Project-Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STAT 208 Final Project Code\n",
    "## Customer Segmentation Analysis\n",
    "\n",
    "\n",
    "### Information:\n",
    "For detail, please refer to our [GitHub](https://github.com/Jen-uis/STAT208_Team8) page.\n",
    "  \n",
    "\n",
    "  \n",
    "### Contact:\n",
    "This Project Code is subject to the team as:\n",
    "- Nathaniel Zhu\n",
    "- Xujuan Liang\n",
    "- Xiaoya Wei\n",
    "- Youyi Fu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Necessary Packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Pre-analysis of the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, load the dataset\n",
    "train_data = pd.read_csv(\"Data/train.csv\")\n",
    "test_data = pd.read_csv(\"Data/test.csv\")\n",
    "\n",
    "# This steps was created while dealing with Part III,\n",
    "# fill the NAs using forward fill function\n",
    "train_data.ffill(inplace = True)\n",
    "test_data.ffill(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8068 entries, 0 to 8067\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               8068 non-null   int64  \n",
      " 1   Gender           8068 non-null   object \n",
      " 2   Ever_Married     8068 non-null   object \n",
      " 3   Age              8068 non-null   int64  \n",
      " 4   Graduated        8068 non-null   object \n",
      " 5   Profession       8068 non-null   object \n",
      " 6   Work_Experience  8068 non-null   float64\n",
      " 7   Spending_Score   8068 non-null   object \n",
      " 8   Family_Size      8068 non-null   float64\n",
      " 9   Var_1            8068 non-null   object \n",
      " 10  Segmentation     8068 non-null   object \n",
      "dtypes: float64(2), int64(2), object(7)\n",
      "memory usage: 693.5+ KB\n",
      "None\n",
      "\n",
      "Test Dataset Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2627 entries, 0 to 2626\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               2627 non-null   int64  \n",
      " 1   Gender           2627 non-null   object \n",
      " 2   Ever_Married     2627 non-null   object \n",
      " 3   Age              2627 non-null   int64  \n",
      " 4   Graduated        2627 non-null   object \n",
      " 5   Profession       2627 non-null   object \n",
      " 6   Work_Experience  2627 non-null   float64\n",
      " 7   Spending_Score   2627 non-null   object \n",
      " 8   Family_Size      2627 non-null   float64\n",
      " 9   Var_1            2627 non-null   object \n",
      "dtypes: float64(2), int64(2), object(6)\n",
      "memory usage: 205.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Show basic information about the datasets\n",
    "print(\"Train Dataset Info:\")\n",
    "print(train_data.info())\n",
    "print(\"\\nTest Dataset Info:\")\n",
    "print(test_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cat_4</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>462643</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Cat_4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466315</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461735</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>462669</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender Ever_Married  Age Graduated     Profession  Work_Experience  \\\n",
       "0  462809    Male           No   22        No     Healthcare              1.0   \n",
       "1  462643  Female          Yes   38       Yes       Engineer              1.0   \n",
       "2  466315  Female          Yes   67       Yes       Engineer              1.0   \n",
       "3  461735    Male          Yes   67       Yes         Lawyer              0.0   \n",
       "4  462669  Female          Yes   40       Yes  Entertainment              0.0   \n",
       "\n",
       "  Spending_Score  Family_Size  Var_1 Segmentation  \n",
       "0            Low          4.0  Cat_4            D  \n",
       "1        Average          3.0  Cat_4            A  \n",
       "2            Low          1.0  Cat_6            B  \n",
       "3           High          2.0  Cat_6            B  \n",
       "4           High          6.0  Cat_6            A  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing head data\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part II: Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>3651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>4417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Count\n",
       "0  Female   3651\n",
       "1    Male   4417"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gender Data preparation\n",
    "gender = train_data.groupby('Gender').count().reset_index()[['Gender','Age']]\n",
    "gender.rename(columns = {'Age':'Count'}, inplace = True)\n",
    "gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender Distribution Visualization\n",
    "plt.figure(figsize = (6, 6))\n",
    "colors = ['#ff9999','#66b3ff']\n",
    "plt.pie(gender['Count'], labels = gender['Gender'], autopct = '%1.1f%%', startangle = 90, colors = colors, textprops = {'fontsize': 14})\n",
    "\n",
    "plt.title('Gender Distribution', fontsize = 18)\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Family Size Distribution Analysis\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.histplot(train_data['Family_Size'], bins = 10, kde=True)\n",
    "plt.title('Family Size Distribution')\n",
    "plt.xlabel('Family Size')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spending Score Distribution Visualization\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.histplot(train_data['Spending_Score'], bins  =20, kde = True)\n",
