customer_churn_prediction_for_bank.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R88Ms0MTi0Ma"
   },
   "source": [
    "## Bank Customer Churn Prediction\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WA6lL1fni0Mb"
   },
   "source": [
    "In this project, we use supervised learning models to identify customers who are likely to churn in the future. Furthermore, we will analyze top factors that influence user retention. [Dataset information](https://www.kaggle.com/adammaus/predicting-churn-for-bank-customers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bO94-bXZi0Md"
   },
   "source": [
    "## Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SIvRSRqAi0Md"
   },
   "source": [
    "\n",
    "* [Part 1: Data Exploration](#Part-1:-Data-Exploration)\n",
    "* [Part 2: Feature Preprocessing](#Part-2:-Feature-Preprocessing)\n",
    "* [Part 3: Model Training and Results Evaluation](#Part-3:-Model-Training-and-Result-Evaluation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TUoI2S7Bi6iR"
   },
   "source": [
    "# Part 0: Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "nK7A1qhYSDxM",
    "outputId": "df7f74d7-d5d0-4176-d506-6065f6b355c5"
   },
   "outputs": [
    {
     "data": {
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       "\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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "churn_df = pd.read_csv('bank.data.csv')\n",
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a6bG_gAPi0Me"
   },
   "source": [
    "# Part 1: Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bspx2K6fi0Me"
   },
   "source": [
    "### Part 1.1: Understand the Raw Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ht5YOBdx8NLV",
    "outputId": "68222de6-8622-4bb7-8404-0a9037440556"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# check data info\n",
    "churn_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZASeB8_089yA",
    "outputId": "062ed5ba-b47e-4a33-edd0-abd19b6907a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          10000\n",
       "CustomerId         10000\n",
       "Surname             2932\n",
       "CreditScore          460\n",
       "Geography              3\n",
       "Gender                 2\n",
       "Age                   70\n",
       "Tenure                11\n",
       "Balance             6382\n",
       "NumOfProducts          4\n",
       "HasCrCard              2\n",
       "IsActiveMember         2\n",
       "EstimatedSalary     9999\n",
       "Exited                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values for each column\n",
    "churn_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "4ec5r_Qdi0NL"
   },
   "outputs": [],
   "source": [
    "# Get target variable\n",
    "y = churn_df['Exited']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SsAbAjhvi0Mx"
   },
   "source": [
    "### Part 1.2:  Understand the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_t1xsBp--_0K",
    "outputId": "89a5be68-4c29-4a60-a7d3-49f41116c4c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "churn_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "BIqBIpOt_COM",
    "outputId": "33e13100-2d85-4018-98b5-ba5198e81473"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>Balance</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>100090.239881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>57510.492818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>100193.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>149388.247500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>199992.480000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CreditScore           Age        Tenure  NumOfProducts        Balance  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800       1.530200   76485.889288   \n",
       "std       96.653299     10.487806      2.892174       0.581654   62397.405202   \n",
       "min      350.000000     18.000000      0.000000       1.000000       0.000000   \n",
       "25%      584.000000     32.000000      3.000000       1.000000       0.000000   \n",
       "50%      652.000000     37.000000      5.000000       1.000000   97198.540000   \n",
       "75%      718.000000     44.000000      7.000000       2.000000  127644.240000   \n",
       "max      850.000000     92.000000     10.000000       4.000000  250898.090000   \n",
       "\n",
       "       EstimatedSalary  \n",
       "count     10000.000000  \n",
       "mean     100090.239881  \n",
       "std       57510.492818  \n",
       "min          11.580000  \n",
       "25%       51002.110000  \n",
       "50%      100193.915000  \n",
       "75%      149388.247500  \n",
       "max      199992.480000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand Numerical feature\n",
    "# discrete/continuous\n",
    "# 'CreditScore', 'Age', 'Tenure', 'NumberOfProducts'\n",
    "# 'Balance', 'EstimatedSalary'\n",
    "churn_df[['CreditScore', 'Age', 'Tenure', 'NumOfProducts','Balance', 'EstimatedSalary']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "uSWC_9arxlfk"
   },
   "outputs": [],
   "source": [
    "# check the feature distribution\n",
    "# pandas.DataFrame.describe()\n",
    "# boxplot, distplot, countplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 868
    },
    "id": "E6o4PlZbuSYy",
