Predictive Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cwd = os.getcwd()\n",
    "\n",
    "df = pd.read_csv(cwd + \"/filtered_customer_booking.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
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       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
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       "  </thead>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49982 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0                   2      Internet  RoundTrip            262              19   \n",
       "1                   1      Internet  RoundTrip            112              20   \n",
       "2                   2      Internet  RoundTrip            243              22   \n",
       "3                   1      Internet  RoundTrip             96              31   \n",
       "4                   2      Internet  RoundTrip             68              22   \n",
       "...               ...           ...        ...            ...             ...   \n",
       "49977               2      Internet  RoundTrip             27               6   \n",
       "49978               1      Internet  RoundTrip            111               6   \n",
       "49979               1      Internet  RoundTrip             24               6   \n",
       "49980               1      Internet  RoundTrip             15               6   \n",
       "49981               1      Internet  RoundTrip             19               6   \n",
       "\n",
       "       flight_hour  flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0                7           6  AKLDEL    New Zealand                    1   \n",
       "1                3           6  AKLDEL    New Zealand                    0   \n",
       "2               17           3  AKLDEL          India                    1   \n",
       "3                4           6  AKLDEL    New Zealand                    0   \n",
       "4               15           3  AKLDEL          India                    1   \n",
       "...            ...         ...     ...            ...                  ...   \n",
       "49977            9           6  PERPNH      Australia                    1   \n",
       "49978            4           7  PERPNH      Australia                    0   \n",
       "49979           22           6  PERPNH      Australia                    0   \n",
       "49980           11           1  PERPNH      Australia                    1   \n",
       "49981           10           4  PERPNH      Australia                    0   \n",
       "\n",
       "       wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                         0                      0             5.52   \n",
       "1                         0                      0             5.52   \n",
       "2                         1                      0             5.52   \n",
       "3                         0                      1             5.52   \n",
       "4                         0                      1             5.52   \n",
       "...                     ...                    ...              ...   \n",
       "49977                     0                      1             5.62   \n",
       "49978                     0                      0             5.62   \n",
       "49979                     0                      1             5.62   \n",
       "49980                     0                      1             5.62   \n",
       "49981                     1                      0             5.62   \n",
       "\n",
       "       booking_complete  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     0  \n",
       "...                 ...  \n",
       "49977                 0  \n",
       "49978                 0  \n",
       "49979                 0  \n",
       "49980                 0  \n",
       "49981                 0  \n",
       "\n",
       "[49982 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "#create instance of one hot encoder\n",
    "encoder = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "#one hot encode Sales Channel\n",
    "encoder_df = pd.DataFrame(encoder.fit_transform(df[[\"sales_channel\"]]).toarray())\n",
    "encoder_df = encoder_df.rename(columns={0:'Internet', 1:'Mobile'})\n",
    "df_final = df_final.join(encoder_df)\n",
    "\n",
    "#one hot encode trip type\n",
    "encoder_df = pd.DataFrame(encoder.fit_transform(df[[\"trip_type\"]]).toarray())\n",
    "encoder_df = encoder_df.rename(columns={0:'RoundTRip', 1:'OneWayTrip',2:'CircleTrip'})\n",
    "df_final = df_final.join(encoder_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop categorical columns now\n",
    "df_final.drop(['sales_channel', 'trip_type','booking_origin', 'route'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#store the label for supervised learning\n",
    "label = df['booking_complete']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.drop('booking_complete', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49982 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  flight_day  \\\n",
       "0                   2            262              19            7           6   \n",
       "1                   1            112              20            3           6   \n",
       "2                   2            243              22           17           3   \n",
       "3                   1             96              31            4           6   \n",
       "4                   2             68              22           15           3   \n",
       "...               ...            ...             ...          ...         ...   \n",
       "49977               2             27               6            9           6   \n",
       "49978               1            111               6            4           7   \n",
       "49979               1             24               6           22           6   \n",
       "49980               1             15               6           11           1   \n",
