              1          0               1        112542.58       0   False   \n",
       "2              3          1               0        113931.57       1    True   \n",
       "3              2          0               0         93826.63       0    True   \n",
       "4              1          1               1         79084.10       0   False   \n",
       "\n",
       "   germany  spain  female   male  \n",
       "0    False  False    True  False  \n",
       "1    False   True    True  False  \n",
       "2    False  False    True  False  \n",
       "3    False  False    True  False  \n",
       "4    False   True    True  False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e06fc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the Geography column \n",
    "\n",
    "df.drop('Geography', axis =1, inplace=True)\n",
    "df.drop('Gender', axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44952d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37a49147",
   "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>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>france</th>\n",
       "      <th>germany</th>\n",
       "      <th>spain</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1115</td>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177</td>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2039</td>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>289</td>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Surname  CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0     1115          619   42       2       0.00              1          1   \n",
       "1     1177          608   41       1   83807.86              1          0   \n",
       "2     2039          502   42       8  159660.80              3          1   \n",
       "3      289          699   39       1       0.00              2          0   \n",
       "4     1821          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  france  germany  spain  female  \\\n",
       "0               1        101348.88       1    True    False  False    True   \n",
       "1               1        112542.58       0   False    False   True    True   \n",
       "2               0        113931.57       1    True    False  False    True   \n",
       "3               0         93826.63       0    True    False  False    True   \n",
       "4               1         79084.10       0   False    False   True    True   \n",
       "\n",
       "    male  \n",
       "0  False  \n",
       "1  False  \n",
       "2  False  \n",
       "3  False  \n",
       "4  False  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15e2ba4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype   \n",
      "---  ------           --------------  -----   \n",
      " 0   Surname          10000 non-null  int32   \n",
      " 1   CreditScore      10000 non-null  int64   \n",
      " 2   Age              10000 non-null  int64   \n",
      " 3   Tenure           10000 non-null  int64   \n",
      " 4   Balance          10000 non-null  float64 \n",
      " 5   NumOfProducts    10000 non-null  category\n",
      " 6   HasCrCard        10000 non-null  category\n",
      " 7   IsActiveMember   10000 non-null  category\n",
      " 8   EstimatedSalary  10000 non-null  float64 \n",
      " 9   Exited           10000 non-null  int64   \n",
      " 10  france           10000 non-null  bool    \n",
      " 11  germany          10000 non-null  bool    \n",
      " 12  spain            10000 non-null  bool    \n",
      " 13  female           10000 non-null  bool    \n",
      " 14  male             10000 non-null  bool    \n",
      "dtypes: bool(5), category(3), float64(2), int32(1), int64(4)\n",
      "memory usage: 586.5 KB\n"
     ]
    }
   ],
   "source": [
    "df['IsActiveMember'] = df['IsActiveMember'].astype('category')\n",
    "df['NumOfProducts'] = df['NumOfProducts'].astype('category')\n",
    "df['HasCrCard'] = df['HasCrCard'].astype('category')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a89e730c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname            0\n",
       "CreditScore        0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "france             0\n",
       "germany            0\n",
       "spain              0\n",
       "female             0\n",
       "male               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd6d357",
   "metadata": {},
   "source": [
    "# seperate x and y from df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7a0ad6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype   \n",
      "---  ------           --------------  -----   \n",
      " 0   Surname          10000 non-null  int32   \n",
      " 1   CreditScore      10000 non-null  int64   \n",
      " 2   Age              10000 non-null  int64   \n",
      " 3   Tenure           10000 non-null  int64   \n",
      " 4   Balance          10000 non-null  float64 \n",
      " 5   NumOfProducts    10000 non-null  category\n",
      " 6   HasCrCard        10000 non-null  category\n",
      " 7   IsActiveMember   10000 non-null  category\n",
      " 8   EstimatedSalary  10000 non-null  float64 \n",
      " 9   france           10000 non-null  bool    \n",
      " 10  germany          10000 non-null  bool    \n",
      " 11  spain            10000 non-null  bool    \n",
      " 12  female           10000 non-null  bool    \n",
      " 13  male             10000 non-null  bool    \n",
      "dtypes: bool(5), category(3), float64(2), int32(1), int64(3)\n",
      "memory usage: 508.4 KB\n"
     ]
    }
   ],
   "source": [
    "x = df.drop('Exited',axis=1)\n",
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d996b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Series name: Exited\n",
      "Non-Null Count  Dtype   \n",
      "--------------  -----   \n",
      "10000 non-null  category\n",
      "dtypes: category(1)\n",
      "memory usage: 10.0 KB\n"
     ]
    }
   ],
   "source": [
    "y = df['Exited']\n",
    "y = y.astype('category')\n",
    "y.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "864f8064",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3788cec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000\n",
      "8000\n",
      "2000\n",
      "2000\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(y_train))\n",
    "print(len(x_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f9f24b",
   "metadata": {},
   "source": [
    "# Check Class Imbalance in the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c519acaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    6377\n",
       "1    1623\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8e6b53f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12716, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    6358\n",
       "1    6358\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is a class imbalance so apply SMOTE analysis\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote = SMOTE(random_state=50)\n",
    "\n",
    "x_train, y_train = smote.fit_resample(x_train, y_train)\n",
    "\n",
    "print(x_train.shape)\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b953935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isna().sum()\n",
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b9c43dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname            0\n",
       "CreditScore        0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "france             0\n",
       "germany            0\n",
       "spain              0\n",
       "female             0\n",
       "male               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa4a2a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd674958",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da9c7203",
   "metadata": {},
   "source": [
    "# Apply Standard Scaler to scale the values between same range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8600d4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12716, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2000, 14)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(x_train)\n",
    "\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "\n",
