Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DETECTING ANOMALIES IN FINANCIAL TRANSACTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Libraries Utilized:**\n",
    "\n",
    "- Pandas (utilized for dataset importing and analysis)\n",
    "- Matplotlib (employed for visualizing insights and result analysis through graphical representation)\n",
    "- IBM Learn (utilized for Sample Minority Oversampling to address imbalanced data distribution)\n",
    "- Sklearn (utilized for constructing machine learning models and conducting evaluations)\n",
    "- XGBoost (utilized for implementing boosted trees using XGBoost algorithm)\n",
    "- Pickle (utilized for storing the trained models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import pickle\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>...</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-21 12:14:25</td>\n",
       "      <td>2291163933867244</td>\n",
       "      <td>fraud_Kirlin and Sons</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>2.86</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Elliott</td>\n",
       "      <td>M</td>\n",
       "      <td>351 Darlene Green</td>\n",
       "      <td>...</td>\n",
       "      <td>33.9659</td>\n",
       "      <td>-80.9355</td>\n",
       "      <td>333497</td>\n",
       "      <td>Mechanical engineer</td>\n",
       "      <td>1968-03-19</td>\n",
       "      <td>2da90c7d74bd46a0caf3777415b3ebd3</td>\n",
       "      <td>1371816865</td>\n",
       "      <td>33.986391</td>\n",
       "      <td>-81.200714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-21 12:14:33</td>\n",
       "      <td>3573030041201292</td>\n",
       "      <td>fraud_Sporer-Keebler</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Joanne</td>\n",
       "      <td>Williams</td>\n",
       "      <td>F</td>\n",
       "      <td>3638 Marsh Union</td>\n",
       "      <td>...</td>\n",
       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
       "      <td>302</td>\n",
       "      <td>Sales professional, IT</td>\n",
       "      <td>1990-01-17</td>\n",
       "      <td>324cc204407e99f51b0d6ca0055005e7</td>\n",
       "      <td>1371816873</td>\n",
       "      <td>39.450498</td>\n",
       "      <td>-109.960431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-06-21 12:14:53</td>\n",
       "      <td>3598215285024754</td>\n",
       "      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>F</td>\n",
       "      <td>9333 Valentine Point</td>\n",
       "      <td>...</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>Librarian, public</td>\n",
       "      <td>1970-10-21</td>\n",
       "      <td>c81755dbbbea9d5c77f094348a7579be</td>\n",
       "      <td>1371816893</td>\n",
       "      <td>40.495810</td>\n",
       "      <td>-74.196111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-06-21 12:15:15</td>\n",
       "      <td>3591919803438423</td>\n",
       "      <td>fraud_Haley Group</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>60.05</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Williams</td>\n",
       "      <td>M</td>\n",
       "      <td>32941 Krystal Mill Apt. 552</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>Set designer</td>\n",
       "      <td>1987-07-25</td>\n",
       "      <td>2159175b9efe66dc301f149d3d5abf8c</td>\n",
       "      <td>1371816915</td>\n",
       "      <td>28.812398</td>\n",
       "      <td>-80.883061</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-06-21 12:15:17</td>\n",
       "      <td>3526826139003047</td>\n",
       "      <td>fraud_Johnston-Casper</td>\n",
       "      <td>travel</td>\n",
       "      <td>3.19</td>\n",
       "      <td>Nathan</td>\n",
       "      <td>Massey</td>\n",
       "      <td>M</td>\n",
       "      <td>5783 Evan Roads Apt. 465</td>\n",
       "      <td>...</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>Furniture designer</td>\n",
       "      <td>1955-07-06</td>\n",