       "49981               1             19               6           10           4   \n",
       "\n",
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "0                        1                     0                      0   \n",
       "1                        0                     0                      0   \n",
       "2                        1                     1                      0   \n",
       "3                        0                     0                      1   \n",
       "4                        1                     0                      1   \n",
       "...                    ...                   ...                    ...   \n",
       "49977                    1                     0                      1   \n",
       "49978                    0                     0                      0   \n",
       "49979                    0                     0                      1   \n",
       "49980                    1                     0                      1   \n",
       "49981                    0                     1                      0   \n",
       "\n",
       "       flight_duration  Internet  Mobile  RoundTRip  OneWayTrip  CircleTrip  \n",
       "0                 5.52       1.0     0.0        0.0         0.0         1.0  \n",
       "1                 5.52       1.0     0.0        0.0         0.0         1.0  \n",
       "2                 5.52       1.0     0.0        0.0         0.0         1.0  \n",
       "3                 5.52       1.0     0.0        0.0         0.0         1.0  \n",
       "4                 5.52       1.0     0.0        0.0         0.0         1.0  \n",
       "...                ...       ...     ...        ...         ...         ...  \n",
       "49977             5.62       1.0     0.0        0.0         0.0         1.0  \n",
       "49978             5.62       1.0     0.0        0.0         0.0         1.0  \n",
       "49979             5.62       1.0     0.0        0.0         0.0         1.0  \n",
       "49980             5.62       1.0     0.0        0.0         0.0         1.0  \n",
       "49981             5.62       1.0     0.0        0.0         0.0         1.0  \n",
       "\n",
       "[49982 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#create a standard scaler object\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#fit and transform the data\n",
    "scaled_df = scaler.fit_transform(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a dataframe of scled data\n",
    "scaled_df = pd.DataFrame(scaled_df, columns = df_final.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the labels back to the dataframe\n",
    "scaled_df['label']  = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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       "    .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>num_passengers</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>Internet</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>RoundTRip</th>\n",
       "      <th>OneWayTrip</th>\n",
       "      <th>CircleTrip</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.400769</td>\n",
       "      <td>1.971093</td>\n",
       "      <td>-0.119401</td>\n",
       "      <td>-0.381588</td>\n",
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       "      <td>-0.863557</td>\n",
       "      <td>-1.174049</td>\n",
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       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>0.302987</td>\n",
       "      <td>-0.089895</td>\n",
       "      <td>-1.120618</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
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       "      <td>-1.174049</td>\n",
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       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.400769</td>\n",
       "      <td>1.759799</td>\n",
       "      <td>-0.030885</td>\n",
       "      <td>1.465988</td>\n",
       "      <td>-0.408618</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>1.538334</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-1.174049</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>0.125056</td>\n",
       "      <td>0.234662</td>\n",
       "      <td>-0.935861</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.174049</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.400769</td>\n",
       "      <td>-0.186323</td>\n",
       "      <td>-0.030885</td>\n",
       "      <td>1.096473</td>\n",
       "      <td>-0.408618</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.174049</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49977</th>\n",
       "      <td>0.400769</td>\n",
       "      <td>-0.642272</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>-0.012073</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49978</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>0.291867</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>-0.935861</td>\n",
       "      <td>1.598707</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49979</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.675634</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>2.389776</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49980</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.775721</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>0.357443</td>\n",
       "      <td>-1.412280</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49981</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.731238</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>0.172685</td>\n",
       "      <td>0.093214</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>1.538334</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49982 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  flight_day  \\\n",
       "0            0.400769       1.971093       -0.119401    -0.381588    1.096876   \n",