    "outputId": "821b2fc0-8e14-4cdd-8cf3-508e78a508e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Exited', ylabel='EstimatedSalary'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot for numerical feature\n",
    "_,axss = plt.subplots(2,3, figsize=[20,10])\n",
    "sns.boxplot(x='Exited', y ='CreditScore', data=churn_df, ax=axss[0][0])\n",
    "sns.boxplot(x='Exited', y ='Age', data=churn_df, ax=axss[0][1])\n",
    "sns.boxplot(x='Exited', y ='Tenure', data=churn_df, ax=axss[0][2])\n",
    "sns.boxplot(x='Exited', y ='NumOfProducts', data=churn_df, ax=axss[1][0])\n",
    "sns.boxplot(x='Exited', y ='Balance', data=churn_df, ax=axss[1][1])\n",
    "sns.boxplot(x='Exited', y ='EstimatedSalary', data=churn_df, ax=axss[1][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 868
    },
    "id": "U0xZ-y3cw8JJ",
    "outputId": "cd321df3-c360-423a-8ad7-3282298d9d6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Exited', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# understand categorical feature\n",
    "# 'Geography', 'Gender'\n",
    "# 'HasCrCard', 'IsActiveMember'\n",
    "_,axss = plt.subplots(2,2, figsize=[20,10])\n",
    "sns.countplot(x='Exited', hue='Geography', data=churn_df, ax=axss[0][0])\n",
    "sns.countplot(x='Exited', hue='Gender', data=churn_df, ax=axss[0][1])\n",
    "sns.countplot(x='Exited', hue='HasCrCard', data=churn_df, ax=axss[1][0])\n",
    "sns.countplot(x='Exited', hue='IsActiveMember', data=churn_df, ax=axss[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aFa4d6t3i0NH"
   },
   "source": [
    "# Part 2: Feature Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "3sfa2fQx2xXa"
   },
   "outputs": [],
   "source": [
    "# Get feature space by dropping useless feature\n",
    "to_drop = ['RowNumber','CustomerId','Surname','Exited']\n",
    "X = churn_df.drop(to_drop, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "lZFFVHUgftso",
    "outputId": "e5dcb649-1500-46d0-8d56-866562465015"
   },
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0          1               1        101348.88  \n",
       "1          0               1        112542.58  \n",
       "2          1               0        113931.57  \n",
       "3          0               0         93826.63  \n",
       "4          1               1         79084.10  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i6WyuvNyxNeE",
    "outputId": "7511e0ef-1929-40b1-f705-e8634939df84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CreditScore          int64\n",
       "Geography           object\n",
       "Gender              object\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "QSna9_kTcDx8"
   },
   "outputs": [],
   "source": [
    "cat_cols = X.columns[X.dtypes == 'object']\n",
    "num_cols = X.columns[(X.dtypes == 'float64') | (X.dtypes == 'int64')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XVAaLjGsgCPq",
    "outputId": "da3b8f7e-302b-4b4b-9780-63ce1c1ed77c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V784I6eGgAH4",
    "outputId": "257039e0-0e58-456c-e659-65500d5330ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Geography', 'Gender'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "77OjmSl9i0Nf"
   },
   "source": [
    "Split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uay8Md5li0Nh",
    "outputId": "3b3f3bda-f072-4110-971e-12d6e4bbde5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data has 7500 observation with 10 features\n",
      "test data has 2500 observation with 10 features\n"
     ]
    }
   ],
   "source": [
    "# Splite data into training and testing\n",
    "# 100 -> 75:y=1, 25:y=0\n",
    "# training(80): 60 y=1; 20 y=0\n",
    "# testing(20):  15 y=1; 5 y=0\n",
    "\n",
    "from sklearn import model_selection\n",
    "\n",
    "# Reserve 25% for testing\n",
    "# stratify example:\n",
    "# 100 -> y: 80 '0', 20 '1' -> 4:1\n",
    "# 80% training 64: '0', 16:'1' -> 4:1\n",
    "# 20% testing  16:'0', 4: '1' -> 4:1\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, stratify = y, random_state = 1) #stratified sampling\n",
    "\n",
    "print('training data has ' + str(X_train.shape[0]) + ' observation with ' + str(X_train.shape[1]) + ' features')\n",
    "print('test data has ' + str(X_test.shape[0]) + ' observation with ' + str(X_test.shape[1]) + ' features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ODAexq7nyppM"
   },
   "source": [
    "* 10000 -> 8000 '0' + 2000 '1'\n",
    "\n",
    "* 25% test 75% training\n",
    "---\n",
    "without stratified sampling:\n",
    "• extreme case:\n",
    "---\n",
    "1. testing: 2000 '1' + 500 '0'\n",
    "2. training: 7500 '0'\n",
    "---\n",
    "with stratified sampling:\n",
    "1. testing: 2000 '0' + 500 '1'\n",
    "2. training: 6000 '0' + 1500 '1'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JMTIEpY7IfPp"
   },
   "source": [
    "Read more for handling [categorical feature](https://github.com/scikit-learn-contrib/categorical-encoding), and there is an awesome package for [encoding](http://contrib.scikit-learn.org/category_encoders/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "apcEXk0Eh978",
    "outputId": "bb7b80ff-371d-4990-dbe2-932c4ebc04db"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
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       "      <th>7971</th>\n",
       "      <td>633</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>79408.17</td>\n",