       "      <td>57ff021bd3f328f8738bb535c302a31b</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>44.959148</td>\n",
       "      <td>-85.884734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2020-06-21 12:14:25  2291163933867244   \n",
       "1           1   2020-06-21 12:14:33  3573030041201292   \n",
       "2           2   2020-06-21 12:14:53  3598215285024754   \n",
       "3           3   2020-06-21 12:15:15  3591919803438423   \n",
       "4           4   2020-06-21 12:15:17  3526826139003047   \n",
       "\n",
       "                               merchant        category    amt   first  \\\n",
       "0                 fraud_Kirlin and Sons   personal_care   2.86    Jeff   \n",
       "1                  fraud_Sporer-Keebler   personal_care  29.84  Joanne   \n",
       "2  fraud_Swaniawski, Nitzsche and Welch  health_fitness  41.28  Ashley   \n",
       "3                     fraud_Haley Group        misc_pos  60.05   Brian   \n",
       "4                 fraud_Johnston-Casper          travel   3.19  Nathan   \n",
       "\n",
       "       last gender                       street  ...      lat      long  \\\n",
       "0   Elliott      M            351 Darlene Green  ...  33.9659  -80.9355   \n",
       "1  Williams      F             3638 Marsh Union  ...  40.3207 -110.4360   \n",
       "2     Lopez      F         9333 Valentine Point  ...  40.6729  -73.5365   \n",
       "3  Williams      M  32941 Krystal Mill Apt. 552  ...  28.5697  -80.8191   \n",
       "4    Massey      M     5783 Evan Roads Apt. 465  ...  44.2529  -85.0170   \n",
       "\n",
       "   city_pop                     job         dob  \\\n",
       "0    333497     Mechanical engineer  1968-03-19   \n",
       "1       302  Sales professional, IT  1990-01-17   \n",
       "2     34496       Librarian, public  1970-10-21   \n",
       "3     54767            Set designer  1987-07-25   \n",
       "4      1126      Furniture designer  1955-07-06   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n",
       "1  324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n",
       "2  c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n",
       "3  2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n",
       "4  57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"fraudTest.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "trans_date_trans_time    0\n",
       "cc_num                   0\n",
       "merchant                 0\n",
       "category                 0\n",
       "amt                      0\n",
       "first                    0\n",
       "last                     0\n",
       "gender                   0\n",
       "street                   0\n",
       "city                     0\n",
       "state                    0\n",
       "zip                      0\n",
       "lat                      0\n",
       "long                     0\n",
       "city_pop                 0\n",
       "job                      0\n",
       "dob                      0\n",
       "trans_num                0\n",
       "unix_time                0\n",
       "merch_lat                0\n",
       "merch_long               0\n",
       "is_fraud                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 int64\n",
       "trans_date_trans_time     object\n",
       "cc_num                     int64\n",
       "merchant                  object\n",
       "category                  object\n",
       "amt                      float64\n",
       "first                     object\n",
       "last                      object\n",
       "gender                    object\n",
       "street                    object\n",
       "city                      object\n",
       "state                     object\n",
       "zip                        int64\n",
       "lat                      float64\n",
       "long                     float64\n",
       "city_pop                   int64\n",
       "job                       object\n",