       "1           -0.579424       0.302987       -0.089895    -1.120618    1.096876   \n",
       "2            0.400769       1.759799       -0.030885     1.465988   -0.408618   \n",
       "3           -0.579424       0.125056        0.234662    -0.935861    1.096876   \n",
       "4            0.400769      -0.186323       -0.030885     1.096473   -0.408618   \n",
       "...               ...            ...             ...          ...         ...   \n",
       "49977        0.400769      -0.642272       -0.502969    -0.012073    1.096876   \n",
       "49978       -0.579424       0.291867       -0.502969    -0.935861    1.598707   \n",
       "49979       -0.579424      -0.675634       -0.502969     2.389776    1.096876   \n",
       "49980       -0.579424      -0.775721       -0.502969     0.357443   -1.412280   \n",
       "49981       -0.579424      -0.731238       -0.502969     0.172685    0.093214   \n",
       "\n",
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "0                 0.703587             -0.650054              -0.863557   \n",
       "1                -1.421288             -0.650054              -0.863557   \n",
       "2                 0.703587              1.538334              -0.863557   \n",
       "3                -1.421288             -0.650054               1.158002   \n",
       "4                 0.703587             -0.650054               1.158002   \n",
       "...                    ...                   ...                    ...   \n",
       "49977             0.703587             -0.650054               1.158002   \n",
       "49978            -1.421288             -0.650054              -0.863557   \n",
       "49979            -1.421288             -0.650054               1.158002   \n",
       "49980             0.703587             -0.650054               1.158002   \n",
       "49981            -1.421288              1.538334              -0.863557   \n",
       "\n",
       "       flight_duration  Internet    Mobile  RoundTRip  OneWayTrip  CircleTrip  \\\n",
       "0            -1.174049  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "1            -1.174049  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "2            -1.174049  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "3            -1.174049  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "4            -1.174049  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "...                ...       ...       ...        ...         ...         ...   \n",
       "49977        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "49978        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "49979        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "49980        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "49981        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "\n",
       "       label  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "...      ...  \n",
       "49977      0  \n",
       "49978      0  \n",
       "49979      0  \n",
       "49980      0  \n",
       "49981      0  \n",
       "\n",
       "[49982 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = scaled_df.corr()\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "#plot the heatmap\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = scaled_df.iloc[:,:-1]\n",
    "y = scaled_df['label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.to_numpy(), y.to_numpy(), test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from sklearn.model_selection import GridSearchCV,  RepeatedStratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fit_predict(model, X, y, X_predict):\n",
    "    model.fit(X, y)\n",
    "    return model.predict(X_predict)\n",
    "\n",
    "def acc_score(y_true, y_pred):\n",
    "    return accuracy_score(y_true, y_pred)\n",
    "\n",
    "def pre_score(y_true, y_pred):\n",
    "    return precision_score(y_true, y_pred)\n",
    "\n",
    "def f_score(y_true, y_pred):\n",
    "    return f1_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf = RandomForestClassifier(max_depth =50 , min_samples_split=5,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy, precision and f1-score for training data are 0.93, 1.0 and 0.72 respectively\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = model_fit_predict(clf_rf, X_train, y_train, X_train)\n",
    "set(y_pred_train)\n",
    "\n",
    "#f1 score for training data\n",
    "f1 = round(f1_score(y_train, y_pred_train),2) \n",
    "\n",
    "#accuracy score for training data\n",
    "acc = round(accuracy_score(y_train, y_pred_train),2) \n",
    "\n",
    "#precision score for training data\n",
    "pre = round(precision_score(y_train, y_pred_train),2) \n",
    "\n",
    "print(f\"Accuracy, precision and f1-score for training data are {acc}, {pre} and {f1} respectively\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVCpdurRlGNP69euVO3du5cmTJ8blW+/fv69BgwZp9erVevTokbJmzSpvb2/Z2dkZ7e/vfz8vEhAQoFmzZsnFxcXSUwXgzWESNYDncnZ2Vt++fXXx4kUNGTLEsnzfvn0KDAxUhQoV9MMPP2j37t2aM2fOP7oyjJOTk65du2a1LDo62mrCapo0aXT16tUYz3267NkAEh+efmB9ttandbyshtSpU0uSRo8ereXLl8f4eTqp9ul8gs2bN2vdunXq2rWrDhw48NKhGaVKldKxY8eee34kadu2bfL19Y0x+VWS1qxZo+HDh6tNmzb66aeftHPnTk2dOjXGZWvd3Nw0atQo7dq1S0uWLFFAQICWLl2qefPm/eO6X8bR0VE5c+Z87rlavnz5c4c9vUxsztHrvM6mnu0NebbHIkmSJGrUqJFWrlypnTt3atiwYYqMjFSnTp2ee7nUNGnSvLBeKW7/PqpUqWK5GtN333333MnTkjRkyBBt2LBBQUFBOnDggDZt2qRRo0ZZ9Q6+jqioKPXv31/Zs2fXnTt3NGrUqDjZLgBzBAgAL/R02MratWst4+IPHjyoqKgoderUyTIk6fHjx5arJz3tXTBRokQJ/fDDD5YhTdKTOQp/H+ZQtGhRnT9/XgcPHrR67jfffCN7e/sYEzjjw7vvvisHBwetXbvWavm+fft04cIFy/yAFz3X3t5ely9flqenp+XHzs5OY8eOVXh4uM6fP68yZcpYblrn5uamNm3aqGTJkpZvz5+9gpAkNWrUSPb29hoyZMhzP5iOHz9eadOmVenSpWM8d//+/UqdOrVat25t6V26c+eO9u/fb3kN169fr+LFi+vq1auytbWVt7e3+vfvr9SpU+vChQtGdcdWsWLFdPHiRaVPn97qfO3cuVMzZsx47nl4mdico9d5nU2kSpVKly5dslr296slSVL9+vU1ePBgSVL69OlVq1YtNWrUSLdu3bLqTXiqaNGi2rJli9W6x48f69tvv5Wnp2ecXprWz89PDg4OWrBggQ4dOvTCALF//375+PioXLlyll6io0eP6saNG1b/PjzttYmtuXPn6sCBAxo6dKg+/fRTLV68OMZVugDEL4YwAXipL7/8UtWrV9fgwYO1atUqywf2gQMHWi5/uXDhQssdfe/evWt806eOHTtq06ZNatWqlVq3bq0bN24oKCjIak5ErVq1tGjRInXs2FGdO3dW1qxZtXnzZq1YsUKffPKJ5Rv++OTk5KS2bdtq4sSJsre3l5+fn8LDwxUcHKzcuXMrICDghc9NmzatWrdureDgYN2+fVs+Pj66fPmygoODZWNjo7x588rR0VGZMmXS4MGDdfv2bWXPnl1Hjx7Vtm3b9PHHH0uSZR7H1q1blSZNGuXNm1dZs2ZV//791atXLzVq1Ej169eXq6urzp07p9mzZyssLEwzZ85U0qRJY9Tl5eWlxYsXa/jw4fLz89OVK1c0c+ZMXbt2zfJNfOHChRUVFaWOHTuqbdu2SpkypdatW6eIiAhVqFBBWbJkeWXdsVWrVi0tWLBALVq0ULt27eTq6qoff/xR06dPV+PGja3eGyZic46SJk36j19nE35+ftq8ebOGDRsmf39/7du3L8alR4sWLapZs2bJ2dlZ3t7eunz5smbPnq1ixYopXbp0MXosPvnkE/3www9q2rSp2rZtK3t7ey1YsEBhYWGaMWPGa9X7rBQpUqhMmTKaNm2avLy8LJPvn+Xl5aV169Zp8eLFcnd314kTJzR58mTZ2NhYfVmQOnVqHThwQHv37n3hpOlnnTlzRkFBQapbt66KFi2qwoULa82aNerVq5fWrFljNPwJwOsjQAB4KTc3NzVp0kSzZs3S4sWL1bhxY/Xt21ezZ8/W+vXr5ezsLB8fH02YMEEdO3bU/v37jW84lzNnTi1YsEDDhw9Xly5dlD59egUGBmr48OGWNsmTJ9f8+fM1ZswYy4dwNzc3DRkyRHXq1Imvw46hU6dOcnZ21oIFC7R06VI5OTmpUqVK+uyzz145Fv+zzz6Ti4uLFi1apBkzZihNmjQqUaKEunbtagkGEyZM0NixYxUcHKw///xTrq6u+uSTTyxzJ/LkyaNq1app4cKF2r59u+Vb8oCAAOXIkUNz585VUFCQrl+/LhcXFxUuXFghISFyd3d/bk0BAQEKDw/XihUrtGjRImXMmFFlypRRw4YN1adPH/32229yd3fXjBkzFBwcrF69eunevXvKkyePQkJCLOPxX1V3bKVIkUILFy7UmDFjNGrUKEVERChLlizq1q2bWrZs+Y+2GZtz9Dqv86vUrl1b586d06pVq7RkyRIVLVpU48ePV4MGDSxtPv30Uzk4OGjFihWaOHGiHB0d5e/v/8IrJeXJk0eLFi3S2LFj1bNnT9nY2MjLy0vz5s0z/lAeG1WqVNH69eufe/Wlp3r06GG5AENkZKSyZs2q9u3b6/Tp09q8ebMeP34sW1tbtWvXTpMmTVKbNm303XffvXLfUVFR6tmzp2XYnPSkZ27QoEGqU6eORowYoYEDB8bZsQJ4MZvo153xBQAAAOA/gzkQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgLG3/j4QBw8eVHR0dKxvLgQAAADgiYcPH8rGxkbe3t6vbPvWB4jo6Gg9fPhQFy5cSOhSAADPkSNHjoQuAQDwCrG5NdxbHyDs7e114cIFfRjcLqFLAQA8R3RouCTp/uO7CVwJAOBFfj32m3Fb5kAAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAF6TjY2NutX5WKfmbNfdtad1aMpGNfQPsGrTrMJHOjJtk+6uPa0Ts7apU82WMbZT1ecD7Q5Zq3vfnlbYor0a266fUiZLEaPd033d+/bJtjpUb/bC2myT2Gp3yFptGf3V6x8oAEBRUVGaPnWGinoXk3OaDMqXp4C+6Npdt27dSujSgDcmwQPEjh07VLt2bb377rvy9/fXzJkzFR0dndBlAcYGNvtcQ1sGaua6JarWp5k2HdyuhT1DVN+vhiSpVeUGmvPFOH27Z7Oq9WmmuaFfaWy7vurZ4BPLNmr6VtI3A2fr9r07qju4vT6b3F/+hXz1/cilsk1ia2k3sk1vy76q9GqqNbtCNbHTELWp0ui5tfWo31HF8haK1+MHgP+SMaPGqkvnrqpUuZKWrViiz7p+qkULFqvBRw35/IL/DJvoBHy3Hzp0SI0bN1blypX14Ycfav/+/Zo6daq6du2qtm3bGm3jyJEjOnv2rD4MbhfP1QIxJU+aTFe++lmT18xX9+mDLcu3jP5KSe0dVPLTGvpt3k7t//WI6g76//fo7C/GquJ7ZZS5/nuSpENTNsrO1lbe7Svp4aOHkqQMTs76bd5OdZk8QDPWLVKOjFn129yd+mRCb01ZO9+yrSW9JsnO1k51Blr/zXi55dNPwd/o5p1bOhn+u/w+/yg+TwXwQtGh4ZKk+4/vJnAlwOuJiopSlgzZVK9BXQWFjLMs/2rZcjVt2Ew7dm3Xe0UKJ2CFwD/367HfJEmenp6vbGsX38W8TEhIiPLly6dRo0ZJkkqXLq1Hjx5pypQpatq0qZIlS5aQ5QGv9OBhpEp+WlNX/rpmtTzyUaTSpHSUJFXt3Uz3Hty3Xv/woZI5JLU8zpc9tyasnmMJD5J05a9rOn7uV1X18deMdYsU4FtJ9yMfaNaGpVbbqj+kQ4y67O3sNa97sMZ/PUvF8/GfGQDEhVu3bqlB4waq81Ftq+UeHu9Ikn7//XcCBP4TEmwIU2RkpHbv3q3y5ctbLa9YsaLu3Lmj/fv3J1BlgLmoqCgdOXNcl/+8KulJr0FgvY4q5/2+Jn0zT5J04txpnb385BvYtI5OalW5gZqWr61Ja+ZZtnPt1g3lyJjVatt2tnbKniGL3FxzSJIKuRfQrxfOqLSnj/ZPWqfIdWd0Zv5Pzx2+1LfxZ7K3s1O/eWPi5bgB4L/IyclJY4NGq6RvCavla1avlSTlz58vIcoC3rgE64EICwvTw4cPlTNnTqvlOXI8+bB05swZ+fr6JkBlwD9T36+GFn85UZK0dtcmLfh+pdX64vkK66fx30iS9p48pDHLp1nWzVq/VL0bfaru9Tpo1volSu6QTINbdFealI66fe+OJMnFKb2ypM+khT1C1H/+WJ0IO636ZWtoWpcRkqTp3y2UJBV55119/tHHKt21jiIfRsb7cQPAf9me3Xs1euQYVa1WRQUKFkjocoA3IsF6ICIiIiRJqVKlslqeMmVKSdLt27ffeE3A69hz4pBKd62tTyb0lm+BIlo/bIHV+rNXzqtMtzpqPqqLXNNl1I9BXyt50ifD9PrPG6vhSyZqULPPdXX5YZ2eu0MR925r9Y8bdfd/w58c7Ozl4pRe7cb31OQ187Tl0I/6OChQ6/duVb8mXSRJSe2Tam73cQpaOVN7Tx56o8cPAP81P+78STWq1lTOXDk1deaUhC4HeGMSrAciKirqpeuTJEnwC0QBsfL7xbP6/eJZbT+yW7fuRGheYLDe9/TR9iO7JUkXr1/WxeuX9cPhXfr94jn9MHaF6rxfVfM3rdDjqMfqOXOY+s8bKzfX7Lpw/bJu3rmlbWOW60bEX5KkiLt3FBUVpe92b7ba7/q9W1SpaFllcHLWF3XbKYlNEg1aGGS5epONjY2kJ5d0fRz1+M2dEAD4F/tq2XK1bfmx8ryTW6u//Vrp06dP6JKANybBPqU7Oj6ZYHrnzh2r5U97Hp7tmQASI+c06dSkXG25OFn/x3Hg9FFJUvYMWdTAr6bcM+e0Xv/rEUlS5vSZJEllvEqoQpEyevDwgY6f+1U379ySbRJbeebKa2n76/kzSpIkiRzs7a22ZW/35PG9yPuq835V5c2eW3fW/KpHG87q0YazKuNVXGW8iuvRhrNqVoErMQHA6xo3JkjNGjWXT/FiCt2yUa6urgldEvBGJViAyJ49u2xtbXX27Fmr5efOnZMkubu7J0RZQKwkd0imeYHBalWpvtXyCu+VlvQkKMzoOkpffGR9meEKRcpIkg6fOS5JqlO6qqZ3GSk72//vFGxZqb7SOjrp6x83SJK+2/Ok56F+2RpW26peorx+/u2YIu7e1od9WqhIxypWP/tPHdb+U4dVpGMVrfkpNA6PHgD+e2ZMm6kvA3up9ke19c13q5UmTZqELgl44xJsCFPSpElVpEgRhYaGqlWrVpZhFhs2bJCjo6O8vLwSqjTAWNjVC5q5bon6Nu6ih48e6eBvR/V+QR/1qN9BM9Yt1vFzv2rYkgka0LSbrvx1TVt+/lHvuuVXvyZdFLr/B637XyiYsna+2vzvhnOzNizRu275NbxVTy3Zslo/HN4lSdp2+Cd989NGjWvXTymTJdfRP06qafk68i1QVDX6Pbmz9dE/TsSoMeJ/k7D3nzr8hs4KAPw7Xbp0Sd27BSpHzhxq1+FjHTxwyGq9m3suubi4JExxwBuUoPeBaN++vVq0aKFPP/1UtWvX1sGDBzVz5kx169ZNyZMnT8jSAGPtx/fU75fOqm3VRsqRIYvCrl5U37ljNPqrJxPqBi8M1tWb1/VJ9eb6/KN2unrzuqasXaD+88ZatvHLHydVrU9zDWvVQ2sGztGlP69oyKIQDV0cYrWvuoPaq1+TLupau61cnNLp2NlfVWtAG63dtemNHjMA/BdtWLdB9+7d09k/zqpc2fIx1k+bOUVNmjVJgMqANytB70QtSaGhoRo/frzOnDmjjBkzqlGjRmrZsqXx87kTNQAkbtyJGgASv7fmTtSSVL58+Rg3kwMAAACQOHGtVAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGP/KECsWbNGly5dkiRNmjRJ1apVU9++ffXgwYM4LQ4AAABA4hLrADFp0iT16tVLFy5c0P79+zV+/Hh5e3tr9+7dGj16dHzUCAAAACCRiHWAWLFihUaMGKHChQtrw4YNKlSokAYNGqQhQ4Zo/fr18VEjAAAAgEQi1gHiypUr8vb2liT9+OOPKlWqlCTJ1dVVt27ditvqAAAAACQqdrF9QqZMmXTmzBk9ePBAp0+flq+vryRp3759ypQpU5wXCAAAACDxiHWAqF+/vj777DM5ODjIw8ND3t7eWrhwoUaOHKnOnTvHR40AAAAAEolYB4hWrVopV65cCgsLU/Xq1SVJqVOnVp8+fVSnTp04LxAAAABA4hHrACFJ/v7+Vo99fX2VLl26OCkIAAAAQOIV60nUt27dUp8+fXTy5Ek9fvxYLVq0kK+vrypXrqywsLD4qBEAAABAIhHrADFs2DDt2rVLdnZ2Cg0N1b59+zRy5EjlzJlTI0eOjI8aAQAAACQSsR7CtG3bNk2cOFHu7u6aPn26fH199eGHH8rDw0ONGjWKjxoBAAAAJBKx7oG4e/euXF1dJUk7d+5UyZIlJUnJkiXT48eP47Y6AAAAAIlKrHsg3N3dtXXrVrm6uurq1asqXbq0JGnZsmVyd3eP8wIBAAAAJB6xDhCdO3dWp06d9PDhQ1WrVk05c+bUsGHDtHDhQk2cODE+agQAAACQSMQ6QJQpU0bbtm3T5cuXlTdvXklS1apVVbduXXogAAAAgH+5f3QfiLRp0ypt2rSWx15eXpKkS5cuKVOmTHFTGQAAAIBEJ9YBIiwsTCNGjNCpU6csk6ajo6MVGRmpGzdu6NixY3FeJAAAAIDEIdZXYRo4cKBOnjypihUr6vLly6pataoKFCiga9euqX///vFQIgAAAIDEItY9EAcOHNCkSZPk4+Oj7du3q1y5cvLy8tK4ceO0bds21a1bNz7qBAAAAJAIxLoHIjIyUtmzZ5ck5cqVSydPnpQk1axZUz///HPcVgcAAAAgUYl1gMiSJYtOnTol6UmAOH78uCQpKipKd+7cidvqAAAAACQqsR7CFBAQoO7du2vkyJEqW7asmjZtqsyZM2vnzp3y8PCIjxoBAAAAJBKxDhBt27ZV0qRJFR0dLS8vL3Xo0EGTJ0+Wq6urRo4cGR81AgAAAEgkbKKjo6MTuojXceTIEZ09e1YfBrdL6FIAAM8RHRouSbr/+G4CVwIAeJFfj/0mSfL09HxlW6MeiK+//tp45zVr1jRuCwAAAODtYhQgevToYbQxGxsbAgQAAADwL2YUIE6cOBHfdQAAAAB4C8TqMq737t3Ts1MmfvvtN92/fz9OiwIAAACQOBkHiLVr18rf31+//PKL1fKhQ4eqTJkyCg0NjfPiAAAAACQuRgFi9+7d6t69u/z8/JQxY0ardV9++aX8/f312Wef6cCBA/FSJAAAAIDEwShATJs2TY0bN9bQoUPl4uJitc7d3V3Dhg1T9erVNXny5HgpEgAAAEDiYBQgjh07pjp16ry0TcOGDXXs2LE4KQoAAABA4mQUIB48eKBkyZK9tI2Tk5Pu3bsXJ0UBAAAASJyMAkSuXLl08ODBl7Y5cOCAsmTJEidFAQAAAEicjO4DUb16dQUHB6t48eIxJlFL0uXLlxUcHKzatWvHeYGm0u6/m2D7BgC8WjLbFAldAgAgDhgFiMaNG2vDhg2qVq2aateuLW9vb6VOnVp//fWXDhw4oFWrVilnzpxq1apVfNcLAHjLpEuXTjdu3EjoMgAAccQm+tk7w71AZGSkgoKCtGLFCt28edOy3NnZWbVr11b79u1fOU8iPhw5ckSSlCe/+xvfNwDg1TK7ZJUkXbgansCVAABeZNO6zcqRI4c8PT1f2daoB0KSHBwc1L17d3Xt2lVhYWG6efOm0qVLp2zZssnGxua1CgYAAADwdjAOEJYn2NkpV65c8VELAAAAgETO6CpMAAAAACARIAAAAADEAgECAAAAgLHXChCRkZFxVQcAAACAt8A/ChCLFy+Wv7+/ChUqpLCwMPXr10+TJk2K69oAAAAAJDKxDhBr1qzRmDFjFBAQIHt7e0mSu7u7pkyZolmzZsV5gQAAAAASj1gHiFmzZqlXr17q1KmTkiR58vSmTZuqb9++Wrp0aZwXCAAAACDxiHWAOHPmjIoUKRJjuY+Pjy5evBgnRQEAAABInGIdIJydnXXmzJkYyw8ePKgMGTLESVEAAAAAEqdYB4h69epp4MCB+v777yVJv//+uxYvXqwhQ4aoVq1acV4gAAAAgMTDLrZPaNOmjSIiItS1a1c9ePBAH3/8sezs7FS/fn21a9cuPmoEAAAAkEjEOkBIUteuXdW+fXudPn1a0dHRcnNzU6pUqeK6NgAAAACJTKwDxIULFyy/p0+fXpJ069Yt3bp1S5KUOXPmOCoNAAAAQGIT6wDh7+8vGxubF64/fvz4axUEAAAAIPGKdYCYN2+e1ePHjx/rzJkzmjNnjnr06BFnhQEAAABIfGIdIIoVKxZjWYkSJZQtWzaFhITI398/TgoDAAAAkPjE+jKuL5IzZ06dOHEirjYHAAAAIBF6rUnUT92+fVtTp05V1qxZ46QoAAAAAIlTnEyijo6OVooUKTRq1Kg4KwwAAABA4vPak6glyd7eXu+8845SpkwZJ0UBAAAASJz+UYDo0qWL3N3d46MeAAAAAIlYrCdR79q1S0mTJo2PWgAAAAAkcrEOEAEBARo9erR+/fVXRUZGxkdNAAAAABKpWA9h2rZtm86dO6cNGzY8dz13ogYAAAD+vWIdINq3bx8fdQAAAAB4CxgFiHz58mnHjh1Knz69AgIC4rsmAAAAAImU0RyI6Ojo+K4DAAAAwFsg1pOoAQAAAPx3Gc+BWLdunVKlSvXKdjVr1nydegAAAAAkYsYBYvDgwa9sY2NjQ4AAAAAA/sWMA8TOnTuVPn36+KwFAAAAQCJnNAfCxsYmvusAAAAA8BbgKkwAAAAAjBkFiICAACVNmjS+awEAAACQyBnNgRg2bFh81wEAAADgLcB9IAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAEAAADAGAECAAAAgDECBAAAAABjBAgAAAAAxggQAAAAAIwRIAAAAAAYI0AAAAAAMEaAAAAAAGCMAAHEsaioKE2fOkNFvYvJOU0G5ctTQF907a5bt25Z2pw/f0HNm7RQlgzZlCFtJlWpUFWHDh6y2s7xY8dVq3ptuTpnUZYM2dS6RRtdunTJqk14+Hk1bdRM2TLlUIa0mVS3dn2d/vX0mzhMAHirRUVFadyYIBXM66W0qdKrWGEfLV60xKrN/Lnz9d67RZQ2VXp55S+kiSGTFB0dbdVmx/ad+qBMeWVIm0l5cnmoW5fPFRERYdXmwYMH6turn/Lk8rDsa/lXK+L9GIH4QoAA4tiYUWPVpXNXVapcSctWLNFnXT/VogWL1eCjhoqOjlZERITK+1XQz4cOK2TyeM2ZP1sREbdVrVJ1Xbx4UZJ04cJFVSpXWTdu/KlZc2dq/KRg7fppt6pWrKaHDx9Kku7cuaOqFavp8M9HFDwxSLPnzdKF8xdU3q+Cbty4kZCnAAASvYH9Bqlf7/5q1qKpVq5eLj9/P7Vs2kpLlyyTJM2eOUdtW7VTpcqVtHL1cjVu0kiBn/fQqOGjLds49ssxVav0oZImddCCxfPUq8+XWrJwqZo3bmG1rxZNW2nq5Gn6IvBzrfj6K3m966WmDZtp4/qNb/SYgbhil9AFPHXp0iVVq1ZNEydOlI+PT0KXA/wjUVFRGjtqnFq3baVBQwdKkvzL+Std+nRq2rCZDuw/qI3rN+rG9Rs6eHS/XF1dJUmFi3irZLFS+mHbdtWrX1ezZ8zWzZu3tGL1V0qfPr0kycXZWRXLVdbWzVtVvmJ5rVqxSqdOntL+n/cqf4H8kqSCngWUN3d+rVy+Sq3btkqYkwAAidzdu3c1YfxEdezUQV8Efi5J8vvATwcPHNKkkMmqV7+uRg4fpYDaARoyfLBl/a+//qrJEyere88vJElLFi2VjY2Nlq1cqlSpUkmSHj16pE4dOuvs2XPKkSO7dmzfqVUrVunrNStVsXJFy7Z+O/27NmzYqAqVKiTAGQBeT6IIEBcvXlSrVq1idPkBb5tbt26pQeMGqvNRbavlHh7vSJJ+//13rVq5SgG1a1rCgyRlypRJv5/7/6FHbdu3UaUqlSzhQZLsHRwkSfcfPJAkfVjjQ23+IbclPFi1uX8/jo8MAP49kiZNqi3bv5dLBher5Q4O9rp166YkadU3K5Q8efJn1jvo/v0Hlsf379+Xvb29UqRIYVmWLn06SdKN69eVI0d2rVq5Sm7ubpbwIEk2Njbasv37OD8u4E1J0CFMUVFRWrlypWrWrKnr168nZClAnHByctLYoNEq6VvCavma1WslSfny5dXxYyeU5508GtB3oHJldZNjsjSq+EFlHfvlmKW9i4uL3itSWNKT/6B279qjLp27yM3dTeXKfyBJSpMmjUqULC5JioyM1JHDR9SmRRs5Ozur9ke13sThAsBbydbWVp5ensqUKZOio6N1+fJljRoxWpu/36K27dpKkvLmy6scOXMoOjpaN27c0OyZc7Rw/iK1bdfGsp1mLZpKkrp/Hqjr16/r2C/HNHTQUBX0LCCvd70kSYcPHVH+Avm1ZPFSFSpYWKmSplahgoX1zeo1b/7AgTiSoD0QJ0+eVL9+/dSwYUOVLFlSbdu2TchygHixZ/dejR45RlWrVVGGjBn06NEjhQRPUK5cuTRp6iQ9iHygQf0HqYJ/Je05uFuZM7taPb9Y4eL69dSvSp48uZYuXxzjGzFJql2jjjaFfq8kSZJoyvRJVr0bAIAXW7b0K8uchcpVKqlBo/pW63fv2iO/9/0lSYWLFNanXTtb1hUoWEBDhg/WZ526aOL4SZKk7Dmya9PWUNna2kqSrl27ptOnT+vggYMaMKifMmXKpKlTpqt+nQb6es1KhjDhrZSgPRCurq4KDQ1Vz549lSxZsoQsBYgXP+78STWq1lTOXDk1deYURUY+tKz75ruvVblqJdUMqKFVa1YpIiJCUyZOibGNoPFjtea71fLzL6taNeoodENojDY9evXQdxvWqnHTRmrbqp3mzJobr8cFAP8WRYsWUejmDRobPEY//bhL1avUtLrSUvYc2bXx+/WaNnOKLl28JL/3P9Ddu3clSaNGjFbnjp+qTbs2WrfxW81fPE+OjqlUpUJVXb58WdKTHuJLFy/pq5VL1ahJI31Q/gMtXrZQ+fLn1dDBwxPkmIHXlaA9EE5OTgm5eyBefbVsudq2/Fh53smt1d9+rfTp0+vmzSdja0uXed8y4U6SsmfPprz5PPTzoZ9jbMe/3JNvvsr6l1VhryIaM2qsylcsb9XGt1RJSU8m5p3946xGDBup5i2bxdehAcC/hpu7m9zc3VSqdCmlTu2o1i3aauf2nSpVupQkKXNmV2XO7Kr3y7yvXLlyqbx/Ra1asUr1GtTT8CEjVL9hPQWNH2vZXuky76vAO54aNzpIw0cNk6NjKmVyzSTvwt6WNra2tvL7wE8zp81648cLxAUu4wrEg3FjgtSsUXP5FC+m0C0bLUOK0qRJIxcXZz14EBnjOQ8fPlKy/w1P2rZlm9Z/t95qvZ2dnQoULKCLF5/cC2Lf3v3PvY54Ie9CunjhYlwfEgD8a1y9elUL5y/UlStXrJYX8i4kSQoLC9OSxUv12+nfrNcXfrL+woWLunr1qu7evasSJa3nvGXIkEHveOTR8WPHJUnuuXPrYeTDGPePePTw0XOHpAJvAwIEEMdmTJupLwN7qfZHtfXNd6uVJk0aq/UVK1fUlu+36Nq1a5Zlp06e0qmTpyw9CYsWLlbrFm2trkwWERGh3bt2q6BnQUnSxvUb1aJJS4WFhVvaPH78WNu2brO0AQDEdO/efbVu0VZznxnuuSn0yZWRCnkXUoe2HTVuTNBz13t6FlSGDBmULl067dyx06rNtWvX9Oup08rllkuSVKlyBV2/fl3fh/7/VZciIyO1cUOo5d984G2TKC7jCvxbXLp0Sd27BSpHzhxq1+FjHTxwyGq9m3sufdm7p9asXqsPK1dXz949FRkZqf59Bihrtqxq0aq5JKlLt8+04quVql3jI3Xp9pkeRD7QmJFjdTvitnr3/VKS1KptS02fNkO1a9TWl32+lIO9g6ZNmaajR37R2nXfvOEjB4C3R/bs2dSsRVMNHTxcdvb2KlToXe3c8aNGjxyj5i2bKV/+fPoi8HMNGjBYLi4uKlO2tA4fPqKhg4bJ/wM/VaxcUTY2Nurdr5e6ftpNqVOnVq3aAbp2/bpGjxgtW1tbfdrlyWTr+g3ra/LEKWrRtJUGDhmgLFkya2LIJJ0PP6+FS+Yn8JkA/hkCBBCHNqzboHv37unsH2dVrmz5GOunzZyiJs2aaMv279W7Zx+1atZatra28i/nr5FjRsjR0VHSk8sHbtq6UX179VOr5q316NEjvV+6lKZMn6R8+fNJkjJmzKjN2zapV88+6tyhs27fvqOixYpq/aZ1fKsFAK8wfmKwcuXKpVkzZuvc2XPKmi2r+vTvrS7dPpMk9egVKGcXZ02ZNEVBY4Pl7OKs1m1bqXe/XrKxsZEkte/YTk5OaRQ8brzmzZmv9M7p5VvKV0uXL1HOXDklSfb29vp2w1r1691f/fv0V0TEbRXyLqRvN6y1mhcBvE1sop8dlJdAdu/eraZNm2revHmxuhP1kSNHJEl58rvHV2kAgNeQ2SWrJOnC1fBXtAQAJJRN6zYrR44c8vT0fGVb5kAAAAAAMJZohjD5+Pjo5MmTCV0GAAAAgJegBwIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMZvo6OjohC7idRw4cEDR0dFycHBI6FIAAM9x9uzZhC4BAPAKLi4usre3V+HChV/Z1u4N1BOvbGxsEroEAMBL5MiRI6FLAAC8wsOHD40/V7/1PRAAAAAA3hzmQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwZpfQBQD/ZZGRkdq/f79+//133blzRzY2NnJ0dJS7u7u8vLyUNGnShC4RAADACgECSCDTp0/X1KlTdfv27eeuT506tdq1a6eWLVu+4coAAABejAABJIBZs2Zp7NixatWqlSpWrKgcOXIoZcqUkqTbt2/r7Nmz2rBhg0aPHq0kSZKoefPmCVswAADA/9hER0dHJ3QRwH/NBx98oOrVq+vTTz99abugoCB9++23Cg0NfUOVAQD+bu/evbFqX7Ro0XiqBEg86IEAEsD169f13nvvvbJd4cKFNWvWrDdQEQDgeTp06GAZahodHS0bG5vntnu67vjx42+yPCBBECCABJA7d26tXbtWpUqVemm7FStWKFeuXG+oKgDAs9asWaOWLVvqxo0bGjFihJInT57QJQEJjiFMQALYsWOH2rVrpwIFCqhcuXLKlSuXZQ7EnTt3dO7cOW3cuFGHDx/W+PHjVa5cuQSuGAD+uy5evKiAgAAFBAQoMDAwocsBEhwBAkgghw4dUkhIiPbs2aOHDx9arbO1tVWRIkXUvn17FS9ePIEqBAA8tXLlSvXv31+hoaHKmDFjQpcDJCgCBJDAIiMjFRYWptu3bysqKkqOjo7Knj27HBwcEro0AMD/REdH6+TJk8qcObNSp06d0OUACYoAAQAAAMBYkoQuAAAAAMDbgwABAAAAwBgBAgAAAIAxAgQAAAAAYwQIAEiE/P395eHhYfnJmzevChcurMaNG2vv3r1xvr/du3fLw8ND4eHhkqQmTZqoR48eRs+9e/euFi5c+Fr7Dw8Pl4eHh3bv3v3SdmFhYerXr5/8/f3l6ekpf39/DRo0SFevXrW0WblypTw8PF6rHgDAixEgACCRatmypXbs2KEdO3bohx9+0JIlS5QqVSq1bt1aFy5ciNd9h4SEqFevXkZtZ82apZkzZ8ZrPZK0f/9+BQQE6MqVKxo2bJjWrVunQYMG6eDBg2rQoIGuXLkS7zUAAAgQAJBopUiRQi4uLnJxcVGGDBn0zjvvaMCAAbp//75CQ0Pjdd9OTk5ydHQ0avsmrgYeGRmpbt26qXjx4po0aZJ8fHyUNWtW+fr6avbs2YqIiNCECRPivQ4AAAECAN4qdnZ2kmS50aC/v79GjBihKlWqyMfHR3v27FF0dLSmT5+uDz74QO+++65q1Kihb775xmo7+/bt00cffSQvLy9Vr15dJ06csFr/7BCmw4cPq3nz5vL29lbJkiXVr18/3bt3TyEhIZowYYLOnz9vNQRqxYoVqly5sry8vFS5cmXNnTtXUVFRlu2dOnVKTZs2VaFChVS+fHn99NNPLz3uLVu26OLFi+rYsaNsbGys1qVJk0bTp09X+/btn/vcCxcuqEuXLipRooQKFCig0qVLa9SoUZZ6Hj9+rFGjRqlMmTIqWLCgKlWqpMWLF1uef/36dXXu3Fk+Pj7y8vJS/fr1tWfPnpfWCwD/ZnYJXQAAwMzly5c1dOhQpUiRQmXKlLEsX7BggaZOnSpHR0d5eHho3LhxWrt2rfr27Ss3Nzft3btX/fv3V0REhBo1aqSwsDC1bNlSNWvW1PDhw3X69Gn17dv3hfsNCwtTs2bNVL58eS1dulQREREKDAzUgAED1KdPH929e1ffffedli9frnTp0mnp0qUaO3as+vbtKy8vLx07dkyDBg3S5cuX1b17d0VERFjCyFdffaUrV66oT58+Lz32o0ePKkWKFMqbN+9z13t5eb3wue3bt5eLi4tmz56tlClT6vvvv9ewYcPk7e2tcuXKadGiRVq/fr3GjRunjBkzasuWLerfv7/y5MmjIkWKqH///oqMjNSCBQvk4OCgKVOmqEOHDvrhhx+UIkWKV7xqAPDvQ4AAgERq6tSpmjVrliTp0aNHioyMlLu7u4KCgpQ5c2ZLuzJlyqhkyZKSnkxonjNnjsaOHauyZctKkrJnz67z589r5syZatSokZYtWyZnZ2f169dPtra2cnd318WLFzVs2LDn1rFs2TI5OTlp6NChlh6QwYMH6+DBg0qZMqVSpEghW1tbubi4SJImTZqk9u3bq2rVqpKkbNmy6fbt2xowYIA+/fRTffvtt7p3756GDx8uR0dH5cmTR19++aU6duz4wnNx8+ZNOTo6xuh9eJX79++rRo0aqly5slxdXSVJzZs31/Tp03Xy5EmVK1dO586dU4oUKZQ1a1ZlyJBBjRs3lpubm3LlyiVJOnfunN555x1ly5ZNyZIlU69evfThhx/K1tY2VrUAwL8FAQIAEqn69eurSZMmkqQkSZK8cF5Cjhw5LL+fPn1aDx48ULdu3ZQkyf+PUn0aQO7fv69Tp04pf/78Vh+ACxcu/MI6Tp06pQIFCljCgyQVL15cxYsXj9H2xo0bunTpksaOHavg4GDL8qioKD148EDh4eE6deqUcubMaXUs3t7eLz0XadOm1c2bNxUdHR2rEJEsWTI1btxY69ev1+HDh3X27FmdPHlS165dswxhatSokTZt2qQyZcooX7588vX1VdWqVZU+fXpJ0ieffKIvvvhCGzZs0HvvvadSpUqpWrVqSpo0qXEdAPBvQoAAgEQqTZo0VuHgRZIlS2b5/emE5qCgILm5ucVo6+DgIBsbG6v5CJKswsGzXrbuWU+327NnT0uvyN+5urrGev/Sk4AzZcoUHTt2TAUKFIixfvr06QoPD9eAAQOslt+9e1eNGzfW/fv3ValSJQUEBMjLy0uNGjWytMmZM6c2btyoPXv2aOfOndq6daumT5+uYcOGKSAgQOXLl9f27du1fft2/fjjj5o9e7YmTJigZcuWKU+ePMbnBgD+LZhEDQD/Im5ubrKzs9OFCxeUI0cOy8+2bds0c+ZMJUmSRHnz5tXRo0cVGRlped7Ro0dfuM3cuXPr2LFjevz4sWVZaGio/P399eDBA6segfTp0ytdunQKCwuz2v8vv/yioKAgSVLevHn1xx9/6MaNG0b7l6QSJUooa9asmjx5coyrPl2/fl1z5syxqu+pHTt26JdfftG8efPUuXNnValSRalSpdL169ct25k3b542btwoX19fde/eXWvWrFGJEiX03XffKTIyUsOGDVNYWJiqVKmiwYMHa9OmTUqSJIm2bt360poB4N+KAAEA/yKOjo6qX7++goODtXr1aoWFhWn58uUaNWqUMmTIIElq0KCB7t27py+//FK//fabtmzZopCQkBdus2HDhvrzzz/Vr18//fbbb9q7d69Gjhyp4sWLK2nSpEqRIoVu3rypM2fO6NGjR2rTpo3mz5+vBQsW6Ny5cwoNDVX//v2VLFkyOTg4WIYHdevWTSdOnNCePXs0ZMiQlx6Xg4ODhgwZoh07dqhjx47au3evwsLCtGnTJjVt2lQpU6ZUly5dYjwvU6ZMkqRvvvlG58+f1759+9ShQwc9fPjQEqBu3LihgQMH6vvvv9f58+e1fft2HT9+XN7e3nJwcNCRI0fUp08fHTp0SOHh4Vq5cqXu3r37ymFXAPBvxRAmAPiX6dmzp9KmTavg4GBduXJFrq6u6ty5s1q3bi1Jypgxo+bOnauhQ4cqICBArq6uat++fYzhP09lzJhRs2bN0qhRo1SzZk2lSZNGVapUUdeuXSVJFSpU0LJly1S9enUtWLBALVu2VNKkSTV//nwNHz5czs7Oqlu3rjp37izpyf0t5s6dq0GDBqlBgwZKkyaNOnfurJ49e770uIoXL64lS5Zo2rRp6tatm/78809lzJhRfn5+ateunWXOwt95eXmpZ8+emjNnjoKCgpQxY0ZVqVJFrq6uOnLkiKQncxwePnyowYMH6+rVq3JxcVGDBg308ccfS5LGjRunYcOGqX379oqIiJCbm5tGjx6tIkWK/LMXCADecjbRb+IOQAAAAAD+FRjCBAAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADGCBAAAAAAjBEgAAAAABgjQAAAAAAwRoAAAAAAYIwAAQAAAMAYAQIAAACAMQIEAAAAAGMECAAAAADG/g+09CDi1sDxywAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'RandomForestClassifier Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "\n",
    "cm = ConfusionMatrix(clf_rf, classes=[0,1], cmap='Greens')\n",
    "\n",
    "cm.fit(X_train, y_train)\n",
    "cm.score(X_train, y_train)\n",
    "\n",
    "# Show the confusion matrix\n",
    "cm.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy, precision and f1-score for training data are 0.86, 0.56 and 0.07 respectively\n"
     ]
    }
   ],
   "source": [
    "clf_rf = RandomForestClassifier(max_depth =50 , min_samples_split=5,random_state=0)\n",
    "\n",
    "y_pred_test = model_fit_predict(clf_rf, X_train, y_train, X_test)\n",
    "\n",
    "#f1 score for training data\n",
    "f1 = round(f1_score(y_test, y_pred_test),2) \n",
    "\n",
    "#accuracy score for training data\n",
    "acc = round(accuracy_score(y_test, y_pred_test),2) \n",
    "\n",
    "#precision score for training data\n",
    "pre = round(precision_score(y_test, y_pred_test),2) \n",
    "\n",
    "print(f\"Accuracy, precision and f1-score for training data are {acc}, {pre} and {f1} respectively\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8554566369910973"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIQkAQBEhCQBAESEJAEARIckG4eX5f83mXQ/Of4195h3X3Hz7nWm0416ZNfe/a21fvOSNnHP5NenY/eNpvcf+Oe4LAzJ1+sw1vn6Xw49Li932zT7HfTq/evz36/w4AN7L0qVL02SzLVLVsm2tr1ZbbZtZs2fX2f7PX2d8aUBDj89GZpOGHgDey9x5f83Rp/XP64sWveOaF2fMyqU3DFnjvlPOvyR/nDAp13/9q2nTumWuHjwsR372zDw/+sFsvlmbJMmgW0fmkutuyhXn90/3rnvk9p8/mBPPPC+P331bDtm323o5LoA1ef7PU7JixYrceduP0rnTTqu3N27cOB07dMjYx0fXec0tw0fknvsfTL/TP1+fo8IHC8m//vWvOf7443PLLbdk//33X1czQZKkpqYmI+9/OBdde2Mqlco7rlu5cmXO+Nrlab/5Znl5/tJa+8aOfy4PP/ZEHv3xLTnm44cmSQ7dt1s6HXpMfnDH3bns3LPy5tKl+c6Q4fnqmafl8vO+lCQ5+ohDcvDJn8/VNw/Lb+780fo7SIB/MXHS5GyyySb5VJ8T06xZszr7D9hv31p/Hj9hYu65/8Fce9UVOeSgA+trTEjyAS5tz58/P3379s2idzlLBB/EpCkv5uzLvp3Pn3xCRt507Tuu+96Pfpq/vbIgXx/Qr86+UU8+lZYtmqfXYQet3rZl+3Y5fP/u+c/HxyRJxk2YnNcWLkqf3keuXlNVVZU+R/fI42OfyZtLl9b5uQDry8RJk7P7rruuMSL/VaVSyTkXfC177L5bLviyy9rUv7UOyZqamjzwwAM56aSTsmDBgvUxEyRJdti2Y1564pEMuvyitNh00zWueeHFafnW4B/mthuuTotNm9fZ/5dpM9Nph+3SuHHjWts7V2+fqTNmJUmmTJuRJNm104611uxcvUNWrlyZ6bPnfvCDAXifVp2RbJxeJ/RJyy23SbvtdsyXvvyVNZ64uee+BzLumT9l8A3X1fl7DurDWofk1KlTc+WVV+akk07KDTfcsD5mgiRJu7abZbuOHd5x/4oVK3L6Vy9Lv38/OYcf0H2Na15ftChtWrWqs711q5ZZuHjxW2tW/bdNq5Z11iTJwsVLiuYHWFuVSiWTnn8h02bMzInHH5v/fPDeXHbRhbnr3vtz7MmfTk1NTa31AwcPycEHHpAjDju0gSZmY7fW90h27Ngxo0ePTocOHTJu3Lj1MRO8L9d8f0ReW7go373k/HdcU/Mu91Y2qmr01pqad1yzal1V2YAAa6lSqeSXP/9Zttxii+y5R5ckyWGHHJwOW2+dU/udlVGjf5tjevdMkvzhj+Py7MTn8ot7/qMhR2Yjt9ZnJNu2bZsOHd75LBHUhwnPT8l1P7g1w6+7Is2aNs2KFStWB+HKmpqsXLkySbJZ61ZZtIYzigsXL8lmbVq9taZ1kmTRkjf+Zc2qM5WbtWm93o4D4J81atQoRxx26OqIfNtxR/dKkjw3+fnV2+578KFsvnnbHNu7V73OCP/McyTZID00+vEsW7Y8PU85K0137pamO3fLmRdfmSTZ5fDjctTnvphk1X2PM1/+7zqXg6bPmpMunTslSXZ7697IabPm1FozbdacNG3aJJ223249Hw3AKvPmz8+IH/80c+bWvjf7zTdX/aO/Lbdsv3rbI78elZOOPy5NmjSp1xnhnwlJNkhnfe5TefqXd9X6uuL8/kmSh24dkmHXXp4k6XXogVm0eElGPfHU6tf+Y8GrefLp8el52KrHZBy0z0fTskXz3Per//9stkqlkgd//dscvn/3NGvWtB6PDNiYrVixImede36G3/aTWtvvuf+BNG7cOIcetOoJFK+++j95adr0HHygR+/RsDyQnA3SNltvlW223qrWtuenTkuSdN1tl+y4/bZJksP2754jDtg3p37lG7n+Gxekfdu2+dbgH6Ztm9Y5+9TPJElaNG+eC794er49ZHiaNm2Sg7p9ND++9xcZ//yUPH73bfV7YMBGbYftt88Znz8lAwcPSfPmm+bA/fbLmLFjc+3AQTn3S1/MrrvsnCSZ/MILSZI9dt+9IccFIcmH3/3Db8qF3x6Yi68dlJpKJQfv87Hcc8vA1Z9qkyRXnN8/m2yySUbcdV9u/NFPs8cunfLQiJtzcPe9G3ByYGP0w5sHpdNOO+aOu+7Jd67/Xrbbdptc/c1Lc9EF561e87e//yNJsnnbtg00JaxSVXm3jwx5D+PGjctpp52WkSNHvu9Ptpk8eXKS5CNt3mMhwAai/d6HJ0lefbnuZ7gDbIge+d3vU11dna5du77rOvdIAgBQ5ANd2t5///0zderUdTULAAAbEGckAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoUlWpVCr1+YbPPvtsKpVKmjZtWp9vC7DezJ49u6FHAFinttxyyzRp0iTdunV713Wb1NM8q1VVVdX3WwKsV9XV1Q09AsA6tXz58vfVbPV+RhIAgA8H90gCAFBESAIAUERIAgBQREgCAFBESAIAUERIAgBQREgCAFBESAIAUERIAgBQpN4/IhHWh2XLlmX8+PGZMWNGlixZkqqqqrRu3TqdO3fOXnvtlWbNmjX0iADwoSMk2eCNGDEiw4cPz+LFi9e4v02bNunfv3/69u1bz5MBwIebkGSDdvvtt2fQoEHp169fevfunerq6rRs2TJJsnjx4syePTujRo3K9773vTRq1Chf+MIXGnZgAPgQqapUKpWGHgJK9ejRI5/4xCdy/vnnv+u6wYMH59FHH83o0aPraTKAcs8888xard93333X0yTw7pyRZIO2YMGC7LPPPu+5rlu3brn99tvrYSKAD27AgAGrb9epVCqpqqpa47q3902ZMqU+x4PVhCQbtJ133jmPPPJIDjnkkHddd//992ennXaqp6kAPpiHH344ffv2zauvvprrr78+zZs3b+iRYI1c2maDNmbMmPTv3z977rlnjjrqqOy0006r75FcsmRJ5syZk9/85jeZNGlShgwZkqOOOqqBJwZ4f+bPn58+ffqkT58+ueSSSxp6HFgjIckGb+LEiRk6dGiefvrpLF++vNa+xo0bp3v37jn77LNzwAEHNNCEAGUeeOCBXHXVVRk9enS23nrrhh4H6hCSfGgsW7Ysc+fOzeLFi1NTU5PWrVtnhx12SNOmTRt6NIAilUolU6dOzTbbbJM2bdo09DhQh5AEAKCIj0gEAKCIkAQAoIiQBACgiJAEAKCIkAQAoIiQBACgiJAEAKCIkAQAoMj/A0Js1R0mxrOBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "\n",
    "cm = ConfusionMatrix(clf_rf, classes=[0,1], cmap='Reds')\n",
    "cm.fit(X_train, y_train)\n",
    "\n",
    "cm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Random Forest Feature Importance')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sorted_idx = clf_rf.feature_importances_.argsort()\n",
    "plt.barh(scaled_df.iloc[:,:-1].columns[sorted_idx], clf_rf.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Random Forest Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    42506\n",
       "1     7476\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df_0 = scaled_df[scaled_df.label ==0].sample(n=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df_new = pd.concat([scaled_df[scaled_df.label==1], scaled_df_0], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df_new = scaled_df_new.sample(frac = 1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>num_passengers</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>Internet</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>RoundTRip</th>\n",
       "      <th>OneWayTrip</th>\n",
       "      <th>CircleTrip</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>1.214885</td>\n",
       "      <td>0.411694</td>\n",
       "      <td>-0.196830</td>\n",
       "      <td>-0.910449</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>1.538334</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>1.037314</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.380962</td>\n",
       "      <td>-0.052875</td>\n",
       "      <td>-0.532474</td>\n",
       "      <td>2.574534</td>\n",
       "      <td>0.595045</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.521453</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.898048</td>\n",
       "      <td>-0.620990</td>\n",
       "      <td>-1.305376</td>\n",
       "      <td>-0.408618</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.631152</td>\n",
       "      <td>0.175652</td>\n",
       "      <td>-1.120618</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.741921</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.920290</td>\n",
       "      <td>-0.620990</td>\n",
       "      <td>-1.490134</td>\n",
       "      <td>-1.412280</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-1.300985</td>\n",
       "      <td>-2.810681</td>\n",
       "      <td>2.810681</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15471</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.920290</td>\n",
       "      <td>-0.502969</td>\n",
       "      <td>0.172685</td>\n",
       "      <td>-1.412280</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>1.538334</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.107240</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15472</th>\n",
       "      <td>3.341348</td>\n",
       "      <td>2.126783</td>\n",
       "      <td>-0.561979</td>\n",
       "      <td>1.465988</td>\n",
       "      <td>1.096876</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-1.300985</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15473</th>\n",
       "      <td>0.400769</td>\n",
       "      <td>1.648592</td>\n",
       "      <td>-0.561979</td>\n",
       "      <td>0.542200</td>\n",
       "      <td>-1.412280</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>1.158002</td>\n",
       "      <td>-1.474687</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15474</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.775721</td>\n",
       "      <td>-0.591484</td>\n",
       "      <td>-1.490134</td>\n",
       "      <td>0.093214</td>\n",