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       "    <tr>\n",
       "      <th>9152</th>\n",
       "      <td>708</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
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       "      <th>6732</th>\n",
       "      <td>548</td>\n",
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       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>645</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>7</td>\n",
       "      <td>90612.34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>149139.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>729</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>91091.06</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure   Balance  NumOfProducts  \\\n",
       "7971          633     Spain    Male   42      10      0.00              1   \n",
       "9152          708   Germany  Female   23       4  71433.08              1   \n",
       "6732          548    France  Female   37       9      0.00              2   \n",
       "902           645    France  Female   48       7  90612.34              1   \n",
       "2996          729     Spain  Female   45       7  91091.06              2   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "7971          0               1         79408.17  \n",
       "9152          1               0        103697.57  \n",
       "6732          0               0         98029.58  \n",
       "902           1               1        149139.13  \n",
       "2996          1               0         71133.12  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "iWEjSK9leWyH"
   },
   "outputs": [],
   "source": [
    "# One hot encoding\n",
    "# another way: get_dummies\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def OneHotEncoding(df, enc, categories):\n",
    "  transformed = pd.DataFrame(enc.transform(df[categories]).toarray(), columns = enc.get_feature_names_out(categories))\n",
    "  return pd.concat([df.reset_index(drop=True), transformed], axis=1).drop(categories, axis=1)\n",
    "\n",
    "categories = ['Geography']\n",
    "enc_ohe = OneHotEncoder()\n",
    "enc_ohe.fit(X_train[categories])\n",
    "\n",
    "X_train = OneHotEncoding(X_train, enc_ohe, categories)\n",
    "X_test = OneHotEncoding(X_test, enc_ohe, categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "txvvIi_Y1G1_",
    "outputId": "46bfb502-a3f9-4c81-c7ea-a014746aca6a"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
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       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>645</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>7</td>\n",
       "      <td>90612.34</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>729</td>\n",
       "      <td>Female</td>\n",
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       "      <td>7</td>\n",
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      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "0          633    Male   42      10      0.00              1          0   \n",
       "1          708  Female   23       4  71433.08              1          1   \n",
       "2          548  Female   37       9      0.00              2          0   \n",
       "3          645  Female   48       7  90612.34              1          1   \n",
       "4          729  Female   45       7  91091.06              2          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "0               1         79408.17               0.0                0.0   \n",
       "1               0        103697.57               0.0                1.0   \n",
       "2               0         98029.58               1.0                0.0   \n",
       "3               1        149139.13               1.0                0.0   \n",
       "4               0         71133.12               0.0                0.0   \n",
       "\n",
       "   Geography_Spain  \n",
       "0              1.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              1.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "6f3JCVj3ouWj"
   },
   "outputs": [],
   "source": [
    "# Ordinal encoding\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "categories = ['Gender']\n",
    "enc_oe = OrdinalEncoder()\n",
    "enc_oe.fit(X_train[categories])\n",
    "\n",
    "X_train[categories] = enc_oe.transform(X_train[categories])\n",
    "X_test[categories] = enc_oe.transform(X_test[categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "qFaSPAc7sP8W",
    "outputId": "fda2276a-590d-4f21-a0c0-93e433b65d66"
   },
   "outputs": [
    {
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       "      <th>Tenure</th>\n",
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       "      <th>3</th>\n",
       "      <td>645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>7</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "0          633     1.0   42      10      0.00              1          0   \n",
       "1          708     0.0   23       4  71433.08              1          1   \n",
       "2          548     0.0   37       9      0.00              2          0   \n",
       "3          645     0.0   48       7  90612.34              1          1   \n",
       "4          729     0.0   45       7  91091.06              2          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "0               1         79408.17               0.0                0.0   \n",
