ML_model/ML_experiments/ML_model_NormalizedStandardized_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3067f179",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0af54476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import our dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1810ca2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBA</th>\n",
       "      <th>STREET</th>\n",
       "      <th>INCOME_LEVEL</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>CUISINE_DESCRIPTION</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>GRADE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PATHOS</td>\n",
       "      <td>1 AVENUE</td>\n",
       "      <td>high income</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10022</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>THE LITTLE BEET</td>\n",
       "      <td>PARK AVENUE</td>\n",
       "      <td>high income</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10017</td>\n",
       "      <td>Salads</td>\n",
       "      <td>13</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMAZE FUSION &amp; LOUNGE</td>\n",
       "      <td>3 AVENUE</td>\n",
       "      <td>high income</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10017</td>\n",
       "      <td>Asian/Asian Fusion</td>\n",
       "      <td>27</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NOURISH THAI</td>\n",
       "      <td>VANDERBILT AVENUE</td>\n",
       "      <td>medium income</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238</td>\n",
       "      <td>Thai</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ESSEN</td>\n",
       "      <td>MADISON AVENUE</td>\n",
       "      <td>high income</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10017</td>\n",
       "      <td>Sandwiches</td>\n",
       "      <td>13</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DBA             STREET   INCOME_LEVEL    BOROUGH  \\\n",
       "0                 PATHOS           1 AVENUE    high income  Manhattan   \n",
       "1        THE LITTLE BEET        PARK AVENUE    high income  Manhattan   \n",
       "2  AMAZE FUSION & LOUNGE           3 AVENUE    high income  Manhattan   \n",
       "3           NOURISH THAI  VANDERBILT AVENUE  medium income   Brooklyn   \n",
       "4                  ESSEN     MADISON AVENUE    high income  Manhattan   \n",
       "\n",
       "   ZIPCODE CUISINE_DESCRIPTION  SCORE GRADE  \n",
       "0    10022       Mediterranean      9     A  \n",
       "1    10017              Salads     13     A  \n",
       "2    10017  Asian/Asian Fusion     27     B  \n",
       "3    11238                Thai      9     A  \n",
       "4    10017          Sandwiches     13     A  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our input dataset\n",
    "\n",
    "# Load the file\n",
    "file_path = Path('FINAL_NYC_restaurants_full_database.csv')\n",
    "# Read into a dataFrame\n",
    "df = pd.read_csv(file_path)\n",
    "# Show dataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cf4d9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['DBA', 'STREET', 'BOROUGH', 'SCORE'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4cd1571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME_LEVEL</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>CUISINE_DESCRIPTION</th>\n",
       "      <th>GRADE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high income</td>\n",
       "      <td>10022</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Salads</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Asian/Asian Fusion</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medium income</td>\n",
       "      <td>11238</td>\n",
       "      <td>Thai</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Sandwiches</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCOME_LEVEL  ZIPCODE CUISINE_DESCRIPTION GRADE\n",
       "0    high income    10022       Mediterranean     A\n",
       "1    high income    10017              Salads     A\n",
       "2    high income    10017  Asian/Asian Fusion     B\n",
       "3  medium income    11238                Thai     A\n",
       "4    high income    10017          Sandwiches     A"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78d1a7cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCOME_LEVEL           object\n",
      "ZIPCODE                 int64\n",
      "CUISINE_DESCRIPTION    object\n",
      "GRADE                  object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36ed6286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCOME_LEVEL           object\n",
      "ZIPCODE                object\n",
      "CUISINE_DESCRIPTION    object\n",
      "GRADE                  object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Convert column for ZIPCODE from data type 'int' to str':\n",
    "df['ZIPCODE'] = df['ZIPCODE'].astype(str)\n",
    "\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0794851b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    7033\n",
       "B     691\n",
       "C     222\n",
       "P     138\n",
       "Z      90\n",
       "N      50\n",
       "Name: GRADE, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine different grades under 'GRADE' column\n",
    "df['GRADE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfc8f368",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the grades 'N', 'P', and 'Z'\n",
    "df.drop(df[(df['GRADE'] == 'N') | (df['GRADE'] == 'P') | (df['GRADE'] == 'Z')].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f708a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    7033\n",
       "B     691\n",