       "dob                       object\n",
       "trans_num                 object\n",
       "unix_time                  int64\n",
       "merch_lat                float64\n",
       "merch_long               float64\n",
       "is_fraud                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_fraud\n",
       "0    553574\n",
       "1      2145\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.is_fraud.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "trans_date_trans_time    0\n",
       "cc_num                   0\n",
       "merchant                 0\n",
       "category                 0\n",
       "amt                      0\n",
       "first                    0\n",
       "last                     0\n",
       "gender                   0\n",
       "street                   0\n",
       "city                     0\n",
       "state                    0\n",
       "zip                      0\n",
       "lat                      0\n",
       "long                     0\n",
       "city_pop                 0\n",
       "job                      0\n",
       "dob                      0\n",
       "trans_num                0\n",
       "unix_time                0\n",
       "merch_lat                0\n",
       "merch_long               0\n",
       "is_fraud                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0', 'cc_num','first', 'last', 'street', 'city', 'state', 'zip', 'dob', 'trans_num','trans_date_trans_time', 'gender', 'merchant', 'merch_lat', 'merch_long'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
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       "      <th>long</th>\n",
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       "      <th>0</th>\n",
       "      <td>personal_care</td>\n",
       "      <td>2.86</td>\n",
       "      <td>33.9659</td>\n",
       "      <td>-80.9355</td>\n",
       "      <td>333497</td>\n",
       "      <td>Mechanical engineer</td>\n",
       "      <td>1371816865</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>personal_care</td>\n",
       "      <td>29.84</td>\n",
       "      <td>40.3207</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>Librarian, public</td>\n",
       "      <td>1371816893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>misc_pos</td>\n",
       "      <td>60.05</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>Set designer</td>\n",
       "      <td>1371816915</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>travel</td>\n",
       "      <td>3.19</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>Furniture designer</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         category    amt      lat      long  city_pop                     job  \\\n",
       "0   personal_care   2.86  33.9659  -80.9355    333497     Mechanical engineer   \n",
       "1   personal_care  29.84  40.3207 -110.4360       302  Sales professional, IT   \n",
       "2  health_fitness  41.28  40.6729  -73.5365     34496       Librarian, public   \n",
       "3        misc_pos  60.05  28.5697  -80.8191     54767            Set designer   \n",
       "4          travel   3.19  44.2529  -85.0170      1126      Furniture designer   \n",
       "\n",
       "    unix_time  is_fraud  \n",
       "0  1371816865         0  \n",
       "1  1371816873         0  \n",
       "2  1371816893         0  \n",
       "3  1371816915         0  \n",
       "4  1371816917         0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df[\"category\"] = le.fit_transform(df[\"category\"])\n",
    "df[\"job\"] = le.fit_transform(df[\"job\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
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       "      <td>2.86</td>\n",
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       "      <td>333497</td>\n",
       "      <td>275</td>\n",
       "      <td>1371816865</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>29.84</td>\n",
       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