       "1               0        103697.57               0.0                1.0   \n",
       "2               0         98029.58               1.0                0.0   \n",
       "3               1        149139.13               1.0                0.0   \n",
       "4               0         71133.12               0.0                0.0   \n",
       "\n",
       "   Geography_Spain  \n",
       "0              1.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vecyDzf8eXgg"
   },
   "source": [
    "Standardize/Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JuPhtUkJi0NW"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train[num_cols])\n",
    "\n",
    "X_train[num_cols] = scaler.transform(X_train[num_cols])\n",
    "X_test[num_cols] = scaler.transform(X_test[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "UydCNgAZ9ajK",
    "outputId": "26d16f03-a8ef-4f1b-9595-063801e53448"
   },
   "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>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.172985</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.289202</td>\n",
       "      <td>1.731199</td>\n",
       "      <td>-1.218916</td>\n",
       "      <td>-0.912769</td>\n",
       "      <td>-1.542199</td>\n",
       "      <td>0.968496</td>\n",
       "      <td>-0.352044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.602407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.509319</td>\n",
       "      <td>-0.341156</td>\n",
       "      <td>-0.076977</td>\n",
       "      <td>-0.912769</td>\n",
       "      <td>0.648425</td>\n",
       "      <td>-1.032529</td>\n",
       "      <td>0.072315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.051762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.184093</td>\n",
       "      <td>1.385806</td>\n",
       "      <td>-1.218916</td>\n",
       "      <td>0.796109</td>\n",
       "      <td>-1.542199</td>\n",
       "      <td>-1.032529</td>\n",
       "      <td>-0.026711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.048922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.857156</td>\n",
       "      <td>0.695022</td>\n",
       "      <td>0.229625</td>\n",
       "      <td>-0.912769</td>\n",
       "      <td>0.648425</td>\n",
       "      <td>0.968496</td>\n",
       "      <td>0.866221</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.819517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573179</td>\n",
       "      <td>0.695022</td>\n",
       "      <td>0.237278</td>\n",
       "      <td>0.796109</td>\n",
       "      <td>0.648425</td>\n",
       "      <td>-1.032529</td>\n",
       "      <td>-0.496617</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender       Age    Tenure   Balance  NumOfProducts  \\\n",
       "0    -0.172985     1.0  0.289202  1.731199 -1.218916      -0.912769   \n",
       "1     0.602407     0.0 -1.509319 -0.341156 -0.076977      -0.912769   \n",
       "2    -1.051762     0.0 -0.184093  1.385806 -1.218916       0.796109   \n",
       "3    -0.048922     0.0  0.857156  0.695022  0.229625      -0.912769   \n",
       "4     0.819517     0.0  0.573179  0.695022  0.237278       0.796109   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Geography_France  \\\n",
       "0  -1.542199        0.968496        -0.352044               0.0   \n",
       "1   0.648425       -1.032529         0.072315               0.0   \n",
       "2  -1.542199       -1.032529        -0.026711               1.0   \n",
       "3   0.648425        0.968496         0.866221               1.0   \n",
       "4   0.648425       -1.032529        -0.496617               0.0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                0.0              1.0  \n",
       "1                1.0              0.0  \n",
       "2                0.0              0.0  \n",
       "3                0.0              0.0  \n",
       "4                0.0              1.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q3x9ySX_i0Nd"
   },
   "source": [
    "# Part 3: Model Training and Result Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c4UTtCQTi0Nl"
   },
   "source": [
    "### Part 3.1: Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "EAhSxINLi0Nl"
   },
   "outputs": [],
   "source": [
    "#@title build models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Logistic Regression\n",
    "classifier_logistic = LogisticRegression()\n",
    "\n",
    "# K Nearest Neighbors\n",
    "classifier_KNN = KNeighborsClassifier()\n",
    "\n",
    "# Random Forest\n",
    "classifier_RF = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "Av0IRSoBQ3pe",
    "outputId": "66a5c7f3-368a-4a24-9931-058d788977be"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "classifier_logistic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EiLuzUDJRBNi",
    "outputId": "ede4e4be-a6c8-4cf7-9c37-d52d5841db82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction of test data\n",
    "classifier_logistic.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XjMV04mKRJ30",
    "outputId": "4a14f32b-fee9-4e08-b58b-464f4b108bb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.808"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy of test data\n",