       "C     222\n",
       "Name: GRADE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine different grades under 'GRADE' column\n",
    "df['GRADE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc894ed",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09243dbb",
   "metadata": {},
   "source": [
    "## Normalizing the Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95aa9c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCOME_LEVEL            3\n",
       "ZIPCODE                78\n",
       "CUISINE_DESCRIPTION    69\n",
       "GRADE                   3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Generate our categorical variable list\n",
    "df_cat = df.dtypes[df.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "# Check the number of unique values in each column\n",
    "df[df_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e92da529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10013    810\n",
      "10022    773\n",
      "10017    564\n",
      "10009    474\n",
      "11238    419\n",
      "        ... \n",
      "11233      2\n",
      "11435      2\n",
      "11206      1\n",
      "10473      1\n",
      "11208      1\n",
      "Name: ZIPCODE, Length: 78, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ZIPCODE_counts = df.ZIPCODE.value_counts()\n",
    "print(ZIPCODE_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0a0cd4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "American            1732\n",
      "Coffee/Tea           692\n",
      "Chinese              682\n",
      "Italian              536\n",
      "Pizza                407\n",
      "                    ... \n",
      "Portuguese             2\n",
      "Cajun                  1\n",
      "Russian                1\n",
      "Hotdogs/Pretzels       1\n",
      "Lebanese               1\n",
      "Name: CUISINE_DESCRIPTION, Length: 69, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "CUISINE_DESCRIPTION_counts = df.CUISINE_DESCRIPTION.value_counts()\n",
    "print(CUISINE_DESCRIPTION_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0cae05f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts\n",
    "ZIPCODE_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7c40810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other    2161\n",
       "10013     810\n",
       "10022     773\n",
       "10017     564\n",
       "10009     474\n",
       "11238     419\n",
       "11101     372\n",
       "10028     344\n",
       "10023     321\n",
       "11375     315\n",
       "10021     315\n",
       "11355     311\n",
       "10024     284\n",
       "10065     280\n",
       "10452     203\n",
       "Name: ZIPCODE, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace\n",
    "replace_ZIPCODE = list(ZIPCODE_counts[ZIPCODE_counts < 200].index)\n",
    "\n",
    "# Replace in DataFrame\n",
    "for ZIPCODE in replace_ZIPCODE:\n",
    "    df.ZIPCODE = df.ZIPCODE.replace(ZIPCODE,\"Other\")\n",
    "\n",
    "\n",
    "# Check to make sure binning was successful\n",
    "df.ZIPCODE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9991e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the new value counts\n",
    "ZIPCODE_counts_new = df.ZIPCODE.value_counts()\n",
    "ZIPCODE_counts_new.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa6f7d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts\n",
    "CUISINE_DESCRIPTION_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8df27e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other             2997\n",
       "American          1732\n",
       "Coffee/Tea         692\n",
       "Chinese            682\n",
       "Italian            536\n",
       "Pizza              407\n",
       "Japanese           360\n",
       "Sandwiches         275\n",
       "Latin American     265\n",
       "Name: CUISINE_DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace\n",
    "replace_CUISINE_DESCRIPTION = list(CUISINE_DESCRIPTION_counts[CUISINE_DESCRIPTION_counts < 250].index)\n",
    "\n",
    "# Replace in DataFrame\n",
    "for CUISINE_DESCRIPTION in replace_CUISINE_DESCRIPTION:\n",
    "    df.CUISINE_DESCRIPTION = df.CUISINE_DESCRIPTION.replace(CUISINE_DESCRIPTION,\"Other\")\n",
    "\n",
    "\n",
    "# Check to make sure binning was successful\n",
    "df.CUISINE_DESCRIPTION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "13b490c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the new value counts\n",
    "CUISINE_DESCRIPTION_counts_new = df.CUISINE_DESCRIPTION.value_counts()\n",
    "CUISINE_DESCRIPTION_counts_new.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10179395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCOME_LEVEL            3\n",
       "ZIPCODE                15\n",
       "CUISINE_DESCRIPTION     9\n",
       "GRADE                   3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Generate our categorical variable list\n",
    "df_cat = df.dtypes[df.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "# Check the number of unique values in each column\n",
    "df[df_cat].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90a91c6",
   "metadata": {},
   "source": [
    "## Encoding the Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4492805",
   "metadata": {},
   "source": [
    "### Encoding the variable 'GRADE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f93fa640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME_LEVEL</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>CUISINE_DESCRIPTION</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high income</td>\n",