    "plt.title('Spending Score Distribution')\n",
    "plt.xlabel('Spending Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Distribution Visualization\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.histplot(train_data['Age'], bins = list(range(15,90,5)), kde = True, color = 'blue')\n",
    "\n",
    "plt.title('Age Distribution of Customers', fontsize = 16)\n",
    "plt.xlabel('Age', fontsize = 14)\n",
    "plt.ylabel('Frequency', fontsize = 14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "      <th>Numeric_spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cat_4</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>462643</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Cat_4</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466315</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461735</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>462669</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender Ever_Married  Age Graduated     Profession  Work_Experience  \\\n",
       "0  462809    Male           No   22        No     Healthcare              1.0   \n",
       "1  462643  Female          Yes   38       Yes       Engineer              1.0   \n",
       "2  466315  Female          Yes   67       Yes       Engineer              1.0   \n",
       "3  461735    Male          Yes   67       Yes         Lawyer              0.0   \n",
       "4  462669  Female          Yes   40       Yes  Entertainment              0.0   \n",
       "\n",
       "  Spending_Score  Family_Size  Var_1 Segmentation  Numeric_spending  \n",
       "0            Low          4.0  Cat_4            D                 1  \n",
       "1        Average          3.0  Cat_4            A                 2  \n",
       "2            Low          1.0  Cat_6            B                 1  \n",
       "3           High          2.0  Cat_6            B                 3  \n",
       "4           High          6.0  Cat_6            A                 3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spending Power Data preparation, transfer spending_score into numbers\n",
    "mapping = {'Low': 1, 'Average': 2, 'High': 3}\n",
    "train_data['Numeric_spending'] = train_data['Spending_Score'].map(mapping)\n",
    "train_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profession</th>\n",
       "      <th>Numeric_spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Artist</td>\n",
       "      <td>4063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Executive</td>\n",
       "      <td>1479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>1381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lawyer</td>\n",
       "      <td>1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Engineer</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Doctor</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marketing</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Homemaker</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Profession  Numeric_spending\n",
       "0         Artist              4063\n",
       "5     Healthcare              1495\n",
       "4      Executive              1479\n",
       "3  Entertainment              1381\n",
       "7         Lawyer              1306\n",
       "2       Engineer              1046\n",
       "1         Doctor               953\n",
       "8      Marketing               386\n",
       "6      Homemaker               365"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spending Power Data preparation, Final step\n",
    "Spending_bypro = train_data.groupby('Profession').sum('Numeric_spending').reset_index().sort_values('Numeric_spending', ascending = False)[['Profession', 'Numeric_spending']]\n",
    "Spending_bypro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spending Power by Profession Visualization\n",
    "plt.figure(figsize = (12, 8))\n",
    "bar_plot = sns.barplot(x = 'Profession', y = 'Numeric_spending', data = Spending_bypro)\n",
    "\n",
    "plt.title('Spending by Profession', fontsize = 18)\n",
    "plt.xlabel('Profession', fontsize = 14)\n",
    "plt.ylabel('Numeric Spending', fontsize = 14)\n",
    "\n",
    "plt.xticks(rotation = 45, ha='right', fontsize = 12)\n",
    "\n",
    "for p in bar_plot.patches:\n",
    "    bar_plot.annotate(format(p.get_height(), '.1f'), \n",
    "                      (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                      ha = 'center', va = 'center', \n",
    "                      xytext = (0, 9), \n",
    "                      textcoords = 'offset points',\n",
    "                      fontsize=12)\n",
    "\n",
    "# Showing Plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w4DqftSm6W2+9NbvvvnseeeSR9OjRo173fdttt6Vv377529/+VvOrFQBYPLgmDgDqwaefflr2+IvrvNq0aZNu3brV+/GuvPLKdO7cOVtuuWW97xuA7zZfpwSAenDkkUfm008/zeabb57Zs2dn5MiReeyxx3LmmWfW663vR4wYkeeffz533nlnLrzwwlpfbwfA94eIA4B6sO222+a8887LHXfckVmzZqVLly656KKLcsQRR9Trcfbdd9+0atUqAwcOzOGHH16v+wagGFwTBwAAUCCuiQMAACgQEQcAAFAgrolrYPPmzcu7776b1q1buzgdAAAWY6VSKTNmzMjyyy+fRo0Wfr5NxDWwd999N506dWroMQAAgO+IiRMnZsUVV1zoehHXwFq3bp3k8z+oNm3aNPA0AABAQ5k+fXo6depU0wgLI+Ia2BdfoWzTpo2IAwAAvvIyKzc2AQAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFslhG3IABA9K3b9+GHgMAAKDOFsuIAwAAKCoR918eeuihbLrppqmsrEzHjh1z3HHHZe7cuUmSO+64I+3atUt1dXWS5LnnnktFRUWOO+64mucffPDB6devX4PMDgAAfP81aegBvkveeeed7LzzzhkwYECuueaavPzyyxk0aFCaN2+ek08+OVtttVVmzJiRZ599Nt27d89DDz2UZZZZJqNHj67Zx0MPPZRf/vKXCz3G7NmzM3v27JrH06dPX5Qv6Rt5++238+GHHzb0GIUyd+7czJgxo6HHYDHSunXrNGnir/LaWmaZZbLiiis29BiF4/OgbnwW8G3zWVB3Rf888Kf9Hy655JJ06tQpF198cSoqKrLWWmvl3XffzS9/+cuceOKJadu2bTbccMOMHj063bt3z+jRo/Ozn/0sp5xySqqqqjJt2rSMHz8+PXv2XOgxhg4dmlNOOeVbfFVfz9SpU3PpZZelNG9eQ48CUG8qGjXKz4cMSbt27Rp6lMLweQB8HxX980DE/YeXXnopm2++eSoqKmqW9ejRI1VVVXn77bez0korpWfPnhk9enSOPfbYPPzwwxk6dGhuuummPPLII5kyZUqWX375rL766gs9xvHHH59jjjmm5vH06dPTqVOnRfq6vo5PPvkkpXnz0mn97mneqnVDj1MYpXnVmT1zZkOPwWKkcoklUtGocUOPUQizqmZk4vNj8sknnxT2Q7sh+DyoO58FfNt8FtTN9+HzQMTV0TbbbJOrrroqY8eOTdOmTbPWWmtlm222yejRo/Pxxx9/6Vm4JKmsrExlZeW3NO03t9QKK6XVUss09BgA31jVlA8z8fkxDT1GYfk8AL4vvg+fB25s8h/WXnvtPP744ymVSjXLHn300bRu3brmO7NfXBd3/vnn1wTbFxE3evTobLPNNg0xOgAAsJhYbCNu2rRpee6558p+DjnkkEycODFHHnlkXn755dx222056aSTcswxx6RRo8/fqiWXXDLrr79+rr/++ppg23rrrfPMM8/klVde+cozcQAAAN/EYvt1ytGjR2ejjTYqWzZw4MDcdddd+fnPf54NNtggSy21VAYOHJhf//rXZdv17Nkzzz33XE3ELbXUUunatWvef//9rLnmmt/WSwAAABZDi2XEDR8+PMOHD1/o+ieffPJLn3/BBRfkggsuKFv23HPPffPBAAAAvsJi+3VKAACAIhJxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEC+VsQ9/PDD6devXzbffPO88847SZJrr702jzzySL0OBwAAQLk6R9zNN9+c3r17p0WLFnn22Wcze/bsJMm0adNy5pln1vuAAAAA/FudI+7000/PZZddliuvvDJNmzatWd6jR48888wz9TocAAAA5eoccePGjcvWW2893/K2bdtm6tSp9TETAAAAC1HniFtuueUyfvz4+ZY/8sgj6dy5c70MBQAAwILVOeIGDRqUo446Kk888UQqKiry7rvv5vrrr8+QIUNy2GGHLYoZAQAA+JcmdX3Ccccdl3nz5qVXr16ZOXNmtt5661RWVmbIkCE58sgjF8WMAAAA/EudIq66ujqPPvpoBg8enJ///OcZP358qqqq0rVr17Rq1WpRzQgAAMC/1CniGjdunB122CEvvfRS2rVrl65duy6quQAAAFiAOl8Tt+666+a1115bFLMAAADwFb7W74kbMmRI7rjjjkyaNCnTp08v+wEAAGDRqfONTXbeeeckya677pqKioqa5aVSKRUVFamurq6/6QAAAChT54gbNWrUopgDAACAWqhzxPXs2XNRzAEAAEAt1DnikmTq1KkZNmxYXnrppSTJOuusk5/85Cdp27ZtvQ4HAABAuTrf2GTMmDFZbbXVcv7552fKlCmZMmVKfvvb32a11VbLM888syhmBAAA4F/qfCbuZz/7WXbddddceeWVadLk86fPnTs3Bx98cI4++uj87W9/q/chAQAA+FydI27MmDFlAZckTZo0yS9+8Yt07969XocDAACgXJ2/TtmmTZu89dZb8y2fOHFiWrduXS9DAQAAsGB1jri99947AwcOzI033piJEydm4sSJGTFiRA4++ODsu+++i2JGAAAA/qXOX6c899xzU1FRkf79+2fu3LlJkqZNm+awww7Lb37zm3ofEAAAgH+rc8Q1a9YsF154YYYOHZoJEyYkSVZbbbUsscQS9T4cAAAA5eoccdOmTUt1dXWWWmqprLfeejXLp0yZkiZNmqRNmzb1OiAAAAD/Vudr4vbZZ5+MGDFivuU33XRT9tlnn3oZCgAAgAWrc8Q98cQT+eEPfzjf8m222SZPPPFEvQwFAADAgtU54mbPnl1zQ5P/9Nlnn+XTTz+tl6EAAABYsDpH3KabbporrrhivuWXXXZZNt5443oZCgAAgAWr841NTj/99Gy33XYZO3ZsevXqlSR54IEH8tRTT+Xee++t9wEBAAD4tzqfievRo0cef/zxdOrUKTfddFP+8pe/pEuXLnn++eez1VZbLYoZAQAA+Jc6n4lLkg033DDXX399fc8CAADAV6h1xM2dOzfV1dWprKysWfb+++/nsssuyyeffJJdd901W2655SIZEgAAgM/VOuIGDRqUZs2a5fLLL0+SzJgxI5tssklmzZqVjh075vzzz89tt92WnXfeeZENCwAAsLir9TVxjz76aH784x/XPL7mmmtSXV2dV199NWPHjs0xxxyTc845Z5EMCQAAwOdqHXHvvPNOVl999ZrHDzzwQH784x+nbdu2SZIDDzwwL774Yv1PCAAAQI1aR1zz5s3Lfpn33//+92y22WZl66uqqup3OgAAAMrUOuI23HDDXHvttUmShx9+OO+//3623XbbmvUTJkzI8ssvX/8TAgAAUKPWNzY58cQTs9NOO+Wmm27KpEmTMmDAgHTs2LFm/S233JIePXoskiEBAAD4XK0jrmfPnnn66adz7733Zrnllsuee+5Ztn7DDTfMpptuWu8DAgAA8G91+mXfa6+9dtZee+0FrjvkkEPqZSAAAAAWrtbXxAEAANDwRBwAAECBiDgAAIACEXEAAAAFIuIAAAAKpE53p0ySJZdcMhUVFfMtr6ioSPPmzdOlS5cMGDAgBx10UL0MCAAAwL/VOeJOPPHEnHHGGdlpp51qfi/ck08+mb/+9a8ZPHhwXn/99Rx22GGZO3duBg0aVO8DAwAALM7qHHGPPPJITj/99Bx66KFlyy+//PLce++9ufnmm7P++uvnd7/7nYgDAACoZ3W+Ju6ee+7JdtttN9/yXr165Z577kmS7Lzzznnttde++XQAAACUqXPELbXUUvnLX/4y3/K//OUvWWqppZIkn3zySVq3bl3rfT7++ONp3Lhxdtlll7qOAwAAsFip89cpTzjhhBx22GEZNWpUzTVxTz31VO66665cdtllSZL77rsvPXv2rPU+hw0bliOPPDLDhg3Lu+++m+WXX76uY9VKqVRKdXV1mjSp88sGAAD4TqjzmbhBgwbloYceSsuWLTNy5MiMHDkySyyxRB566KEMHDgwSXLsscfmxhtvrNX+qqqqcuONN+awww7LLrvskuHDhydJ9ttvv+y9995l23722WdZZpllcs011yRJ5s2bl6FDh2bVVVdNixYtssEGG+TPf/5zzfajR49ORUVF7r777my88caprKzMI488kgkTJmS33XZLhw4d0qpVq2yyySa5//77y441adKk7LLLLmnRokVWXXXV/PGPf8wqq6ySCy64oGabqVOn5uCDD0779u3Tpk2bbLvtthk7dmxd31IAAIBa+1qnpHr06JEePXrUywA33XRT1lprray55prp169fjj766Bx//PHZf//9s+eee6aqqiqtWrVK8vn1eDNnzszuu++eJBk6dGiuu+66XHbZZVl99dXzt7/9Lf369Uv79u3LzgQed9xxOffcc9O5c+csueSSmThxYnbeeeecccYZqayszDXXXJM+ffpk3LhxWWmllZIk/fv3z4cffpjRo0enadOmOeaYYzJ58uSy2ffcc8+0aNEid999d9q2bZvLL788vXr1yiuvvFLz1dL/Nnv27MyePbvm8fTp0+vlfVxUZk77uKFHAKgX/j77Zrx/wPfF9+Hvs68VcfPmzcv48eMzefLkzJs3r2zd1ltvXad9DRs2LP369UuS7Ljjjpk2bVoeeuih9O7dOy1btswtt9ySAw44IEnyxz/+Mbvuumtat26d2bNn58wzz8z999+fzTffPEnSuXPnPPLII7n88svLIu7UU0/N9ttvX/N4qaWWygYbbFDz+LTTTsstt9yS22+/PUcccURefvnl3H///XnqqafSvXv3JMn//d//ZfXVV695ziOPPJInn3wykydPTmVlZZLk3HPPza233po///nPOeSQQxb4eocOHZpTTjmlTu9RQ2jZsmWaNG2aVx8b1dCjANSbJk2bpmXLlg09RqH4PAC+j4r+eVDniPv73/+e/fbbL2+++WZKpVLZuoqKilRXV9d6X+PGjcuTTz6ZW2655fNhmjTJ3nvvnWHDhmWbbbbJXnvtleuvvz4HHHBAPvnkk9x2220ZMWJEkmT8+PGZOXNmWZwlyZw5c7LRRhuVLfsixL5QVVWVk08+OXfeeWcmTZqUuXPn5tNPP81bb71VM1eTJk3SrVu3mud06dIlSy65ZM3jsWPHpqqqKksvvXTZvj/99NNMmDBhoa/5+OOPzzHHHFPzePr06enUqdNXvlfftnbt2uWYn/0sn3zySUOPwvfcBx98kJtuuil77bVX2rdv39Dj8D3XsmXLtGvXrqHHKBSfB3xbfB7wbSr650GdI+7QQw9N9+7dc+edd6Zjx46pqKj42gcfNmxY5s6dW3Yjk1KplMrKylx88cXZf//907Nnz0yePDn33XdfWrRokR133DHJ5yGWJHfeeWdWWGGFsv1+cWbsC/9d2UOGDMl9992Xc889N126dEmLFi2yxx57ZM6cObWevaqqKh07dszo0aPnW/dl/4OorKycb77vqnbt2hX6f9wUS/v27ef7v2Xgu8HnAd8mnwfw1eocca+++mr+/Oc/p0uXLt/owHPnzs0111yT8847LzvssEPZur59++aGG27IoYcemk6dOuXGG2/M3XffnT333DNNmzZNknTt2jWVlZV566236nQnzCR59NFHM2DAgJpr66qqqvLGG2/UrF9zzTUzd+7cPPvss9l4442TfH7m7+OP//392W7duuW9995LkyZNssoqq3yNdwAAAKDu6hxxm222WcaPH/+NI+6OO+7Ixx9/nIEDB6Zt27Zl63784x9n2LBhOfTQQ7PffvvlsssuyyuvvJJRo/79ffzWrVtnyJAh+dnPfpZ58+Zlyy23zLRp0/Loo4+mTZs2OfDAAxd67NVXXz0jR45Mnz59UlFRkRNOOKHs2r611lor2223XQ455JBceumladq0aY499ti0aNGi5szjdtttl8033zx9+/bN2WefnTXWWCPvvvtu7rzzzuy+++7zfYUTAACgPtQ54o488sgce+yxee+997LeeuvVnBn7wvrrr1+r/QwbNizbbbfdfAGXfB5xZ599dp5//vnsv//+OeOMM7LyyivPd0fM0047Le3bt8/QoUPz2muvpV27dunWrVv+3//7f1967N/+9rf5yU9+ki222CLLLLNMfvnLX853l8hrrrkmAwcOzNZbb53lllsuQ4cOzYsvvpjmzZsn+fz6v7vuuiu/+tWvctBBB+WDDz7Icsstl6233jodOnSo1XsAAABQVxWl/747yVdo1Gj+Xy1XUVGRUqlU5xubFMnbb7+dTp065f7770+vXr3qbb/Tp09P27ZtM23atLRp06be9gtF8c477+T3v/99Bg8e7BoIgMWYzwOofRvU+Uzc66+//o0GK4oHH3wwVVVVWW+99TJp0qT84he/yCqrrFLnX6EAAABQn+occSuvvPKimOM757PPPsv/+3//L6+99lpat26dLbbYItdff/18Xx8FAAD4NtUq4m6//fbstNNOadq0aW6//fYv3XbXXXetl8EaWu/evdO7d++GHgMAAKBMrSKub9++ee+997Lsssumb9++C93u+3xNHAAAwHdBrSLuP2+//5//DQAAwLdr/ltNAgAA8J1VqzNxv/vd72q9w//93//92sMAAADw5WoVceeff37Z4w8++CAzZ85Mu3btkiRTp07NEksskWWXXVbEAQAALEK1+jrl66+/XvNzxhlnZMMNN8xLL72UKVOmZMqUKXnppZfSrVu3nHbaaYt6XgAAgMVana+JO+GEE3LRRRdlzTXXrFm25ppr5vzzz8+vf/3reh0OAACAcnWOuEmTJmXu3LnzLa+urs77779fL0MBAACwYHWOuF69euWnP/1pnnnmmZplTz/9dA477LBst9129TocAAAA5eoccVdddVWWW265dO/ePZWVlamsrMymm26aDh065P/+7/8WxYwAAAD8S63uTvmf2rdvn7vuuiuvvPJKXn755STJWmutlTXWWKPehwMAAKBcnSPuC2ussYZwAwAA+JbVOeKqq6szfPjwPPDAA5k8eXLmzZtXtv7BBx+st+EAAAAoV+eIO+qoozJ8+PDssssuWXfddVNRUbEo5gIAAGAB6hxxI0aMyE033ZSdd955UcwDAADAl6jz3SmbNWuWLl26LIpZAAAA+Ap1jrhjjz02F154YUql0qKYBwAAgC9R569TPvLIIxk1alTuvvvurLPOOmnatGnZ+pEjR9bbcAAAAJSrc8S1a9cuu++++6KYBQAAgK9Q54i7+uqrF8UcAAAA1EKdr4lLkrlz5+b+++/P5ZdfnhkzZiRJ3n333VRVVdXrcAAAAJSr85m4N998MzvuuGPeeuutzJ49O9tvv31at26ds846K7Nnz85ll122KOYEAAAgX+NM3FFHHZXu3bvn448/TosWLWqW77777nnggQfqdTgAAADK1flM3MMPP5zHHnsszZo1K1u+yiqr5J133qm3wQAAAJhfnc/EzZs3L9XV1fMtf/vtt9O6det6GQoAAIAFq3PE7bDDDrngggtqHldUVKSqqionnXRSdt555/qcDQAAgP9S569Tnnfeeendu3e6du2aWbNmZb/99surr76aZZZZJjfccMOimBEAAIB/qXPErbjiihk7dmxGjBiR559/PlVVVRk4cGD233//shudAAAAUP/qHHFJ0qRJk/Tr16++ZwEAAOArfK2IGzduXC666KK89NJLSZK11147RxxxRNZaa616HQ4AAIBydb6xyc0335x11103Tz/9dDbYYINssMEGeeaZZ7Leeuvl5ptvXhQzAgAA8C91PhP3i1/8Iscff3xOPfXUsuUnnXRSfvGLX+THP/5xvQ0HAABAuTqfiZs0aVL69+8/3/J+/fpl0qRJ9TIUAAAAC1bniNtmm23y8MMPz7f8kUceyVZbbVUvQwEAALBgdf465a677ppf/vKXefrpp/ODH/wgSfL3v/89f/rTn3LKKafk9ttvL9sWAACA+lPniDv88MOTJJdcckkuueSSBa5LkoqKilRXV3/D8QAAAPhPdY64efPmLYo5AAAAqIU6XxMHAABAw6l1xD3++OO54447ypZdc801WXXVVbPsssvmkEMOyezZs+t9QAAAAP6t1hF36qmn5sUXX6x5/MILL2TgwIHZbrvtctxxx+Uvf/lLhg4dukiGBAAA4HO1jrjnnnsuvXr1qnk8YsSIbLbZZrnyyitzzDHH5He/+11uuummRTIkAAAAn6t1xH388cfp0KFDzeOHHnooO+20U83jTTbZJBMnTqzf6QAAAChT64jr0KFDXn/99STJnDlz8swzz9T8nrgkmTFjRpo2bVr/EwIAAFCj1hG3884757jjjsvDDz+c448/PksssUS22mqrmvXPP/98VltttUUyJAAAAJ+r9e+JO+200/I///M/6dmzZ1q1apU//OEPadasWc36q666KjvssMMiGRIAAIDP1Trilllmmfztb3/LtGnT0qpVqzRu3Lhs/Z/+9Ke0atWq3gcEAADg32odcV9o27btApcvtdRS33gYAAAAvlytr4kDAACg4Yk4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEAQAAFIiIAwAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAABQICIOAACgQEQcAABAgYg4AACAAhFxAAAABSLiAAAACkTEJRk+fHjatWtXp+cMGDAgffv2XSTzAAAALMz3PuIWFlujR49ORUVFpk6dmr333juvvPLKtz8cAABAHTVp6AG+C1q0aJEWLVo09BgAAABf6Xt/Jq42FvR1ytNPPz3LLrtsWrdunYMPPjjHHXdcNtxww/mee+6556Zjx45ZeumlM3jw4Hz22WffztAAAMBiyZm4Bbj++utzxhln5JJLLkmPHj0yYsSInHfeeVl11VXLths1alQ6duyYUaNGZfz48dl7772z4YYbZtCgQQvd9+zZszN79uyax9OnT19kr4Nv35w5c/LBBx809BiF8sX75X37etq3b59mzZo19BjAf/BZ8PX4PPj6fBYsfhaLiLvjjjvSqlWrsmXV1dUL3f6iiy7KwIEDc9BBByVJTjzxxNx7772pqqoq227JJZfMxRdfnMaNG2ettdbKLrvskgceeOBLI27o0KE55ZRTvsGr4bvsgw8+yO9///uGHqOQbrrppoYeoZAGDx6cFVZYoaHHAP6Dz4JvxudB3fksWPwsFhH3wx/+MJdeemnZsieeeCL9+vVb4Pbjxo3L4YcfXrZs0003zYMPPli2bJ111knjxo1rHnfs2DEvvPDCl85y/PHH55hjjql5PH369HTq1KlWr4Pvvvbt22fw4MENPQaLkfbt2zf0CMB/8VnAt81nweJnsYi4li1bpkuXLmXL3n777W+836ZNm5Y9rqioyLx58770OZWVlamsrPzGx+a7qVmzZv4lDGAx57MAWNTc2GQB1lxzzTz11FNly/77MQAAQENYLM7E1dWRRx6ZQYMGpXv37tliiy1y44035vnnn0/nzp0bejQAAGAxJ+IWYP/9989rr72WIUOGZNasWdlrr70yYMCAPPnkkw09GgAAsJirKJVKpYYeogi23377LLfccrn22mvrdb/Tp09P27ZtM23atLRp06Ze9w0AABRHbdvAmbgFmDlzZi677LL07t07jRs3zg033JD7778/9913X0OPBgAALOZE3AJUVFTkrrvuyhlnnJFZs2ZlzTXXzM0335ztttuuoUcDAAAWcyJuAVq0aJH777+/occAAACYj18xAAAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBAmjT0AIu7UqmUJJk+fXoDTwIAADSkL5rgi0ZYGBHXwGbMmJEk6dSpUwNPAgAAfBfMmDEjbdu2Xej6itJXZR6L1Lx58/Luu++mdevWqaioaOhx4Fs3ffr0dOrUKRMnTkybNm0aehwAGojPA/j8DNyMGTOy/PLLp1GjhV/55kxcA2vUqFFWXHHFhh4DGlybNm18aAPg84DF3pedgfuCG5sAAAAUiIgDAAAoEBEHNKjKysqcdNJJqaysbOhRAGhAPg+g9tzYBAAAoECciQMAACgQEQcAAFAgIg4AAKBARBzwnfPGG2+koqIizz33XEOPAsB33CqrrJILLrigoceAb5WIA+rFgAEDUlFRkUMPPXS+dYMHD05FRUUGDBjw7Q8GQL354u/6//4ZP358Q48GixURB9SbTp06ZcSIEfn0009rls2aNSt//OMfs9JKKzXgZADUlx133DGTJk0q+1l11VUbeixYrIg4oN5069YtnTp1ysiRI2uWjRw5MiuttFI22mijmmV//etfs+WWW6Zdu3ZZeuml86Mf/SgTJkz40n3/4x//yE477ZRWrVqlQ4cOOeCAA/Lhhx8ustcCwIJVVlZmueWWK/tp3LhxbrvttnTr1i3NmzdP586dc8opp2Tu3Lk1z6uoqMjll1+eH/3oR1liiSWy9tpr5/HHH8/48eOzzTbbpGXLltliiy3KPg8mTJiQ3XbbLR06dEirVq2yySab5P777//S+aZOnZqDDz447du3T5s2bbLttttm7Nixi+z9gIYg4oB69ZOf/CRXX311zeOrrroqBx10UNk2n3zySY455piMGTMmDzzwQBo1apTdd9898+bNW+A+p06dmm233TYbbbRRxowZk7/+9a95//33s9deey3S1wJA7Tz88MPp379/jjrqqPzzn//M5ZdfnuHDh+eMM84o2+60005L//7989xzz2WttdbKfvvtl5/+9Kc5/vjjM2bMmJRKpRxxxBE121dVVWXnnXfOAw88kGeffTY77rhj+vTpk7feemuhs+y5556ZPHly7r777jz99NPp1q1bevXqlSlTpiyy1w/fuhJAPTjwwANLu+22W2ny5MmlysrK0htvvFF64403Ss2bNy998MEHpd1226104IEHLvC5H3zwQSlJ6YUXXiiVSqXS66+/XkpSevbZZ0ulUql02mmnlXbYYYey50ycOLGUpDRu3LhF+bIA+A8HHnhgqXHjxqWWLVvW/Oyxxx6lXr16lc4888yyba+99tpSx44dax4nKf3617+uefz444+XkpSGDRtWs+yGG24oNW/e/EtnWGeddUoXXXRRzeOVV165dP7555dKpVLp4YcfLrVp06Y0a9assuesttpqpcsvv7zOrxe+q5o0aEEC3zvt27fPLrvskuHDh6dUKmWXXXbJMsssU7bNq6++mhNPPDFPPPFEPvzww5ozcG+99VbWXXfd+fY5duzYjBo1Kq1atZpv3YQJE7LGGmssmhcDwHx++MMf5tJLL6153LJly6y//vp59NFHy868VVdXZ9asWZk5c2aWWGKJJMn6669fs75Dhw5JkvXWW69s2axZszJ9+vS0adMmVVVVOfnkk3PnnXdm0qRJmTt3bj799NOFnokbO3ZsqqqqsvTSS5ct//TTT7/ya/tQJCIOqHc/+clPar4O8/vf/36+9X369MnKK6+cK6+8Mssvv3zmzZuXddddN3PmzFng/qqqqtKnT5+cddZZ863r2LFj/Q4PwJdq2bJlunTpUrasqqoqp5xySv7nf/5nvu2bN29e899Nmzat+e+KioqFLvviH/eGDBmS++67L+eee266dOmSFi1aZI899vjSz4uOHTtm9OjR861r165d7V4gFICIA+rdjjvumDlz5qSioiK9e/cuW/fRRx9l3LhxufLKK7PVVlslSR555JEv3V+3bt1y8803Z5VVVkmTJv7aAviu6datW8aNGzdf3H1Tjz76aAYMGJDdd989yeeR9sYbb3zpHO+9916aNGmSVVZZpV5nge8SNzYB6l3jxo3z0ksv5Z///GcaN25ctm7JJZfM0ksvnSuuuCLjx4/Pgw8+mGOOOeZL9zd48OBMmTIl++67b5566qlMmDAh99xzTw466KBUV1cvypcCQC2ceOKJueaaa3LKKafkxRdfzEsvvZQRI0bk17/+9Tfa7+qrr56RI0fmueeey9ixY7Pffvst9CZYSbLddttl8803T9++fXPvvffmjTfeyGOPPZZf/epXGTNmzDeaBb5LRBywSLRp0yZt2rSZb3mjRo0yYsSIPP3001l33XXzs5/9LOecc86X7mv55ZfPo48+murq6uywww5Zb731cvTRR6ddu3Zp1MhfYwANrXfv3rnjjjty7733ZpNNNskPfvCDnH/++Vl55ZW/0X5/+9vfZskll8wWW2yRPn36pHfv3unWrdtCt6+oqMhdd92VrbfeOgcddFDWWGON7LPPPnnzzTdrrsGD74OKUqlUaughAAAAqB3/hA0AAFAgIg4AAKBARBwAAECBiDgAAIACEXEAAAAFIuIAAAAKRMQBAAAUiIgDAAAoEBEHAAWwzTbb5Oijj27oMQD4DhBxAFBL7733Xo466qh06dIlzZs3T4cOHdKjR49ceumlmTlzZkOPB8BioklDDwAARfDaa6+lR48eadeuXc4888yst956qayszAsvvJArrrgiK6ywQnbdddeGHnOhqqurU1FRkUaN/PstQNH5mxwAauHwww9PkyZNMmbMmOy1115Ze+2107lz5+y22265884706dPnyTJ1KlTc/DBB6d9+/Zp06ZNtt1224wdO7ZmPyeffHI23HDDXHvttVlllVXStm3b7LPPPpkxY0bNNp988kn69++fVq1apWPHjjnvvPPmm2f27NkZMmRIVlhhhbRs2TKbbbZZRo8eXbN++PDhadeuXW6//fZ07do1lZWVeeuttxbdGwTAt0bEAcBX+Oijj3Lvvfdm8ODBadmy5QK3qaioSJLsueeemTx5cu6+++48/fTT6datW3r16pUpU6bUbDthwoTceuutueOOO3LHHXfkoYceym9+85ua9T//+c/z0EMP5bbbbsu9996b0aNH55lnnik73hFHHJHHH388I0aMyPPPP58999wzO+64Y1599dWabWbOnJmzzjor//d//5cXX3wxyy67bH2+LQA0EF+nBICvMH78+JRKpay55pply5dZZpnMmjUrSTJ48OD06dMnTz75ZCZPnpzKysokybnnnptbb701f/7zn3PIIYckSebNm5fhw4endevWSZIDDjggDzzwQM4444xUVVVl2LBhue6669KrV68kyR/+8IesuOKKNcd96623cvXVV+ett97K8ssvnyQZMmRI/vrXv+bqq6/OmWeemST57LPPcskll2SDDTZYhO8OAN82EQcAX9OTTz6ZefPmZf/998/s2bMzduzYVFVVZemlly7b7tNPP82ECRNqHq+yyio1AZckHTt2zOTJk5N8fpZuzpw52WyzzWrWL7XUUmUB+cILL6S6ujprrLFG2XFmz55dduxmzZpl/fXXr58XC8B3hogDgK/QpUuXVFRUZNy4cWXLO3funCRp0aJFkqSqqiodO3YsuzbtC+3atav576ZNm5atq6ioyLx582o9T1VVVRo3bpynn346jRs3LlvXqlWrmv9u0aJFzdc8Afj+EHEA8BWWXnrpbL/99rn44otz5JFHLvS6uG7duuW9995LkyZNssoqq3ytY6222mpp2rRpnnjiiay00kpJko8//jivvPJKevbsmSTZaKONUl1dncmTJ2errbb6WscBoLjc2AQAauGSSy7J3Llz071799x444156aWXMm7cuFx33XV5+eWX07hx42y33XbZfPPN07dv39x7771544038thjj+VXv/pVxowZU6vjtGrVKgMHDszPf/7zPPjgg/nHP/6RAQMGlP1qgDXWWCP7779/+vfvn5EjR+b111/Pk08+maFDh+bOO+9cVG8BAN8RzsQBQC2sttpqefbZZ3PmmWfm+OOPz9tvv53Kysp07do1Q4YMyeGHH56Kiorcdddd+dWvfpWDDjooH3zwQZZbbrlsvfXW6dChQ62Pdc4556Sqqip9+vRJ69atc+yxx2batGll21x99dU5/fTTc+yxx+add97JMssskx/84Af50Y9+VN8vHYDvmIpSqVRq6CEAAACoHV+nBAAAKBARBwAAUCAiDgAAoEBEHAAAQIGIOAAAgAIRcQAAAAUi4gAAAApExAEAABSIiAMAACgQEQcAAFAgIg4AAKBA/j+wwwgR4yFxWQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the boxplot for Spending by Gender\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(x='Gender', y='Spending_Score', data=train_data, color = \"lightblue\")\n",
    "plt.title('Spending by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Work Experience Analysis\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(x = 'Gender', y = 'Work_Experience', data = train_data)\n",
    "plt.title('Work Experience by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Work Experience')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graduation Status and Profession\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.countplot(x = 'Profession', hue = 'Graduated', data = train_data)\n",
    "plt.title('Profession Distribution by Graduation Status')\n",
    "plt.xlabel('Profession')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Marital Status and Spending\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(x = 'Ever_Married', y = 'Spending_Score', data = train_data)\n",
    "plt.title('Spending by Marital Status')\n",
    "plt.xlabel('Ever Married')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix\n",
    "\n",
    "We aim to create a correlation matrix of this dataset, however, there are a lot of categorical values in the dataset. Let us use LabelEncoder first before accessing the correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>462643</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466315</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461735</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>462669</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender  Ever_Married  Age  Graduated  Profession  Work_Experience  \\\n",
       "0  462809       1             0   22          0           5              1.0   \n",
       "1  462643       0             1   38          1           2              1.0   \n",
       "2  466315       0             1   67          1           2              1.0   \n",
       "3  461735       1             1   67          1           7              0.0   \n",
       "4  462669       0             1   40          1           3              0.0   \n",
       "\n",
       "   Spending_Score  Family_Size  Var_1  Segmentation  \n",
       "0               2          4.0      3             3  \n",
       "1               0          3.0      3             0  \n",
       "2               2          1.0      5             1  \n",
       "3               1          2.0      5             1  \n",
       "4               1          6.0      5             0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a label encoder objects\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# List of Categorical values in the data\n",
    "categorical_columns = [\n",
    "    'Gender',\n",
    "    'Ever_Married',\n",
    "    'Graduated',\n",
    "    'Profession',\n",
    "    'Spending_Score',\n",
    "    'Var_1',\n",
    "    'Segmentation'\n",
    "]\n",
    "\n",
    "# Create a new dataframe to store data\n",
    "numerical_train = train_data.copy().drop(columns = ['Numeric_spending'])\n",