    "classifier_logistic.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7J-23z78i0Ns"
   },
   "source": [
    "### (Optional) Part 3.2: Use Grid Search to Find Optimal Hyperparameters\n",
    "alternative: random search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lqTL8zwnANHr"
   },
   "outputs": [],
   "source": [
    "#Loss/cost function --> (wx + b - y) ^2 + ƛ * |w| --> ƛ is a hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "Hpe9PEAAi0Nt"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# helper function for printing out grid search results\n",
    "def print_grid_search_metrics(gs):\n",
    "    print (\"Best score: \" + str(gs.best_score_))\n",
    "    print (\"Best parameters set:\")\n",
    "    best_parameters = gs.best_params_\n",
    "    for param_name in sorted(best_parameters.keys()):\n",
    "        print(param_name + ':' + str(best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qvYo9I5Ti0Nv"
   },
   "source": [
    "#### Part 3.2.1: Find Optimal Hyperparameters - LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "wOc48syxi0Nx",
    "outputId": "ef3fe492-47fe-4c82-d79e-c8978a35599b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(solver=&#x27;liblinear&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: (0.01, 0.05, 0.1, 0.2, 1),\n",
       "                         &#x27;penalty&#x27;: (&#x27;l2&#x27;, &#x27;l1&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(solver=&#x27;liblinear&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: (0.01, 0.05, 0.1, 0.2, 1),\n",
       "                         &#x27;penalty&#x27;: (&#x27;l2&#x27;, &#x27;l1&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.2, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.2, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(solver='liblinear'),\n",
       "             param_grid={'C': (0.01, 0.05, 0.1, 0.2, 1),\n",
       "                         'penalty': ('l2', 'l1')})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Possible hyperparamter options for Logistic Regression Regularization\n",
    "# Penalty is choosed from L1 or L2\n",
    "# C is the 1/lambda value(weight) for L1 and L2\n",
    "# solver: algorithm to find the weights that minimize the cost function\n",
    "\n",
    "# ('l1', 0.01)('l1', 0.05) ('l1', 0.1) ('l1', 0.2)('l1', 1)\n",
    "# ('12', 0.01)('l2', 0.05) ('l2', 0.1) ('l2', 0.2)('l2', 1)\n",
    "parameters = {\n",
    "    'penalty':('l2','l1'),\n",
    "    'C':(0.01, 0.05, 0.1, 0.2, 1)\n",
    "}\n",
    "\n",
    "Grid_LR = GridSearchCV(LogisticRegression(solver='liblinear'),parameters, cv = 5)\n",
    "Grid_LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nN5rU0e-i0N1",
    "outputId": "06c79e91-0247-4fae-924e-bf4a90a7202b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8125333333333333\n",
      "Best parameters set:\n",
      "C:0.2\n",
      "penalty:l1\n"
     ]
    }
   ],
   "source": [
    "# the best hyperparameter combination\n",
    "# C = 1/lambda\n",
    "print_grid_search_metrics(Grid_LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "TtkDsXgui0N3"
   },
   "outputs": [],
   "source": [
    "# best model\n",
    "best_LR_model = Grid_LR.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "epIrhEO3DimN",
    "outputId": "30c58dca-67a3-434c-91ee-25907e4cb7d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_LR_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zF6EfOVaDZvH",
    "outputId": "fb97ea65-9964-4cfd-93ee-72202441f1e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_LR_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 485
    },
    "id": "Jy5hU5KymPT7",
    "outputId": "0ef02dfa-4b8a-4751-ee9e-9d701c79d0a6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_models = pd.DataFrame(Grid_LR.cv_results_)\n",
    "res = (LR_models.pivot(index='param_penalty', columns='param_C', values='mean_test_score'))\n",
    "_ = sns.heatmap(res, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9u9YFedOi0N6"
   },
   "source": [
    "#### Part 3.2.2: Find Optimal Hyperparameters: KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "o78422XVi0N6",
    "outputId": "23e7c63f-e77a-4c06-cb9d-8795bd047d7c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 3, 5, 7, 9]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 3, 5, 7, 9]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=9)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=9)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [1, 3, 5, 7, 9]})"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Possible hyperparamter options for KNN\n",
    "# Choose k\n",
    "parameters = {\n",
    "    'n_neighbors':[1,3,5,7,9]\n",
    "}\n",
    "Grid_KNN = GridSearchCV(KNeighborsClassifier(),parameters, cv=5)\n",
    "Grid_KNN.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ydaRZVAIi0N_",
    "outputId": "c63b93e8-6b44-4a4d-e28c-44b4f56a8408"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8433333333333334\n",
      "Best parameters set:\n",
      "n_neighbors:9\n"
     ]
    }
   ],
   "source": [
    "# best k\n",
    "print_grid_search_metrics(Grid_KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "Nq_qfVpXUJcx"
   },
   "outputs": [],
   "source": [
    "best_KNN_model = Grid_KNN.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KIZVHcWQuGbN",