       "      <td>10022</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medium income</td>\n",
       "      <td>11238</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Sandwiches</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCOME_LEVEL ZIPCODE CUISINE_DESCRIPTION GRADE  Grade\n",
       "0    high income   10022               Other     A      0\n",
       "1    high income   10017               Other     A      0\n",
       "2    high income   10017               Other     B      1\n",
       "3  medium income   11238               Other     A      0\n",
       "4    high income   10017          Sandwiches     A      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a 'high' and 'low' grade\n",
    "\n",
    "# Creating an instance of label encoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "df[\"Grade\"] = label_encoder.fit_transform(df[\"GRADE\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3258a8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grade dictionary\n",
    "## The dictionary creates two categories for grades: \"high\" and \"low\".\n",
    "## \"high\" grade has been made to replace grades A and B, \n",
    "## whereas \"low\" grade has been made to replace all grades lower than A and B.\n",
    "\n",
    "GRADE_num = {\n",
    "    \"A\": \"high\",\n",
    "    \"B\": \"high\",\n",
    "    \"C\": \"low\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2671c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME_LEVEL</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>CUISINE_DESCRIPTION</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>Grade</th>\n",
       "      <th>GRADE_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high income</td>\n",
       "      <td>10022</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medium income</td>\n",
       "      <td>11238</td>\n",
       "      <td>Other</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Sandwiches</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCOME_LEVEL ZIPCODE CUISINE_DESCRIPTION GRADE  Grade GRADE_num\n",
       "0    high income   10022               Other     A      0      high\n",
       "1    high income   10017               Other     A      0      high\n",
       "2    high income   10017               Other     B      1      high\n",
       "3  medium income   11238               Other     A      0      high\n",
       "4    high income   10017          Sandwiches     A      0      high"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grades encoded using the dictionary values\n",
    "df[\"GRADE_num\"] = df[\"GRADE\"].apply(lambda x: GRADE_num[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0190014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME_LEVEL</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>CUISINE_DESCRIPTION</th>\n",
       "      <th>GRADE_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high income</td>\n",
       "      <td>10022</td>\n",
       "      <td>Other</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Other</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medium income</td>\n",
       "      <td>11238</td>\n",
       "      <td>Other</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high income</td>\n",
       "      <td>10017</td>\n",
       "      <td>Sandwiches</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    INCOME_LEVEL ZIPCODE CUISINE_DESCRIPTION GRADE_num\n",
       "0    high income   10022               Other      high\n",
       "1    high income   10017               Other      high\n",
       "2    high income   10017               Other      high\n",
       "3  medium income   11238               Other      high\n",
       "4    high income   10017          Sandwiches      high"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the GRADE and Grade columns\n",
    "df = df.drop([\"GRADE\", \"Grade\"], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0aca75c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCOME_LEVEL            3\n",
       "ZIPCODE                15\n",
       "CUISINE_DESCRIPTION     9\n",
       "GRADE_num               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Generate our categorical variable list again\n",
    "df_cat = df.dtypes[df.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "# Check the number of unique values in each column again\n",
    "df[df_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b51f5925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "high    7724\n",
      "low      222\n",
      "Name: GRADE_num, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "GRADE_num_counts = df.GRADE_num.value_counts()\n",
    "print(GRADE_num_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ea1c00",
   "metadata": {},
   "source": [
    "### Encoding the Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a31cfe6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>INCOME_LEVEL_high income</th>\n",
       "      <th>INCOME_LEVEL_low income</th>\n",
       "      <th>INCOME_LEVEL_medium income</th>\n",
       "      <th>ZIPCODE_10009</th>\n",
       "      <th>ZIPCODE_10013</th>\n",
       "      <th>ZIPCODE_10017</th>\n",
       "      <th>ZIPCODE_10021</th>\n",
       "      <th>ZIPCODE_10022</th>\n",
       "      <th>ZIPCODE_10023</th>\n",
       "      <th>ZIPCODE_10024</th>\n",
       "      <th>...</th>\n",
       "      <th>CUISINE_DESCRIPTION_Chinese</th>\n",
       "      <th>CUISINE_DESCRIPTION_Coffee/Tea</th>\n",
       "      <th>CUISINE_DESCRIPTION_Italian</th>\n",
       "      <th>CUISINE_DESCRIPTION_Japanese</th>\n",
       "      <th>CUISINE_DESCRIPTION_Latin American</th>\n",