       "      <td>-1.421288</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>0.195526</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15475</th>\n",
       "      <td>-0.579424</td>\n",
       "      <td>-0.620031</td>\n",
       "      <td>0.500210</td>\n",
       "      <td>-0.935861</td>\n",
       "      <td>-1.412280</td>\n",
       "      <td>0.703587</td>\n",
       "      <td>-0.650054</td>\n",
       "      <td>-0.863557</td>\n",
       "      <td>-0.439155</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>-0.355786</td>\n",
       "      <td>-0.048231</td>\n",
       "      <td>-0.088336</td>\n",
       "      <td>0.100826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15476 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  flight_day  \\\n",
       "0           -0.579424       1.214885        0.411694    -0.196830   -0.910449   \n",
       "1            1.380962      -0.052875       -0.532474     2.574534    0.595045   \n",
       "2           -0.579424      -0.898048       -0.620990    -1.305376   -0.408618   \n",
       "3           -0.579424      -0.631152        0.175652    -1.120618    1.096876   \n",
       "4           -0.579424      -0.920290       -0.620990    -1.490134   -1.412280   \n",
       "...               ...            ...             ...          ...         ...   \n",
       "15471       -0.579424      -0.920290       -0.502969     0.172685   -1.412280   \n",
       "15472        3.341348       2.126783       -0.561979     1.465988    1.096876   \n",
       "15473        0.400769       1.648592       -0.561979     0.542200   -1.412280   \n",
       "15474       -0.579424      -0.775721       -0.591484    -1.490134    0.093214   \n",
       "15475       -0.579424      -0.620031        0.500210    -0.935861   -1.412280   \n",
       "\n",
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "0                 0.703587              1.538334               1.158002   \n",
       "1                -1.421288             -0.650054               1.158002   \n",
       "2                -1.421288             -0.650054               1.158002   \n",
       "3                 0.703587             -0.650054               1.158002   \n",
       "4                -1.421288             -0.650054              -0.863557   \n",
       "...                    ...                   ...                    ...   \n",
       "15471             0.703587              1.538334               1.158002   \n",
       "15472             0.703587             -0.650054              -0.863557   \n",
       "15473            -1.421288             -0.650054               1.158002   \n",
       "15474            -1.421288             -0.650054              -0.863557   \n",
       "15475             0.703587             -0.650054              -0.863557   \n",
       "\n",
       "       flight_duration  Internet    Mobile  RoundTRip  OneWayTrip  CircleTrip  \\\n",
       "0             1.037314  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "1            -1.521453  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "2            -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "3            -1.741921  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "4            -1.300985 -2.810681  2.810681  -0.048231   -0.088336    0.100826   \n",
       "...                ...       ...       ...        ...         ...         ...   \n",
       "15471        -1.107240  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "15472        -1.300985  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "15473        -1.474687  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "15474         0.195526  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "15475        -0.439155  0.355786 -0.355786  -0.048231   -0.088336    0.100826   \n",
       "\n",
       "       label  \n",
       "0          1  \n",
       "1          1  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "...      ...  \n",
       "15471      1  \n",
       "15472      1  \n",
       "15473      0  \n",
       "15474      1  \n",
       "15475      0  \n",
       "\n",
       "[15476 rows x 15 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = scaled_df_new.iloc[:,:-1]\n",
    "y = scaled_df_new['label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.to_numpy(), y.to_numpy(), test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf = RandomForestClassifier(n_estimators=50,max_depth =50 , min_samples_split=5,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy, precision, recall and f1-score for training data are 0.61, 0.59, 0.59, 0.62 and 0.59 respectively\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model_fit_predict(clf_rf, X_train, y_train, X_test)\n",
    "\n",
    "#f1 score for training data\n",
    "f1 = round(f1_score(y_test, y_pred_test),2) \n",
    "\n",
    "#accuracy score for training data\n",
    "acc = round(accuracy_score(y_test, y_pred_test),2) \n",
    "\n",
    "#precision score for training data\n",
    "pre = round(precision_score(y_test, y_pred_test),2) \n",
    "\n",
    "recall = round(recall_score(y_test, y_pred_test),2)\n",
    "\n",
    "specificity = round(recall_score(y_test, y_pred_test, pos_label=0),2)\n",
    "\n",
    "print(f\"Accuracy, precision, recall and f1-score for training data are {acc}, {pre}, {recall}, {specificity} and {f1} respectively\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6078811369509044"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(clf_rf, classes=[0,1])\n",
    "cm.fit(X_train, y_train)\n",
    "\n",
    "cm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Random Forest Feature Importance')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sorted_idx = clf_rf.feature_importances_.argsort()\n",
    "plt.barh(scaled_df.iloc[:,:-1].columns[sorted_idx], clf_rf.feature_importances_[sorted_idx], color='lightcoral')\n",
    "plt.xlabel(\"Random Forest Feature Importance\")\n"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}