       "      <td>302</td>\n",
       "      <td>392</td>\n",
       "      <td>1371816873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>41.28</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>259</td>\n",
       "      <td>1371816893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>60.05</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>407</td>\n",
       "      <td>1371816915</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>3.19</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>196</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category    amt      lat      long  city_pop  job   unix_time  is_fraud\n",
       "0        10   2.86  33.9659  -80.9355    333497  275  1371816865         0\n",
       "1        10  29.84  40.3207 -110.4360       302  392  1371816873         0\n",
       "2         5  41.28  40.6729  -73.5365     34496  259  1371816893         0\n",
       "3         9  60.05  28.5697  -80.8191     54767  407  1371816915         0\n",
       "4        13   3.19  44.2529  -85.0170      1126  196  1371816917         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sampling Minority using SMOTE:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['is_fraud']) \n",
    "y = df['is_fraud'] \n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting to train-test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = LogisticRegression()\n",
    "model2 = DecisionTreeClassifier()\n",
    "model3 = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_train(model, x_train, y_train, x_test, y_test):\n",
    "    model.fit(x_train,y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    print('Accuracy Score: ',accuracy_score(y_test,y_pred))\n",
    "    print(classification_report(y_test,y_pred))\n",
    "    with open(str(model)[:3] + '_mdl.pkl', 'wb') as f:\n",
    "        pickle.dump(model,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.8793614234746873\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.89    110422\n",
      "           1       0.95      0.81      0.87    111008\n",
      "\n",
      "    accuracy                           0.88    221430\n",
      "   macro avg       0.89      0.88      0.88    221430\n",
      "weighted avg       0.89      0.88      0.88    221430\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_train(model1, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9909000587092986\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99    110422\n",
      "           1       0.99      0.99      0.99    111008\n",
      "\n",
      "    accuracy                           0.99    221430\n",
      "   macro avg       0.99      0.99      0.99    221430\n",
      "weighted avg       0.99      0.99      0.99    221430\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_train(model2, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XG Boost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9908639299101296\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99    110422\n",
      "           1       0.99      0.99      0.99    111008\n",
      "\n",
      "    accuracy                           0.99    221430\n",
      "   macro avg       0.99      0.99      0.99    221430\n",
      "weighted avg       0.99      0.99      0.99    221430\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_train(model3, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = model1.predict(X_test)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "y_pred3 = model3.predict(X_test)\n",
    "y_pred = [list(y_pred1), list(y_pred2), list(y_pred3)]\n",
    "n = len(y_pred[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_list = []\n",
    "for i in range(n):\n",
    "    prediction_list = [y_pred1[i], y_pred2[i], y_pred3[i]]\n",
    "    counter = Counter(prediction_list)\n",
    "    most_common_prediction = counter.most_common(1)\n",