       "      <th>CUISINE_DESCRIPTION_Other</th>\n",
       "      <th>CUISINE_DESCRIPTION_Pizza</th>\n",
       "      <th>CUISINE_DESCRIPTION_Sandwiches</th>\n",
       "      <th>GRADE_num_high</th>\n",
       "      <th>GRADE_num_low</th>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   INCOME_LEVEL_high income  INCOME_LEVEL_low income  \\\n",
       "0                       1.0                      0.0   \n",
       "1                       1.0                      0.0   \n",
       "2                       1.0                      0.0   \n",
       "3                       0.0                      0.0   \n",
       "4                       1.0                      0.0   \n",
       "\n",
       "   INCOME_LEVEL_medium income  ZIPCODE_10009  ZIPCODE_10013  ZIPCODE_10017  \\\n",
       "0                         0.0            0.0            0.0            0.0   \n",
       "1                         0.0            0.0            0.0            1.0   \n",
       "2                         0.0            0.0            0.0            1.0   \n",
       "3                         1.0            0.0            0.0            0.0   \n",
       "4                         0.0            0.0            0.0            1.0   \n",
       "\n",
       "   ZIPCODE_10021  ZIPCODE_10022  ZIPCODE_10023  ZIPCODE_10024  ...  \\\n",
       "0            0.0            1.0            0.0            0.0  ...   \n",
       "1            0.0            0.0            0.0            0.0  ...   \n",
       "2            0.0            0.0            0.0            0.0  ...   \n",
       "3            0.0            0.0            0.0            0.0  ...   \n",
       "4            0.0            0.0            0.0            0.0  ...   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Chinese  CUISINE_DESCRIPTION_Coffee/Tea  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          0.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             0.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Italian  CUISINE_DESCRIPTION_Japanese  \\\n",
       "0                          0.0                           0.0   \n",
       "1                          0.0                           0.0   \n",
       "2                          0.0                           0.0   \n",
       "3                          0.0                           0.0   \n",
       "4                          0.0                           0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Latin American  CUISINE_DESCRIPTION_Other  \\\n",
       "0                                 0.0                        1.0   \n",
       "1                                 0.0                        1.0   \n",
       "2                                 0.0                        1.0   \n",
       "3                                 0.0                        1.0   \n",
       "4                                 0.0                        0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Pizza  CUISINE_DESCRIPTION_Sandwiches  GRADE_num_high  \\\n",
       "0                        0.0                             0.0             1.0   \n",
       "1                        0.0                             0.0             1.0   \n",
       "2                        0.0                             0.0             1.0   \n",
       "3                        0.0                             0.0             1.0   \n",
       "4                        0.0                             1.0             1.0   \n",
       "\n",
       "   GRADE_num_low  \n",
       "0            0.0  \n",
       "1            0.0  \n",
       "2            0.0  \n",
       "3            0.0  \n",
       "4            0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(df[df_cat]))\n",
    "\n",
    "# Add the encoded variable names to the DataFrame\n",
    "encode_df.columns = enc.get_feature_names(df_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd35522a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCOME_LEVEL_high income</th>\n",
       "      <th>INCOME_LEVEL_low income</th>\n",
       "      <th>INCOME_LEVEL_medium income</th>\n",
       "      <th>ZIPCODE_10009</th>\n",
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       "      <th>ZIPCODE_10024</th>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   INCOME_LEVEL_high income  INCOME_LEVEL_low income  \\\n",
       "0                       1.0                      0.0   \n",
       "1                       1.0                      0.0   \n",
       "2                       1.0                      0.0   \n",
       "3                       0.0                      0.0   \n",
       "4                       1.0                      0.0   \n",
       "\n",
       "   INCOME_LEVEL_medium income  ZIPCODE_10009  ZIPCODE_10013  ZIPCODE_10017  \\\n",
       "0                         0.0            0.0            0.0            0.0   \n",
       "1                         0.0            0.0            0.0            1.0   \n",
       "2                         0.0            0.0            0.0            1.0   \n",
       "3                         1.0            0.0            0.0            0.0   \n",
       "4                         0.0            0.0            0.0            1.0   \n",
       "\n",
       "   ZIPCODE_10021  ZIPCODE_10022  ZIPCODE_10023  ZIPCODE_10024  ...  \\\n",
       "0            0.0            1.0            0.0            0.0  ...   \n",
       "1            0.0            0.0            0.0            0.0  ...   \n",
       "2            0.0            0.0            0.0            0.0  ...   \n",