    "outputId": "7799693c-ce16-42b2-fbcc-28b7e6a38452"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_KNN_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KiOIP_3CuD7v",
    "outputId": "192798d6-834f-4261-e143-9c62c4b5cee5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8428"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_KNN_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nKn_oKLSi0OB"
   },
   "source": [
    "#### Part 3.2.3: Find Optimal Hyperparameters: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "NniAZIPfi0OC",
    "outputId": "e6d419e5-1af9-4948-93e7-50fa80177132"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [60, 80, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [60, 80, 100]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [1, 5, 10],\n",
       "                         'n_estimators': [60, 80, 100]})"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Possible hyperparamter options for Random Forest\n",
    "# Choose the number of trees\n",
    "parameters = {\n",
    "    'n_estimators' : [60,80,100],\n",
    "    'max_depth': [1,5,10]\n",
    "}\n",
    "Grid_RF = GridSearchCV(RandomForestClassifier(),parameters, cv=5)\n",
    "Grid_RF.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ScPiI-Bfi0OE",
    "outputId": "e57f6404-13f7-4fba-80d0-ec6213c6b7e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8650666666666667\n",
      "Best parameters set:\n",
      "max_depth:10\n",
      "n_estimators:100\n"
     ]
    }
   ],
   "source": [
    "# best number of tress\n",
    "print_grid_search_metrics(Grid_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "xJgfri_Mi0OG"
   },
   "outputs": [],
   "source": [
    "# best random forest\n",
    "best_RF_model = Grid_RF.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "LjmwORLjqCwZ",
    "outputId": "72b1f62a-ecd2-4c1e-b8ec-95934645e90e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_RF_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_qxQMlCTHx6c",
    "outputId": "6892b2e3-5aec-4d75-8786-42a046b60747"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.856"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_RF_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xxDAOrGIi0OI"
   },
   "source": [
    "####Part 3.3: Model Evaluation - Confusion Matrix (Precision, Recall, Accuracy)\n",
    "\n",
    "class of interest as positive\n",
    "\n",
    "TP: correctly labeled real churn\n",
    "\n",
    "Precision(PPV, positive predictive value): tp / (tp + fp);\n",
    "Total number of true predictive churn divided by the total number of predictive churn;\n",
    "High Precision means low fp, not many return users were predicted as churn users.\n",
    "\n",
    "\n",
    "Recall(sensitivity, hit rate, true positive rate): tp / (tp + fn)\n",
    "Predict most postive or churn user correctly. High recall means low fn, not many churn users were predicted as return users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "o-tP94iFi0OI"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "# calculate accuracy, precision and recall, [[tn, fp],[]]\n",
    "def cal_evaluation(classifier, cm):\n",
    "    tn = cm[0][0]\n",
    "    fp = cm[0][1]\n",
    "    fn = cm[1][0]\n",
    "    tp = cm[1][1]\n",
    "    accuracy  = (tp + tn) / (tp + fp + fn + tn + 0.0)\n",
    "    precision = tp / (tp + fp + 0.0)\n",
    "    recall = tp / (tp + fn + 0.0)\n",
    "    print (classifier)\n",
    "    print (\"Accuracy is: \" + str(accuracy))\n",
    "    print (\"precision is: \" + str(precision))\n",
    "    print (\"recall is: \" + str(recall))\n",
    "    print ()\n",
    "\n",
    "# print out confusion matrices\n",
    "def draw_confusion_matrices(confusion_matricies):\n",
    "    class_names = ['Not','Churn']\n",
    "    for cm in confusion_matrices:\n",
    "        classifier, cm = cm[0], cm[1]\n",
    "        cal_evaluation(classifier, cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OpSGaN49i0OL",
    "outputId": "73645b12-401d-4485-82fc-483d91814c9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy is: 0.856\n",
      "precision is: 0.7811320754716982\n",
      "recall is: 0.4066797642436149\n",
      "\n",
      "Logistic Regression\n",
      "Accuracy is: 0.81\n",
      "precision is: 0.6049382716049383\n",
      "recall is: 0.1925343811394892\n",
      "\n",
      "K nearest neighbor\n",
      "Accuracy is: 0.8428\n",
      "precision is: 0.7283464566929134\n",
      "recall is: 0.36345776031434185\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix, accuracy, precison and recall for random forest and logistic regression\n",
    "confusion_matrices = [\n",
    "    (\"Random Forest\", confusion_matrix(y_test,best_RF_model.predict(X_test))),\n",
    "    (\"Logistic Regression\", confusion_matrix(y_test,best_LR_model.predict(X_test))),\n",
    "    (\"K nearest neighbor\", confusion_matrix(y_test, best_KNN_model.predict(X_test)))\n",
    "]\n",
    "\n",
    "draw_confusion_matrices(confusion_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OvHlyhPBi0OT"
   },
   "source": [
    "### Part 3.4: Model Evaluation - ROC & AUC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jx_3XkgKi0OW"
   },
   "source": [
    "RandomForestClassifier, KNeighborsClassifier and LogisticRegression have predict_prob() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Os_ZLTvi0OX"
   },
   "source": [