    "    pred_list.append(most_common_prediction[0][0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.9900058709298649\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99    110422\n",
      "           1       0.99      0.99      0.99    111008\n",
      "\n",
      "    accuracy                           0.99    221430\n",
      "   macro avg       0.99      0.99      0.99    221430\n",
      "weighted avg       0.99      0.99      0.99    221430\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.array(pred_list)\n",
    "print('Accuracy Score: ',accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saved models:**\n",
    "\n",
    "Log_mdl.pkl - Logistic Regression\n",
    "Dec_mdl.pkl - Decision Tree\n",
    "XGB_mdl.pkl - XG Boosting Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefficients = model1.coef_[0]\n",
    "feature_importance = np.abs(coefficients)\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(X_test.columns)[sorted_idx])\n",
    "plt.title('Feature Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAAqmRbkLKILtJt6ZZpVtyl0GAAAAa6lmygHlLqFRWBkAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAFrYMyYMRk1alS5ywAAAIAGIQwAAACAgtngwoA77rgju+66azp27JguXbrkwAMPzHPPPZckqampSUVFRW644Ybstttuad26dXbeeec8/fTTuf/++zN48OC0a9cu++23X1599dUkyaRJk3L11Vfnt7/9bSoqKlJRUZGZM2eW8QwBAABg/WxwYcDbb7+db33rW3nggQdy9913p1mzZvnsZz+b2trauj4TJ07Md7/73Tz00ENp0aJFDj/88Jx66qm55JJL8r//+7959tlnc+aZZyZJTj755IwePTr77rtvFixYkAULFmTo0KGrPPbSpUuzePHiehsAAAB81LQodwEN7fOf/3y91z/96U/TtWvXPPHEE2nXrl2S/3zBHzlyZJLkxBNPzGGHHZa77747w4YNS5J85StfyYwZM5Ik7dq1S+vWrbN06dL06NHjA4993nnnZfLkyQ18RgAAANCwNriVAc8880wOO+ywbL755qmqqspmm22WJJk/f35dnx122KHu5+7duydJtt9++3ptCxcuXOtjT5gwIYsWLarbXnzxxXU8CwAAAGg8G9zKgIMOOih9+vTJtGnT0qtXr9TW1ma77bbLsmXL6vpstNFGdT9XVFSssu39lxWsqcrKylRWVq5H9QAAAND4Nqgw4LXXXsvcuXMzbdq07LbbbkmSv/zlL+s9bsuWLbNixYr1HgcAAAA+CjaoMKBTp07p0qVLfvSjH6Vnz56ZP39+xo8fv97jbrbZZrnzzjszd+7cdOnSJR06dKi3kgAAAAA+TjaoewY0a9Ys119/fR588MFst912+eY3v5kLLrhgvccdN25cttpqqwwePDhdu3bNrFmzGqBaAAAAKI+KUqlUKncRG6rFixenQ4cOqT7phjSrbFPucgAAAFhLNVMOKHcJa+W976GLFi1KVVXVavttUCsDAAAAgA8nDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIJpUe4CiuDxySNTVVVV7jIAAAAgiZUBAAAAUDjCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFEyLchdQBNtNvDPNKtuUuwzKrGbKAeUuAQAAIImVAQAAAFA4wgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACiYJg0DZs6cmYqKirz55puNdowxY8Zk1KhRjTY+AAAAfNy1aMqDDR06NAsWLEiHDh3We6yampr07ds3s2fPzsCBA+vaL7nkkpRKpfUeHwAAADZUTRoGtGzZMj169GjUYzRE0AAAAAAbsrW6TGCzzTbLxRdfXK9t4MCBmTRpUpKkoqIiP/7xj/PZz342bdq0Sf/+/XPLLbfU9f3vywSOPvro7LDDDlm6dGmSZNmyZRk0aFCOPPLID62lb9++SZJBgwaloqIiI0aMSLLyZQIjRozICSeckJNOOimdOnVK9+7dM23atLz99tsZO3Zs2rdvn379+uX222+vN/7jjz+e/fbbL+3atUv37t1zxBFH5F//+tdafFoAAADw0dTg9wyYPHlyRo8enUcffTT7779/vvSlL+X1119fZd9LL700b7/9dsaPH58kOf300/Pmm2/m8ssv/9Dj/P3vf0+S3HXXXVmwYEF+/etfr7bv1VdfnY033jh///vfc8IJJ+TrX/96DjnkkAwdOjQPPfRQ9tlnnxxxxBF55513kiRvvvlm9txzzwwaNCgPPPBA7rjjjrzyyisZPXr02n4cAAAA8JHT4JcJjBkzJocddliS5Nxzz82ll16av//979l3331X6tuuXbtce+21GT58eNq3b5+LL74499xzT6qqqj70OF27dk2SdOnS5UMvPfjEJz6R7373u0mSCRMmZMqUKdl4440zbty4JMmZZ56ZqVOn5tFHH82nPvWpXH755Rk0aFDOPffcujF++tOfprq6Ok8//XS23HLLVR5n6dKldasckmTx4sUfeh4AAADQ1Bp8ZcAOO+xQ93Pbtm1TVVWVhQsXrrb/kCFDcvLJJ+fss8/Ot7/97ey6664NXVK9mpo3b54uXbpk++23r2vr3r17ktTV+cgjj+See+5Ju3bt6ratt946SfLcc8+t9jjnnXdeOnToULdVV1c3+LkAAADA+lqrlQHNmjVb6U79y5cvr/d6o402qve6oqIitbW1qx2ztrY2s2bNSvPmzfPss8+uTTlrbFU1vb+toqKirpYkeeutt3LQQQfl+9///kpj9ezZc7XHmTBhQr71rW/VvV68eLFAAAAAgI+ctQoDunbtmgULFtS9Xrx4cebNm7deBVxwwQV56qmn8qc//SkjR47M9OnTM3bs2A99X8uWLZMkK1asWK/jr8qOO+6Ym266KZtttllatFjzj6iysjKVlZUNXg8AAAA0pLW6TGDPPffMNddck//93//NY489lqOOOirNmzdf54PPnj07Z555Zn784x9n2LBh+T//5//kxBNPzPPPP/+h7+3WrVtat25dd3O/RYsWrXMd/+24447L66+/nsMOOyz3339/nnvuudx5550ZO3Zso4QPAAAA0JTWKgyYMGFChg8fngMPPDAHHHBARo0alS222GKdDvzvf/87X/7ylzNmzJgcdNBBSZJjjz02e+yxR4444ogP/dLdokWLXHrppbnqqqvSq1evfOYzn1mnOlalV69emTVrVlasWJF99tkn22+/fU466aR07NgxzZo1+G0WAAAAoElVlP77JgA0mMWLF//nRoIn3ZBmlW3KXQ5lVjPlgHKXAAAAbODe+x66aNGiD3xSnz9zAwAAQMF8ZMOAc889t96j/d6/7bfffuUuDwAAAD621uppAk3pa1/7WkaPHr3Kfa1bt27iagAAAGDD8ZENAzp37pzOnTuXuwwAAADY4HxkLxMAAAAAGocwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYFqUu4AieHzyyFRVVZW7DAAAAEhiZQAAAAAUjjAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAgmlR7gKKYLuJd6ZZZZtyl0ETqplyQLlLAAAAWC0rAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAomI9EGDBp0qQMHDiw3GUAAABAIXwkwoCPmuXLl5e7BAAAAGg0DRYG1NbW5vzzz0+/fv1SWVmZTTfdNOecc06S5LTTTsuWW26ZNm3aZPPNN88ZZ5xR94V7xowZmTx5ch555JFUVFSkoqIiM2bMSJK8+eabOeaYY9K1a9dUVVVlzz33zCOPPFLvuN/73vfSrVu3tG/fPsccc0zGjx9fb5VBbW1tzjrrrGyyySaprKzMwIEDc8cdd9Ttr6mpSUVFRX75y19m+PDhadWqVX70ox+lqqoqN954Y71j3XzzzWnbtm2WLFnSUB8bAAAANLkWDTXQhAkTMm3atPzgBz/IrrvumgULFuSpp55KkrRv3z4zZsxIr1698thjj2XcuHFp3759Tj311Hzxi1/M448/njvuuCN33XVXkqRDhw5JkkMOOSStW7fO7bffng4dOuSqq67KXnvtlaeffjqdO3fOddddl3POOSdXXnllhg0bluuvvz4XXXRR+vbtW1fXJZdckosuuihXXXVVBg0alJ/+9Kc5+OCDM2fOnPTv37+u3/jx43PRRRdl0KBBadWqVR555JFMnz49X/jCF+r6vPe6ffv2DfWxAQAAQJOrKJVKpfUdZMmSJenatWsuv/zyHHPMMR/a/8ILL8z111+fBx54IMl/7hlw88035+GHH67r85e//CUHHHBAFi5cmMrKyrr2fv365dRTT82xxx6bT33qUxk8eHAuv/zyuv277rpr3nrrrbqxevfuneOOOy7f+c536vrssssu2XnnnXPFFVekpqYmffv2zcUXX5wTTzyxrs/f//73DB06NC+++GJ69uyZhQsXpnfv3rnrrrsyfPjwVZ7X0qVLs3Tp0rrXixcvTnV1dapPuiHNKtt86OfChqNmygHlLgEAACigxYsXp0OHDlm0aFGqqqpW269BLhN48skns3Tp0uy1116r3P/LX/4yw4YNS48ePdKuXbt897vfzfz58z9wzEceeSRvvfVWunTpknbt2tVt8+bNy3PPPZckmTt3bnbZZZd673v/68WLF+ell17KsGHD6vUZNmxYnnzyyXptgwcPXmmcbbfdNldffXWS5Nprr02fPn2y++67r7bm8847Lx06dKjbqqurP/AcAQAAoBwaJAxo3br1avfdd999+dKXvpT9998/v//97zN79uycfvrpWbZs2QeO+dZbb6Vnz555+OGH621z587NKaec0hBl19O2bduV2o455pi6+xdMnz49Y8eOTUVFxWrHmDBhQhYtWlS3vfjiiw1eJwAAAKyvBgkD+vfvn9atW+fuu+9ead+9996bPn365PTTT8/gwYPTv3//vPDCC/X6tGzZMitWrKjXtuOOO+bll19OixYt0q9fv3rbxhtvnCTZaqutcv/999d73/tfV1VVpVevXpk1a1a9PrNmzco222zzoef15S9/OS+88EIuvfTSPPHEEznqqKM+sH9lZWWqqqrqbQAAAPBR0yA3EGzVqlVOO+20nHrqqWnZsmWGDRuWV199te4mffPnz8/111+fnXfeObfeemt+85vf1Hv/Zpttlnnz5uXhhx/OJptskvbt22fvvffOkCFDMmrUqJx//vnZcsst89JLL+XWW2/NZz/72QwePDgnnHBCxo0bl8GDB2fo0KH55S9/mUcffTSbb7553dinnHJKJk6cmC222CIDBw7M9OnT8/DDD+e666770PPq1KlTPve5z+WUU07JPvvsk0022aQhPi4AAAAoqwZ7tOAZZ5yRb3/72znzzDMzYMCAfPGLX8zChQtz8MEH55vf/GaOP/74DBw4MPfee2/OOOOMeu/9/Oc/n3333Td77LFHunbtml/84hepqKjIbbfdlt133z1jx47NlltumUMPPTQvvPBCunfvniT50pe+lAkTJuTkk0/OjjvumHnz5mXMmDFp1apV3djf+MY38q1vfSvf/va3s/322+eOO+7ILbfcUu9JAh/kK1/5SpYtW5ajjz66oT4qAAAAKKsGeZrAR8mnP/3p9OjRI9dcc02DjHfNNdfkm9/8Zl566aW0bNlyrd773l0cPU2geDxNAAAAKIc1fZpAg1wmUC7vvPNOfvjDH2bkyJFp3rx5fvGLX+Suu+7KH//4xwYZe8GCBZkyZUq++tWvrnUQAAAAAB9VDXaZQDm8/1KCnXbaKb/73e9y0003Ze+9917vsc8///xsvfXW6dGjRyZMmNAA1QIAAMBHwwZ3mcBHicsEistlAgAAQDms6WUCH+uVAQAAAMDaEwYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKpkW5CyiCxyePTFVVVbnLAAAAgCRWBgAAAEDhCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAomBblLqAItpt4Z5pVtil3GU2qZsoB5S4BAACA1bAyAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAXTpGFATU1NKioq8vDDDzflYQEAAID3adIwoLq6OgsWLMh2222XJJk5c2YqKiry5ptvNmUZAAAAUGgtmvJgzZs3T48ePZrykAAAAMB/aZSVAbW1tTn//PPTr1+/VFZWZtNNN80555xT7zKBmpqa7LHHHkmSTp06paKiImPGjMnPfvazdOnSJUuXLq035qhRo3LEEUd86LEnTZqUgQMH5qqrrkp1dXXatGmT0aNHZ9GiRfXqO+uss7LJJpuksrIyAwcOzB133FG3/706r7/++gwdOjStWrXKdtttlz/96U8N9AkBAABA+TRKGDBhwoRMmTIlZ5xxRp544on8/Oc/T/fu3ev1qa6uzk033ZQkmTt3bhYsWJBLLrkkhxxySFasWJFbbrmlru/ChQtz66235uijj16j4z/77LO54YYb8rvf/S533HFHZs+enf/5n/+p23/JJZfkoosuyoUXXphHH300I0eOzMEHH5xnnnmm3jinnHJKvv3tb2f27NkZMmRIDjrooLz22murPe7SpUuzePHiehsAAAB81DR4GLBkyZJccsklOf/883PUUUdliy22yK677ppjjjmmXr/mzZunc+fOSZJu3bqlR48e6dChQ1q3bp3DDz8806dPr+t77bXXZtNNN82IESPWqIZ///vf+dnPfpaBAwdm9913z2WXXZbrr78+L7/8cpLkwgsvzGmnnZZDDz00W221Vb7//e9n4MCBufjii+uNc/zxx+fzn/98BgwYkKlTp6ZDhw75yU9+strjnnfeeenQoUPdVl1dvUb1AgAAQFNq8DDgySefzNKlS7PXXnut8xjjxo3LH/7wh/zzn/9MksyYMSNjxoxJRUXFGr1/0003Te/eveteDxkyJLW1tZk7d24WL16cl156KcOGDav3nmHDhuXJJ5+s1zZkyJC6n1u0aJHBgwev1Of9JkyYkEWLFtVtL7744hrVCwAAAE2pwW8g2Lp16/UeY9CgQfnEJz6Rn/3sZ9lnn30yZ86c3HrrrQ1QXeOqrKxMZWVlucsAAACAD9TgKwP69++f1q1b5+677/7Qvi1btkySrFixYqV9xxxzTGbMmJHp06dn7733Xqsl9/Pnz89LL71U9/qvf/1rmjVrlq222ipVVVXp1atXZs2aVe89s2bNyjbbbFOv7a9//Wvdz++++24efPDBDBgwYI3rAAAAgI+iBl8Z0KpVq5x22mk59dRT07JlywwbNiyvvvpq5syZs9KlA3369ElFRUV+//vfZ//990/r1q3Trl27JMnhhx+