       "3            0.0            0.0            0.0            0.0  ...   \n",
       "4            0.0            0.0            0.0            0.0  ...   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Chinese  CUISINE_DESCRIPTION_Coffee/Tea  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          0.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             0.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Italian  CUISINE_DESCRIPTION_Japanese  \\\n",
       "0                          0.0                           0.0   \n",
       "1                          0.0                           0.0   \n",
       "2                          0.0                           0.0   \n",
       "3                          0.0                           0.0   \n",
       "4                          0.0                           0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Latin American  CUISINE_DESCRIPTION_Other  \\\n",
       "0                                 0.0                        1.0   \n",
       "1                                 0.0                        1.0   \n",
       "2                                 0.0                        1.0   \n",
       "3                                 0.0                        1.0   \n",
       "4                                 0.0                        0.0   \n",
       "\n",
       "   CUISINE_DESCRIPTION_Pizza  CUISINE_DESCRIPTION_Sandwiches  GRADE_num_high  \\\n",
       "0                        0.0                             0.0             1.0   \n",
       "1                        0.0                             0.0             1.0   \n",
       "2                        0.0                             0.0             1.0   \n",
       "3                        0.0                             0.0             1.0   \n",
       "4                        0.0                             1.0             1.0   \n",
       "\n",
       "   GRADE_num_low  \n",
       "0            0.0  \n",
       "1            0.0  \n",
       "2            0.0  \n",
       "3            0.0  \n",
       "4            0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "df = df.merge(encode_df,left_index=True, right_index=True)\n",
    "df = df.drop(df_cat,1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03de063c",
   "metadata": {},
   "source": [
    "## Splitting the Data, and Standardizing the Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "464ab95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove GRADE_num target from features data\n",
    "y = df.GRADE_num_high\n",
    "X = df.drop(columns=[\"GRADE_num_high\",\"GRADE_num_low\"])\n",
    "\n",
    "# Split training/test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)\n",
    "\n",
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cda3b9",
   "metadata": {},
   "source": [
    "# Resampling Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac39bcfe",
   "metadata": {},
   "source": [
    "## Oversampling: Naive Random Oversampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbe0ef89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1.0: 5598, 0.0: 5598})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b011fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11196,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e6c9d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e9ce0b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., ..., 1., 0., 1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38c1a936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.523\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ddc1ac8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.523\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75bcda9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 34,  19],\n",
       "       [897, 970]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d942b9ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.04      0.64      0.52      0.07      0.58      0.34        53\n",
      "        1.0       0.98      0.52      0.64      0.68      0.58      0.33      1867\n",
      "\n",
      "avg / total       0.95      0.52      0.64      0.66      0.58      0.33      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a127073b",
   "metadata": {},
   "source": [
    "## Oversampling: SMOTE Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d371963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1.0: 5598, 0.0: 5598})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, y_resampled = SMOTE(random_state=1, sampling_strategy='auto').fit_resample(\n",
    "    X_train_scaled, y_train\n",
    ")\n",
    "\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2428815a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11196,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a4bb0075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b8dcbae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., ..., 0., 0., 1.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aa20deec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.539\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "745db727",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.539\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "90b8add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 35,  18],\n",
       "       [868, 999]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cbca8322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.04      0.66      0.54      0.07      0.59      0.36        53\n",
      "        1.0       0.98      0.54      0.66      0.69      0.59      0.35      1867\n",
      "\n",