    "#### Part 3.4.1: ROC of RF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "UypvQMVBi0OY"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn import metrics\n",
    "\n",
    "# Use predict_proba to get the probability results of Random Forest\n",
    "y_pred_rf = best_RF_model.predict_proba(X_test)[:, 1]\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RyADmHiPTtln",
    "outputId": "516516a3-89e1-446f-c1f2-bb287007fd29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7132398 , 0.2867602 ],\n",
       "       [0.94102804, 0.05897196],\n",
       "       [0.73434802, 0.26565198],\n",
       "       ...,\n",
       "       [0.85330969, 0.14669031],\n",
       "       [0.94865966, 0.05134034],\n",
       "       [0.88089025, 0.11910975]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_RF_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "s3PR-PdPi0Ob",
    "outputId": "38848817-7307-487c-bc24-32404b255c9a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve of Random Forest result\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve - RF model')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R89IUMYDi0Oe",
    "outputId": "57892d09-7eb1-4c12-8c28-80f2956f8760"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8464169312002241"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# AUC score\n",
    "metrics.auc(fpr_rf,tpr_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1DVqnJVi0Oh"
   },
   "source": [
    "#### Part 3.4.1: ROC of LR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "t-q5XJPoi0Oi"
   },
   "outputs": [],
   "source": [
    "# Use predict_proba to get the probability results of Logistic Regression\n",
    "y_pred_lr = best_LR_model.predict_proba(X_test)[:, 1]\n",
    "fpr_lr, tpr_lr, thresh = roc_curve(y_test, y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zc4k8gUYcpNE",
    "outputId": "06d8f16b-42ab-4339-bbc5-940d6896fad1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.82263162, 0.17736838],\n",
       "       [0.92924848, 0.07075152],\n",
       "       [0.85489575, 0.14510425],\n",
       "       ...,\n",
       "       [0.71582344, 0.28417656],\n",
       "       [0.8905902 , 0.1094098 ],\n",
       "       [0.85477852, 0.14522148]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_LR_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "KZSrN-1Mi0Ok",
    "outputId": "f154b9dc-f0e0-4ee8-f3b1-4b715cd4cabb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_lr, tpr_lr, label='LR')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve - LR Model')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LHAyxishi0On",
    "outputId": "d0704b4b-28be-408c-ab94-00fb891c7305"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.772075518615696"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AUC score\n",
    "metrics.auc(fpr_lr,tpr_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gHHurD8Ii0Oq"
   },
   "source": [
    "# Part 4: Model Extra Functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dSx4TPO-i0Or"
   },
   "source": [
    "### Part 4.1:  Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BtLHUixoi0Ot"
   },
   "source": [
    "The corelated features that we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "mbNTNeb7saCy",
    "outputId": "1bc7fc96-e3e1-45ee-ea99-131ef42a58fb"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>SalaryInRMB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>648632.832</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>729162.048</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600490.432</td>\n",
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       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>506138.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619     0.0   42       2       0.00              1          1   \n",
       "1          608     0.0   41       1   83807.86              1          0   \n",
       "2          502     0.0   42       8  159660.80              3          1   \n",
       "3          699     0.0   39       1       0.00              2          0   \n",
       "4          850     0.0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "0               1        101348.88               1.0                0.0   \n",
       "1               1        112542.58               0.0                0.0   \n",
       "2               0        113931.57               1.0                0.0   \n",
       "3               0         93826.63               1.0                0.0   \n",
       "4               1         79084.10               0.0                0.0   \n",
       "\n",
       "   Geography_Spain  SalaryInRMB  \n",
       "0              0.0   648632.832  \n",
       "1              1.0   720272.512  \n",
       "2              0.0   729162.048  \n",
       "3              0.0   600490.432  \n",
       "4              1.0   506138.240  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_with_corr = X.copy()\n",
    "\n",
    "X_with_corr = OneHotEncoding(X_with_corr, enc_ohe, ['Geography'])\n",
    "X_with_corr['Gender'] = enc_oe.transform(X_with_corr[['Gender']])\n",
    "X_with_corr['SalaryInRMB'] = X_with_corr['EstimatedSalary'] * 6.4\n",
    "X_with_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cQaXOIsUi0Ou",
    "outputId": "7cc8f32f-c9ef-4951-fabb-39e87fd251ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression (L1) Coefficients\n",