ek08+OdOmTcvPfvazta7hqKOOyoUXXpjFixfnG9/4RkaPHl33WMNTTjklEydOzBZbbJGBAwdm+vTpefjhh3PdddfVG+eKK65I//79M2DAgPzgBz/IG2+8scY3MQQAAICPqgYPA5LkjDPOSIsWLXLmmWfmpZdeSs+ePfO1r31tpX69e/fO5MmTM378+IwdOzZHHnlkZsyYkSTp0KFDPv/5z+fWW2/NqFGj1ur4/fr1y+c+97nsv//+ef3113PggQfmyiuvrNv/jW98I4sWLcq3v/3tLFy4MNtss01uueWW9O/fv944U6ZMyZQpU/Lwww+nX79+ueWWW7Lxxhuv9ecBAAAAHyUVpVKpVO4iVmevvfbKtttum0svvXSN3zNp0qTcfPPNefjhh9f5uDU1Nenbt29mz56dgQMHrvM4ixcv/s9TBU66Ic0q26zzOB9HNVMOKHcJAAAAhfPe99BFixalqqpqtf0aZWXA+nrjjTcyc+bMzJw5s95f9AEAAID195EMAwYNGpQ33ngj3//+97PVVlvV27ftttvmhRdeWOX7rrrqqqYoDwAAAD7WPpJhQE1NzWr33XbbbVm+fPkq93Xv3j3t27fPpEmT1uv4m222WT7CV08AAADAevlIhgEfpE+fPuUuAQAAAD7WmpW7AAAAAKBpCQMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAAqmRbkLKILHJ49MVVVVucsAAACAJFYGAAAAQOEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDAtyl1AEWw38c40q2xT7jLWS82UA8pdAgAAAA3EygAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACqaQYcCIESNy0kknlbsMAAAAKItChgFrY+bMmamoqMibb75Z7lIAAACgQQgDAAAAoGAKHwZcc801GTx4cNq3b58ePXrk8MMPz8KFC5MkNTU12WOPPZIknTp1SkVFRcaMGVPGagEAAGD9FT4MWL58ec4+++w88sgjufnmm1NTU1P3hb+6ujo33XRTkmTu3LlZsGBBLrnkktWOtXTp0ixevLjeBgAAAB81LcpdQLkdffTRdT9vvvnmufTSS7PzzjvnrbfeSrt27dK5c+ckSbdu3dKxY8cPHOu8887L5MmTG7NcAAAAWG+FXxnw4IMP5qCDDsqmm26a9u3bZ/jw4UmS+fPnr/VYEyZMyKJFi+q2F198saHLBQAAgPVW6JUBb7/9dkaOHJmRI0fmuuuuS9euXTN//vyMHDkyy5YtW+vxKisrU1lZ2QiVAgAAQMMpdBjw1FNP5bXXXsuUKVNSXV2dJHnggQfq9WnZsmWSZMWKFU1eHwAAADSGQl8msOmmm6Zly5a57LLL8vzzz+eWW27J2WefXa9Pnz59UlFRkd///vd59dVX89Zbb5WpWgAAAGgYhQ4DunbtmhkzZuRXv/pVttlmm0yZMiUXXnhhvT69e/fO5MmTM378+HTv3j3HH398maoFAACAhlFRKpVK5S5iQ7V48eJ06NAh1SfdkGaVbcpdznqpmXJAuUsAAADgQ7z3PXTRokWpqqpabb9CrwwAAACAIhIGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwbQodwFF8Pjkkamqqip3GQAAAJDEygAAAAAoHGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKpkW5CyiC7SbemWaVbcpdxjqrmXJAuUsAAACgAVkZAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgILZIMKAESNG5KSTTip3GQAAAPCxsEGEAQAAAMCaEwYAAABAwWxwYcAbb7yRI488Mp06dUqbNm2y33775ZlnnqnbP2PGjHTs2DF33nlnBgwYkHbt2mXffffNggUL6vq8++67+cY3vpGOHTumS5cuOe2003LUUUdl1KhRZTgjAAAAaFgbXBgwZsyYPPDAA7nlllty3333pVQqZf/998/y5cvr+rzzzju58MILc8011+TPf/5z5s+fn5NPPrlu//e///1cd911mT59embNmpXFixfn5ptvLsPZAAAAQMNrUe4CGtIzzzyTW265JbNmzcrQoUOTJNddd12qq6tz880355BDDkmSLF++PD/84Q+zxRZbJEmOP/74nHXWWXXjXHbZZZkwYUI++9nPJkkuv/zy3HbbbR96/KVLl2bp0qV1rxcvXtxg5wYAAAANZYNaGfDkk0+mRYsW+eQnP1nX1qVLl2y11VZ58skn69ratGlTFwQkSc+ePbNw4cIkyaJFi/LKK69kl112qdvfvHnz7LTTTh96/PPOOy8dOnSo26qrqxvitAAAAKBBbVBhwJraaKON6r2uqKhIqVRa73EnTJiQRYsW1W0vvvjieo8JAAAADW2DCgMGDBiQd999N3/729/q2l577bXMnTs322yzzRqN0aFDh3Tv3j33339/XduKFSvy0EMPfeh7KysrU1VVVW8DAACAj5oN6p4B/fv3z2c+85mMGzcuV111Vdq3b5/x48end+/e+cxnPrPG45xwwgk577zz0q9fv2y99da57LLL8sYbb6SioqIRqwcAAICmsUGtDEiS6dOnZ6eddsqBBx6YIUOGpFQq5bbbblvp0oAPctppp+Wwww7LkUcemSFDhqRdu3YZOXJkWrVq1YiVAwAAQNOoKDXExfIbuNra2gwYMCCjR4/O2WefvcbvW7x48X9uJHjSDWlW2aYRK2xcNVMOKHcJAAAArIH3vocuWrToAy9d36AuE2goL7zwQv7whz9k+PDhWbp0aS6//PLMmzcvhx9+eLlLAwAAgPW2wV0m0BCaNWuWGTNmZOedd86wYcPy2GOP5a677sqAAQPKXRoAAACsNysDVqG6ujqzZs0qdxkAAADQKKwMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAF06LcBRTB45NHpqqqqtxlAAAAQBIrAwAAAKBwhAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACiYFuUuoAi2m3hnmlW2KXcZa6VmygHlLgEAAIBGYmUAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACqYwYcCYMWMyatSoNeo7c+bMVFRU5M0332zUmgAAAKAcWpS7gKZyySWXpFQqlbsMAAAAKLvChAEdOnQodwkAAADwkVDIywSWLl2ab3zjG+nWrVtatWqVXXfdNffff/9K75k1a1Z22GGHtGrVKp/61Kfy+OOPN3HVAAAA0PAKEwa836mnnpqbbropV199dR566KH069cvI0eOzOuvv16v3ymnnJKLLroo999/f7p27ZqDDjooy5cvL1PVAAAA0DAKFwa8/fbbmTp1ai644ILst99+2WabbTJt2rS0bt06P/nJT+r1nThxYj796U9n++23z9VXX51XXnklv/nNb1Y79tKlS7N48eJ6GwAAAHzUFC4MeO6557J8+fIMGzasrm2jjTbKLrvskieffLJe3yFDhtT93Llz52y11VYr9Xm/8847Lx06dKjbqqurG/4EAAAAYD0VLgxoTBMmTMiiRYvqthdffLHcJQEAAMBKChcGbLHFFmnZsmVmzZpV17Z8+fLcf//92Wabber1/etf/1r38xtvvJGnn346AwYMWO3YlZWVqaqqqrcBAADAR01hHi34nrZt2+brX/96TjnllHTu3Dmbbrppzj///Lzzzjv5yle+Uq/vWWedlS5duqR79+45/fTTs/HGG9c9kQAAAAA+rgoXBiTJlClTUltbmyOOOCJLlizJ4MGDc+edd6ZTp04r9TvxxBPzzDPPZODAgfnd736Xli1blqlqAAAAaBiFCQOWLl2adu3aJUlatWqVSy+9NJdeeukq+44YMSKlUilJcuCBBzZZjQAAANAUNvh7Brz77rt54oknct9992XbbbctdzkAAABQdht8GPD4449n8ODB2XbbbfO1r32t3OUAAABA2W3wlwkMHDgw77zzTrnLAAAAgI+MDX5lAAAAAFCfMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwLcpdQBE8Pnlkqqqqyl0GAAAAJLEyAAAAAApHGAAAAAAFIwwAAACAghEGAAAAQMEIAwAAAKBghAEAAABQMMIAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMAAAAgIIRBgAAAEDBCAMAAACgYIQBAAAAUDDCAAAAACgYYQAAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCaVHuAjZkpVIpSbJ48eIyVwIAAEARvPf9873vo6sjDGhEr732WpKkurq6zJUAAABQJEuWLEmHDh1Wu18Y0Ig6d+6cJJk/f/4H/hIonsWLF6e6ujovvvhiqqqqyl0OHyHmBqtjbvBBzA9Wx9xgdcyNDVepVMqSJUvSq1evD+wnDGhEzZr955YMHTp08D8wVqmqqsrcYJXMDVbH3OCDmB+sjrnB6pgbG6Y1+WO0GwgCAABAwQgDAAAAoGCEAY2osrIyEydOTGVlZblL4SPG3GB1zA1Wx9zgg5gfrI65weqYG1SUPux5AwAAAMAGxcoAAAAAKBhhAAAAABSMMAAAAAAKRhgAAAAABSMMWE9XXHFFNttss7Rq1Sqf/OQn8/e///0D+//qV7/K1ltvnVatWmX77bfPbbfd1kSV0tTWZm7MmTMnn//857PZZpuloqIiF198cdMVSpNbm7kxbdq07LbbbunUqVM6deqUvffe+0P/neHja23mxq9//esMHjw4HTt2TNu2bTNw4MBcc801TVgtTW1t/5vjPddff30qKioyatSoxi2QslmbuTFjxoxUVFTU21q1atWE1dKU1vbfjTfffDPHHXdcevbsmcrKymy55Za+r2zAhAHr4Ze//GW+9a1vZeLEiXnooYfyiU98IiNHjszChQtX2f/ee+/NYYcdlq985SuZPXt2Ro0alVGjRuXxxx9v4sppbGs7N955551svvnmmTJlSnr06NHE1dKU1nZuzJw5M4cddljuueee3Hfffamurs4+++yTf/7zn01cOY1tbedG586dc/rpp+e+++7Lo48+mrFjx2bs2LG58847m7hymsLazo/31NTU5OSTT85uu+3WRJXS1NZlblRVVWXBggV12wsvvNCEFdNU1nZuLFu2LJ/+9KdTU1OTG2+8MXPnzs20adPSu3fvJq6cJlNine2yyy6l4447ru71ihUrSr169Sqdd955q+w/evTo0gEHHFCv7ZOf/GTpq1/9aqPWSdNb27nxfn369Cn94Ac/aMTqKKf1mRulUqn07rvvltq3b1+6+uqrG6tEymR950apVCoNGjSo9N3vfrcxyqPM1mV+vPvuu6WhQ4eWfvzjH5eOOuqo0mc+85kmqJSmtrZzY/r06aUOHTo0UXWU09rOjalTp5Y233zz0rJly5qqRMrMyoB1tGzZsjz44IPZe++969qaNWuWvffeO/fdd98q33PffffV658kI0eOXG1/Pp7WZW5QDA0xN955550sX748nTt3bqwyKYP1nRulUil333135s6dm913370xS6UM1nV+nHXWWenWrVu+8pWvNEWZlMG6zo233norffr0SXV1dT7zmc9kzpw5TVEuTWhd5sYtt9ySIUOG5Ljjjkv37t2z3Xbb5dxzz82KFSuaqmyamDBgHf3rX//KihUr0r1793rt3bt3z8svv7zK97z88str1Z+Pp3WZGxRDQ8yN0047Lb169VopWOTjbV3nxqJFi9KuXbu0bNkyBxxwQC677LJ8+tOfbuxyaWLrMj/+8pe/5Cc/+UmmTZvWFCVSJusyN7baaqv89Kc/zW9/+9tce+21qa2tzdChQ/OPf/yjKUqmiazL3Hj++edz4403ZsWKFbnttttyxhln5KKLLsr3vve9piiZMmhR7gIAWDNTpkzJ9ddfn5kzZ7rZE0mS9u3b5+GHH85bb72Vu+++O9/61rey+eabZ8SIEeUujTJasmRJjjjiiEybNi0bb7xxucvhI2bIkCEZMmRI3euhQ4dmwIABueqqq3L22WeXsTLKrba2Nt26dcuPfvSjNG/ePDvttFP++c9/5oILLsjEiRPLXR6NQBiwjjbeeOM0b948r7zySr32V155ZbU3gOvRo8da9efjaV3mBsWwPnPjwgsvzJQpU3LXXXdlhx12aMwyKYN1nRvNmjVLv379kiQDBw7Mk08+mfPOO08YsIFZ2/nx3HPPpaamJgcddFBdW21tbZKkRYsWmTt3brbYYovGLZom0RD/zbHRRhtl0KBBefbZZxujRMpkXeZGz549s9FGG6V58+Z1bQMGDMjLL7+cZcuWpWXLlo1aM03PZQLrqGXLltlpp51y991317XV1tbm7rvvrpe2vt+QIUPq9U+SP/7xj6vtz8fTuswNimFd58b555+fs88+O3fccUcGDx7cFKXSxBrq343a2tosXbq0MUqkjNZ2fmy99dZ57LHH8vDDD9dtBx98cPbYY488/PDDqa6ubsryaUQN8W/HihUr8thjj6Vnz56NVSZlsC5zY9iwYXn22WfrwsMkefrpp9OzZ09BwIaq3Hcw/Di7/vrrS5WVlaUZM2aUnnjiidKxxx5b6tixY+nll18ulUql0hFHHFEaP358Xf9Zs2aVWrRoUbrwwgtLTz75ZGnixImljTbaqPTYY4+V6xRoJGs7N5YuXVqaPXt2afbs2aWePXuWTj755NLs2bNLzzzzTLlOgUaytnNjypQppZYtW5ZuvPHG0oIFC+q2JUuWlOsUaCRrOzfOPffc0h/+8IfSc889V3riiSdKF154YalFixaladOmlesUaERrOz/+m6cJbLjWdm5Mnjy5dOedd5aee+650oMPPlg69NBDS61atSrNmTOnXKdAI1nbuTF//vxS+/btS8cff3xp7ty5pd///velbt26lb73ve+V6xRoZC4TWA9f/OIX8+qrr+bMM8/Myy+/nIEDB+aOO+6ou1HH/Pnz06zZ/1t8MXTo0Pz85z/Pd7/73XznO99J//79c/PNN2e77bYr1ynQSNZ2brz00ksZNGhQ3esLL7wwF154YYYPH56ZM2c2dfk0orWdG1OnTs2yZcvyhS98od44EydOzKRJk5qydBrZ2s6Nt99+O//zP/+Tf/zjH2ndunW23nrrXHvttfniF79YrlOgEa3t/KA41nZuvPHGGxk3blxefvnldOrUKTvttFPuvffebLPNNuU6BRrJ2s6N6urq3HnnnfnmN7+ZHXbYIb17986JJ56Y0047rVynQCOrKJVKpXIXAQAAADQdETIAAAAUjDAAAAAACkYYAAAAAAUjDAAAAICCEQYAAABAwQgDAAAAoGCEAQAAAFAwwgAAAAAoGGEAAAAAFIwwAAAAAApGGAAAAAAFIwwAAACAgvn/AG79/sV40mMDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = model2.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(X_test.columns)[sorted_idx])\n",
    "plt.title('Feature Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = model3.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(X_test.columns)[sorted_idx])\n",
    "plt.title('Feature Importance')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}