    "print(x_train.shape)\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb9875c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6372"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.count_nonzero(np.isnan(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf1ad795",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.where(np.isnan(x_train), np.nanmedian(x_train, axis=0),x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "135917ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_count_x_train = np.count_nonzero(np.isnan(x_train))\n",
    "nan_count_x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c80a630",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "280e02f5",
   "metadata": {},
   "source": [
    "# Apply PCA for dimentionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c78de9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12716, 14)\n",
      "(2000, 14)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=0.99)\n",
    "\n",
    "pca.fit(x_train)\n",
    "x_train = pca.transform(x_train)\n",
    "x_test = pca.transform(x_test)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69525384",
   "metadata": {},
   "source": [
    "# Apply Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4cc18c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model_rfc = RandomForestClassifier()\n",
    "model_rfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "98751c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "\n",
    "model_rfc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e097cff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model training score:  1.0\n",
      "model training score:  0.843\n"
     ]
    }
   ],
   "source": [
    "# model training and testing score\n",
    "\n",
    "training_score = model_rfc.score(x_train,y_train)\n",
    "print('model training score: ',training_score)\n",
    "testing_score = model_rfc.score(x_test,y_test)\n",
    "print('model training score: ',testing_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78041ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model predicted values\n",
    "\n",
    "model_pred = model_rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c5791faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.843\n"
     ]
    }
   ],
   "source": [
    "# Model accuracy Score\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy = accuracy_score(model_pred,y_test)\n",
    "print('Accuracy Score: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fb0591e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.89      0.90      1665\n",
      "           1       0.53      0.62      0.57       335\n",
      "\n",
      "    accuracy                           0.84      2000\n",
      "   macro avg       0.72      0.75      0.74      2000\n",
      "weighted avg       0.85      0.84      0.85      2000\n",
      "\n",
      "\n",
      "[[1478  187]\n",
      " [ 127  208]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 44,
     "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": [
    "# Classification Report\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix , classification_report\n",
    "\n",
    "cr = classification_report(model_pred,y_test)\n",
    "print(cr)\n",
    "print()\n",
    "\n",
    "# Heatmap of the confusion matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(model_pred,y_test)\n",
    "print(cm)\n",
    "\n",
    "sns.heatmap(cm,annot=True,fmt='d',cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612e6dfd",
   "metadata": {},
   "source": [
    "# Apply Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "67f519f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model_lr = LogisticRegression()\n",
    "\n",
    "model_lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fc8d996a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model training score:  0.8446052217678516\n",
      "model testing score:  0.8085\n"
     ]
    }
   ],
   "source": [
    "print(\"model training score: \",model_lr.score(x_train,y_train))\n",
    "print(\"model testing score: \",model_lr.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "de86845e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predicted_lr = model_lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9e258677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model accuracy score:  0.8085\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.86      0.88      1680\n",
      "           1       0.42      0.52      0.46       320\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.66      0.69      0.67      2000\n",
      "weighted avg       0.83      0.81      0.82      2000\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix , classification_report\n",
    "\n",
    "print(\"model accuracy score: \", accuracy_score(model_predicted_lr,y_test))\n",
    "print()\n",
    "\n",
    "cr = classification_report(model_predicted_lr,y_test)\n",
    "print(cr)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "33fa1a12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.86      0.88      1680\n",
      "           1       0.42      0.52      0.46       320\n",
      "\n",
      "    accuracy                           0.81      2000\n",
      "   macro avg       0.66      0.69      0.67      2000\n",
      "weighted avg       0.83      0.81      0.82      2000\n",
      "\n",
      "\n",
      "[[1451  229]\n",
      " [ 154  166]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 49,
     "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": [
    "# Classification Report\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix , classification_report\n",
    "\n",
    "cr = classification_report(model_predicted_lr,y_test)\n",
    "print(cr)\n",
    "print()\n",
    "\n",
    "# Heatmap of the confusion matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(model_predicted_lr,y_test)\n",
    "print(cm)\n",
    "\n",
    "sns.heatmap(cm,annot=True,fmt='d',cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b1582b",
   "metadata": {},
   "source": [
    "# Apply Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b683a17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>GradientBoostingClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model_gbc = GradientBoostingClassifier()\n",
    "model_gbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ab11566b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "\n",
    "model_gbc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "61577b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model training score:  0.8932054105064485\n",
      "model testing score:  0.8425\n"
     ]
    }
   ],
   "source": [
    "# model training and testing score\n",
    "\n",
    "training_score = model_gbc.score(x_train,y_train)\n",
    "testing_score = model_gbc.score(x_test,y_test)\n",
    "print('model training score: ',training_score )\n",
    "print('model testing score: ',testing_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "21193c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model prediction\n",
    "\n",
    "model_pred_gb = model_gbc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "51b24d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model accuracy score is:  0.843\n"
     ]
    }
   ],
   "source": [
    "# model accuracy score\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy = accuracy_score(model_pred,y_test)\n",
    "\n",
    "print('model accuracy score is: ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c7e5eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.89      0.90      1665\n",
      "           1       0.53      0.62      0.57       335\n",
      "\n",
      "    accuracy                           0.84      2000\n",
      "   macro avg       0.72      0.75      0.74      2000\n",
      "weighted avg       0.85      0.84      0.85      2000\n",
      "\n",
      "\n",
      "[[1478  187]\n",
      " [ 127  208]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 55,
     "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": [
    "# Classification Report\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix , classification_report\n",
    "\n",
    "cr = classification_report(model_pred,y_test)\n",
    "print(cr)\n",
    "print()\n",
    "\n",
    "# Heatmap of the confusion matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(model_pred,y_test)\n",
    "print(cm)\n",
    "\n",
    "sns.heatmap(cm,annot=True,fmt='d',cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c54568",
   "metadata": {},
   "source": [
    "# Thank You :)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}