      "Age : 0.7307\n",
      "IsActiveMember : -0.5046\n",
      "Geography_Germany : 0.3122\n",
      "Gender : -0.2409\n",
      "Balance : 0.1509\n",
      "CreditScore : -0.0457\n",
      "NumOfProducts : -0.0439\n",
      "Tenure : -0.0271\n",
      "EstimatedSalary : 0.0091\n",
      "Geography_France : -0.0042\n",
      "HasCrCard : -0.0022\n",
      "SalaryInRMB : 0.0001\n",
      "Geography_Spain : 0.0\n"
     ]
    }
   ],
   "source": [
    "# add L1 regularization to logistic regression\n",
    "# check the coef for feature selection\n",
    "scaler = StandardScaler()\n",
    "X_l1 = scaler.fit_transform(X_with_corr)\n",
    "LRmodel_l1 = LogisticRegression(penalty=\"l1\", C = 0.04, solver='liblinear')\n",
    "LRmodel_l1.fit(X_l1, y)\n",
    "\n",
    "indices = np.argsort(abs(LRmodel_l1.coef_[0]))[::-1]\n",
    "\n",
    "print (\"Logistic Regression (L1) Coefficients\")\n",
    "for ind in range(X_with_corr.shape[1]):\n",
    "  print (\"{0} : {1}\".format(X_with_corr.columns[indices[ind]],round(LRmodel_l1.coef_[0][indices[ind]], 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "majifZZqi0O9",
    "outputId": "2f2c2038-f2da-4ae9-c6e1-06ba0675d720"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression (L2) Coefficients\n",
      "Age : 0.751\n",
      "IsActiveMember : -0.5272\n",
      "Gender : -0.2591\n",
      "Geography_Germany : 0.2279\n",
      "Balance : 0.162\n",
      "Geography_France : -0.1207\n",
      "Geography_Spain : -0.089\n",
      "CreditScore : -0.0637\n",
      "NumOfProducts : -0.0586\n",
      "Tenure : -0.0452\n",
      "HasCrCard : -0.0199\n",
      "SalaryInRMB : 0.0137\n",
      "EstimatedSalary : 0.0137\n"
     ]
    }
   ],
   "source": [
    "# add L2 regularization to logistic regression\n",
    "# check the coef for feature selection\n",
    "np.random.seed()\n",
    "scaler = StandardScaler()\n",
    "X_l2 = scaler.fit_transform(X_with_corr)\n",
    "LRmodel_l2 = LogisticRegression(penalty=\"l2\", C = 0.1, solver='liblinear', random_state=42)\n",
    "LRmodel_l2.fit(X_l2, y)\n",
    "LRmodel_l2.coef_[0]\n",
    "\n",
    "indices = np.argsort(abs(LRmodel_l2.coef_[0]))[::-1]\n",
    "\n",
    "print (\"Logistic Regression (L2) Coefficients\")\n",
    "for ind in range(X_with_corr.shape[1]):\n",
    "  print (\"{0} : {1}\".format(X_with_corr.columns[indices[ind]],round(LRmodel_l2.coef_[0][indices[ind]], 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqs41ydLi0O_"
   },
   "source": [
    "### Part 4.2:  Random Forest Model - Feature Importance Discussion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "z6HNnaBRvE1T",
    "outputId": "dcc382c3-40ca-4aee-dc2c-c62fb551b8b7"
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\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>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
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       "      <th>EstimatedSalary</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
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       "      <td>619</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619     0.0   42       2       0.00              1          1   \n",
       "1          608     0.0   41       1   83807.86              1          0   \n",
       "2          502     0.0   42       8  159660.80              3          1   \n",
       "3          699     0.0   39       1       0.00              2          0   \n",
       "4          850     0.0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
       "0               1        101348.88               1.0                0.0   \n",
       "1               1        112542.58               0.0                0.0   \n",
       "2               0        113931.57               1.0                0.0   \n",
       "3               0         93826.63               1.0                0.0   \n",
       "4               1         79084.10               0.0                0.0   \n",
       "\n",
       "   Geography_Spain  \n",
       "0              0.0  \n",
       "1              1.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              1.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_RF = X.copy()\n",
    "\n",
    "X_RF = OneHotEncoding(X_RF, enc_ohe, ['Geography'])\n",
    "X_RF['Gender'] = enc_oe.transform(X_RF[['Gender']])\n",
    "\n",
    "X_RF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MPxUM2lei0PA",
    "outputId": "e13b09fd-c3c5-49ec-d39c-a4b24aa717d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance ranking by Random Forest Model:\n",
      "Age : 0.2374\n",
      "EstimatedSalary : 0.1469\n",
      "CreditScore : 0.1457\n",
      "Balance : 0.1426\n",
      "NumOfProducts : 0.1309\n",
      "Tenure : 0.0819\n",
      "IsActiveMember : 0.0385\n",
      "Geography_Germany : 0.0213\n",
      "Gender : 0.0182\n",
      "HasCrCard : 0.0178\n"
     ]
    }
   ],
   "source": [
    "# check feature importance of random forest for feature selection\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_RF, y)\n",
    "\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature importance ranking by Random Forest Model:\")\n",
    "for ind in range(X.shape[1]):\n",
    "  print (\"{0} : {1}\".format(X_RF.columns[indices[ind]],round(importances[indices[ind]], 4)))"
   ]
  }
 ],
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