    "\n",
    "# Apply label encoding to each categorical columns\n",
    "for column in categorical_columns:\n",
    "    numerical_train[column] = label_encoder.fit_transform(numerical_train[column].astype(str))\n",
    "    \n",
    "# Check the results\n",
    "numerical_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Matrix Visualization\n",
    "plt.figure(figsize = (8, 6))\n",
    "corr_matrix = numerical_train.corr()\n",
    "sns.heatmap(corr_matrix, annot = True, cmap = 'coolwarm', linewidths = 0.5)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part III: Model Selection\n",
    "\n",
    "Analysis:  \n",
    "Throughout this part in Modeling, we will be mainly focuses in these areas:\n",
    "1. Predictive Modeling for Spending Scores\n",
    "    - Preferrably Linear Regression as we will transform Spending_Score to numerical\n",
    "    - Decision Trees/Random Forest Regressor\n",
    "    - Gradient Boosting Regressor\n",
    "    - Support Vector Regressor\n",
    "2. Customer Segmentation\n",
    "    - Create new segments of customers based on clustering\n",
    "    - For example: K-Means Clustering / Hierarchical Clustering\n",
    "3. Customer Segmentation Prediction (Classification)\n",
    "    - Predict customer segment (A, B, C, D) for new potential customers based on fetures in the training data\n",
    "    - Logistic Regression / Random Forest Classifier / Gradient Boosting Classifier\n",
    "4. Feature Importance Analysis\n",
    "    - Identify which features are most important in predicting the target values, like Spending_score\n",
    "    - For example: Random Forest / Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dependent and independent variables (or features and target)\n",
    "X_train = numerical_train.drop(columns = ['Spending_Score', 'Segmentation'])\n",
    "Y_train = numerical_train['Spending_Score']\n",
    "\n",
    "# Create the categorical columns for test now,\n",
    "# since it does not have 'Segmentation'\n",
    "categorical_columns_test = [\n",
    "    'Gender',\n",
    "    'Ever_Married',\n",
    "    'Graduated',\n",
    "    'Profession',\n",
    "    'Var_1',\n",
    "    'Spending_Score'\n",
    "]\n",
    "\n",
    "# Copy and paste the test_data so that it won't modify the original data\n",
    "numerical_test = test_data.copy()\n",
    "\n",
    "# Label Encodering\n",
    "for column in categorical_columns_test:\n",
    "    numerical_test[column] = label_encoder.fit_transform(numerical_test[column].astype(str))\n",
    "\n",
    "# Define dep and indep vars again\n",
    "X_test = numerical_test.drop(columns = ['Spending_Score'])\n",
    "Y_test = numerical_test['Spending_Score']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression MSE: 0.4134\n"
     ]
    }
   ],
   "source": [
    "# Start and train the Linear Regression Model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "Y_pred_lr = lr_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_lr = mean_squared_error(Y_test, Y_pred_lr)\n",
    "print(f'Linear Regression MSE: {mse_lr:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Regressor MSE: 0.3689\n"
     ]
    }
   ],
   "source": [
    "# Start and train the Decision Tree Regressor Model\n",
    "dt_model = DecisionTreeRegressor(random_state = 208)\n",
    "dt_model.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions\n",
    "Y_pred_dt = dt_model.predict(X_test)\n",
    "\n",
    "# Evaluate model\n",
    "mse_dt = mean_squared_error(Y_test, Y_pred_dt)\n",
    "print(f'Decision Tree Regressor MSE: {mse_dt:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor MSE: 0.1973\n"
     ]
    }
   ],
   "source": [
    "# Start and train\n",
    "rf_model = RandomForestRegressor(n_estimators = 100, random_state = 208)\n",
    "rf_model.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions\n",
    "Y_pred_rf = rf_model.predict(X_test)\n",
    "\n",
    "# Evaluate\n",
    "mse_rf = mean_squared_error(Y_test, Y_pred_rf)\n",
    "print(f'Random Forest Regressor MSE: {mse_rf:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regressor MSE: 0.3044\n"
     ]
    }
   ],
   "source": [
    "# Start and train\n",
    "gbm_model = GradientBoostingRegressor(n_estimators = 100, random_state = 208)\n",
    "gbm_model.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions\n",
    "Y_pred_gbm = gbm_model.predict(X_test)\n",
    "\n",
    "# Evaluate Model\n",
    "mse_gbm = mean_squared_error(Y_test, Y_pred_gbm)\n",
    "print(f'Gradient Boosting Regressor MSE: {mse_gbm:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Regressor MSE: 0.9840\n"
     ]
    }
   ],
   "source": [
    "# Start and train\n",
    "svr_model = SVR()\n",
    "svr_model.fit(X_train, Y_train)\n",
    "\n",
    "# Make predictions\n",
    "Y_pred_svr = svr_model.predict(X_test)\n",
    "\n",
    "# Evaluate model\n",
    "mse_svr = mean_squared_error(Y_test, Y_pred_svr)\n",
    "print(f'Support Vector Regressor MSE: {mse_svr:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the SVR result above, it isn't the result we wanted to see, mainly due to an issue in choice of kernel, hyperparameters or even the nature of data itself. \n",
    "  \n",
    "Below, we are going to explore ways to improve SVR performance (MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the data before implementing SVR improvements\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "# Define parameter grids for GridSearchCV\n",
    "param_grid = {\n",
    "    'kernel' : ['linear', 'poly', 'rbf'],\n",