      "avg / total       0.96      0.54      0.66      0.68      0.59      0.35      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24f01ff",
   "metadata": {},
   "source": [
    "## Undersampling: Cluster Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "117d372c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 159, 1.0: 159})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "cc = ClusterCentroids(random_state=1)\n",
    "X_resampled, y_resampled = cc.fit_resample(X_train_scaled, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ec036909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(318,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b0b8110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98d1e7fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 1.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "97a293ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.383\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d8930b93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.383\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1aa9c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  36,   17],\n",
       "       [1167,  700]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "598468f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.03      0.68      0.37      0.06      0.50      0.26        53\n",
      "        1.0       0.98      0.37      0.68      0.54      0.50      0.25      1867\n",
      "\n",
      "avg / total       0.95      0.38      0.67      0.53      0.50      0.25      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ec8d1d",
   "metadata": {},
   "source": [
    "## Combination (Over and Under) Sampling: SMOTEENN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3847b183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 3828, 1.0: 3372})"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "smote_enn = SMOTEENN(random_state=1)\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X_train_scaled, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "087a53e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7200,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e4c78d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9c75ca7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., ..., 0., 0., 1.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f55b5029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.516\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8f73f93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.516\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "81ccc300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 34,  19],\n",
       "       [911, 956]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0378ad1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.04      0.64      0.51      0.07      0.57      0.33        53\n",
      "        1.0       0.98      0.51      0.64      0.67      0.57      0.32      1867\n",
      "\n",
      "avg / total       0.95      0.52      0.64      0.66      0.57      0.32      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ad3d3c",
   "metadata": {},
   "source": [
    "# Ensemble Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe1d532",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f89278a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a random forest classifier.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model = RandomForestClassifier(n_estimators=128, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "465d59e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1.0: 5598, 0.0: 159})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model\n",
    "rf_model = rf_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6aa5806e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5757,)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6da66c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = rf_model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eeda9fd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.972\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "818115bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.972\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d336e1b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>0</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0            0           53\n",
       "Actual 1            0         1867"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5679faa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.00      0.00      1.00      0.00      0.00      0.00        53\n",
      "        1.0       0.97      1.00      0.00      0.99      0.00      0.00      1867\n",
      "\n",
      "avg / total       0.95      0.97      0.03      0.96      0.00      0.00      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(\"Classification Report\")\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b432785f",
   "metadata": {},
   "source": [
    "## Balanced Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "45428d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "# Create a balanced random forest classifier:\n",
    "brf_model = BalancedRandomForestClassifier(n_estimators=128, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "707a12b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1.0: 5598, 0.0: 159})"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model:\n",
    "brf_model = brf_model.fit(X_train_scaled, y_train)\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "314d13cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5757,)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e920846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., ..., 0., 1., 1.])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictions using the testing data:\n",