    "    'C' : [0.1, 1, 10],\n",
    "    'epsilon' : [0.01, 0.1, 1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Support Vector Regressor MSE: 0.3378\n"
     ]
    }
   ],
   "source": [
    "# GridSearchCV\n",
    "svr = SVR()\n",
    "grid_search = GridSearchCV(svr, param_grid, cv = 5, scoring = 'neg_mean_squared_error', n_jobs = -1)\n",
    "grid_search.fit(X_train_scaled, Y_train)\n",
    "\n",
    "# Get the model from gridsearchcv\n",
    "best_svr = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions\n",
    "Y_pred_svr_scaled = best_svr.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate models\n",
    "mse_svr_scaled = mean_squared_error(Y_test, Y_pred_svr_scaled)\n",
    "print(f'Scaled Support Vector Regressor MSE: {mse_svr_scaled:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictive Modeling Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.413417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>0.368862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>0.197269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>0.304380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scaled Support Vector Regressor</td>\n",
       "      <td>0.337755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MSE\n",
       "0                Linear Regression  0.413417\n",
       "1          Decision Tree Regressor  0.368862\n",
       "2          Random Forest Regressor  0.197269\n",
       "3      Gradient Boosting Regressor  0.304380\n",
       "4  Scaled Support Vector Regressor  0.337755"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Model Performance\n",
    "mse_performance_summary = {\n",
    "    'Model': [\n",
    "        'Linear Regression',\n",
    "        'Decision Tree Regressor',\n",
    "        'Random Forest Regressor',\n",
    "        'Gradient Boosting Regressor',\n",
    "        'Scaled Support Vector Regressor'\n",
    "    ],\n",
    "    'MSE': [\n",
    "        mse_lr,\n",
    "        mse_dt,\n",
    "        mse_rf,\n",
    "        mse_gbm,\n",
    "        mse_svr_scaled\n",
    "    ]\n",
    "}\n",
    "\n",
    "performance_df = pd.DataFrame(mse_performance_summary)\n",
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Segmentation Clustering\n",
    "\n",
    "Beforing doing clustering modeling, we need to standardize our whole dataset before using clustering methods like KMeans.\n",
    "  \n",
    "Objective: Create new segments of customer based on clustering to understand different customer groups.\n",
    "Approach: Use clustering techniques to identify distinct customer segments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we need to standardize features\n",
    "scaled_train_data = scaler.fit_transform(numerical_train.drop(columns = ['ID'])) # since we dont need to use 'ID' column\n",
    "\n",
    "# Second, find out optimal # of k using elbow method\n",
    "wcss = [] # within-cluster sum of square for elbow method\n",
    "for i in range(1, 11): # use 10\n",
    "    kmeans = KMeans(\n",
    "        n_clusters = i,\n",
    "        init = 'k-means++',\n",
    "        max_iter = 300,\n",
    "        n_init = 10,\n",
    "        random_state = 208\n",
    "    )\n",
    "    kmeans.fit(scaled_train_data)\n",
    "    wcss.append(kmeans.inertia_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thirdly, plot the elbow methods in graph for better visz\n",
    "plt.figure(figsize = (8, 6))\n",
    "plt.plot(range(1, 11), wcss, marker = 'o')\n",
    "plt.title('Elbow Method for KMeans Clustering')\n",
    "plt.xlabel('# of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>463462.096995</td>\n",
       "      <td>0.454463</td>\n",
       "      <td>0.414845</td>\n",
       "      <td>41.377049</td>\n",
       "      <td>0.828324</td>\n",
       "      <td>1.228142</td>\n",
       "      <td>3.112022</td>\n",
       "      <td>1.988160</td>\n",
       "      <td>1.913934</td>\n",
       "      <td>4.196721</td>\n",
       "      <td>0.801002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>463554.754498</td>\n",
       "      <td>0.586565</td>\n",
       "      <td>0.997201</td>\n",
       "      <td>46.648541</td>\n",
       "      <td>0.757297</td>\n",
       "      <td>1.493003</td>\n",
       "      <td>2.572171</td>\n",
       "      <td>0.240304</td>\n",
       "      <td>3.207117</td>\n",
       "      <td>4.151939</td>\n",
       "      <td>1.410636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>463415.966318</td>\n",
       "      <td>0.539372</td>\n",
       "      <td>0.086937</td>\n",
       "      <td>27.721438</td>\n",
       "      <td>0.284479</td>\n",
       "      <td>4.321802</td>\n",
       "      <td>2.865726</td>\n",
       "      <td>1.969504</td>\n",
       "      <td>3.739190</td>\n",
       "      <td>3.817478</td>\n",
       "      <td>2.609012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>463468.670358</td>\n",
       "      <td>0.653322</td>\n",
       "      <td>0.959114</td>\n",
       "      <td>70.063884</td>\n",
       "      <td>0.581772</td>\n",
       "      <td>5.624361</td>\n",
       "      <td>1.333049</td>\n",
       "      <td>1.429302</td>\n",
       "      <td>2.153322</td>\n",
       "      <td>4.602215</td>\n",
       "      <td>1.348382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ID    Gender  Ever_Married        Age  Graduated  \\\n",
       "Cluster                                                                \n",
       "0        463462.096995  0.454463      0.414845  41.377049   0.828324   \n",