    "y_pred = brf_model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "af8d9602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.477\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "26ab2ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.477\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "81ebfbc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>983</td>\n",
       "      <td>884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           32           21\n",
       "Actual 1          983          884"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e958a01f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.03      0.60      0.47      0.06      0.53      0.29        53\n",
      "        1.0       0.98      0.47      0.60      0.64      0.53      0.28      1867\n",
      "\n",
      "avg / total       0.95      0.48      0.60      0.62      0.53      0.28      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(\"Classification Report\")\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2a3333e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.08424448170040882, 'CUISINE_DESCRIPTION_Other'),\n",
       " (0.0695642928179929, 'ZIPCODE_Other'),\n",
       " (0.06831691426808033, 'CUISINE_DESCRIPTION_American'),\n",
       " (0.05853864748417314, 'CUISINE_DESCRIPTION_Chinese'),\n",
       " (0.05720136721962033, 'CUISINE_DESCRIPTION_Coffee/Tea'),\n",
       " (0.04571813863649864, 'ZIPCODE_10013'),\n",
       " (0.04493066839702294, 'CUISINE_DESCRIPTION_Italian'),\n",
       " (0.043499907000916695, 'CUISINE_DESCRIPTION_Japanese'),\n",
       " (0.04313799723635767, 'INCOME_LEVEL_low income'),\n",
       " (0.041805267473455025, 'CUISINE_DESCRIPTION_Pizza'),\n",
       " (0.03786727779611009, 'ZIPCODE_10021'),\n",
       " (0.03272833315919302, 'CUISINE_DESCRIPTION_Sandwiches'),\n",
       " (0.030875816931269187, 'ZIPCODE_10024'),\n",
       " (0.0297997976163734, 'INCOME_LEVEL_high income'),\n",
       " (0.028299392578761642, 'ZIPCODE_10017'),\n",
       " (0.02750671884152612, 'ZIPCODE_10022'),\n",
       " (0.02634273187359743, 'ZIPCODE_11355'),\n",
       " (0.02593344583938964, 'INCOME_LEVEL_medium income'),\n",
       " (0.025087767100465314, 'CUISINE_DESCRIPTION_Latin American'),\n",
       " (0.0248146229972555, 'ZIPCODE_10028'),\n",
       " (0.024204846059664765, 'ZIPCODE_10023'),\n",
       " (0.024043454361126235, 'ZIPCODE_10452'),\n",
       " (0.0225222650727551, 'ZIPCODE_10009'),\n",
       " (0.021310945513418434, 'ZIPCODE_10065'),\n",
       " (0.020869597628011483, 'ZIPCODE_11238'),\n",
       " (0.02086216200646216, 'ZIPCODE_11101'),\n",
       " (0.019973142390094022, 'ZIPCODE_11375')]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "# Calculate feature importance in the Balanced Random Forest model.\n",
    "importances = brf_model.feature_importances_\n",
    "importances\n",
    "\n",
    "# We can sort the features by their importance.\n",
    "sorted(zip(brf_model.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde36512",
   "metadata": {},
   "source": [
    "## Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "68c5a5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the EasyEnsembleClassifier\n",
    "# Create a balanced random forest classifier:\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "\n",
    "ee_model = EasyEnsembleClassifier(n_estimators=128, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d6fc4389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1.0: 5598, 0.0: 159})"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model:\n",
    "ee_model = ee_model.fit(X_train_scaled, y_train)\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "45b668a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5757,)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "58cf6c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., ..., 1., 0., 1.])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictions using the testing data:\n",
    "y_pred = ee_model.predict(X_test_scaled)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "21539fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive accuracy is: 0.501\n"
     ]
    }
   ],
   "source": [
    "# Calculated the accuracy score\n",
    "acc_score = accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c10add16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This model's predictive balanced accuracy is: 0.501\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "acc_score = balanced_accuracy_score(y_test, y_pred)\n",
    "print(f\" This model's predictive balanced accuracy is: {accuracy_score(y_test,y_pred):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "884467d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>940</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           34           19\n",
       "Actual 1          940          927"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bac5d6c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "        0.0       0.03      0.64      0.50      0.07      0.56      0.32        53\n",
      "        1.0       0.98      0.50      0.64      0.66      0.56      0.31      1867\n",
      "\n",
      "avg / total       0.95      0.50      0.64      0.64      0.56      0.31      1920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(\"Classification Report\")\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a71bb20a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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