       "1        463554.754498  0.586565      0.997201  46.648541   0.757297   \n",
       "2        463415.966318  0.539372      0.086937  27.721438   0.284479   \n",
       "3        463468.670358  0.653322      0.959114  70.063884   0.581772   \n",
       "\n",
       "         Profession  Work_Experience  Spending_Score  Family_Size     Var_1  \\\n",
       "Cluster                                                                       \n",
       "0          1.228142         3.112022        1.988160     1.913934  4.196721   \n",
       "1          1.493003         2.572171        0.240304     3.207117  4.151939   \n",
       "2          4.321802         2.865726        1.969504     3.739190  3.817478   \n",
       "3          5.624361         1.333049        1.429302     2.153322  4.602215   \n",
       "\n",
       "         Segmentation  \n",
       "Cluster                \n",
       "0            0.801002  \n",
       "1            1.410636  \n",
       "2            2.609012  \n",
       "3            1.348382  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply K-Means with the optimal number of clsuter based on graph above\n",
    "kmeans = KMeans(\n",
    "    n_clusters = 4,\n",
    "    init = 'k-means++',\n",
    "    max_iter = 300,\n",
    "    n_init = 10,\n",
    "    random_state = 208\n",
    ")\n",
    "clusters = kmeans.fit_predict(scaled_train_data)\n",
    "\n",
    "# Add labels to the `numerical_train` dataframe\n",
    "numerical_train['Cluster'] = clusters\n",
    "\n",
    "# Analyze the clusters\n",
    "cluster_analysis = numerical_train.groupby('Cluster').mean()\n",
    "cluster_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of the clustering above\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.scatterplot(\n",
    "    x = numerical_train['Age'],\n",
    "    y = numerical_train['Work_Experience'],\n",
    "    hue = numerical_train['Cluster'],\n",
    "    palette = 'RdYlBu'\n",
    ")\n",
    "plt.title('Customer Segmentation by Age and Work Experience')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Work Expereince')\n",
    "plt.legend(title = 'Cluster(s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Segmentation Prediction (CSP) (Using Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then, seperate features from data\n",
    "X_train_class = numerical_train.drop(columns = ['Segmentation', 'ID', 'Cluster']) # remove cluster as we do not need it here\n",
    "Y_train_class = numerical_train['Segmentation']\n",
    "X_test_class = numerical_test.drop(columns = ['ID'])\n",
    "# Y_test_class does not exist as there is no Segmentation in test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "logreg_model = LogisticRegression(max_iter = 1000, random_state = 208)\n",
    "logreg_model.fit(X_train_class, Y_train_class)\n",
    "\n",
    "# Predict segments for the test data\n",
    "test_data['LogReg_Segmentation'] = logreg_model.predict(X_test_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Classifer\n",
    "rfc_model = RandomForestClassifier(n_estimators = 100, random_state = 208)\n",
    "rfc_model.fit(X_train_class, Y_train_class)\n",
    "\n",
    "# Predict segments\n",
    "test_data['RFC_Segmentation'] = rfc_model.predict(X_test_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient Boosting Classifer\n",
    "gbc_model = GradientBoostingClassifier(n_estimators = 100, random_state = 208)\n",
    "gbc_model.fit(X_train_class, Y_train_class)\n",
    "\n",
    "# Predict segments\n",
    "test_data['GBC_Segmentation'] = gbc_model.predict(X_test_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ID  LogReg_Segmentation  RFC_Segmentation  GBC_Segmentation\n",
      "0  458989                    0                 1                 0\n",
      "1  458994                    2                 2                 1\n",
      "2  458996                    0                 0                 3\n",
      "3  459000                    0                 2                 1\n",
      "4  459001                    3                 3                 3\n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of `test_data` with the predicted segments\n",
    "print(test_data[['ID', 'LogReg_Segmentation', 'RFC_Segmentation', 'GBC_Segmentation']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ID LogReg_Segmentation RFC_Segmentation GBC_Segmentation\n",
      "0  458989                   A                B                A\n",
      "1  458994                   C                C                B\n",
      "2  458996                   A                A                D\n",
      "3  459000                   A                C                B\n",
      "4  459001                   D                D                D\n",
      "5  459003                   C                C                B\n",
      "6  459005                   B                A                C\n",
      "7  459008                   C                C                C\n",
      "8  459013                   C                C                C\n",
      "9  459014                   D                D                D\n"
     ]
    }
   ],
   "source": [
    "# Decode the predicted segments back to the original labels (A, B, C, D)\n",
    "# test_data['LogReg_Segmentation'] = label_encoder.inverse_transform(test_data['LogReg_Segmentation'])\n",
    "# test_data['RFC_Segmentation'] = label_encoder.inverse_transform(test_data['RFC_Segmentation'])\n",
    "# test_data['GBC_Segmentation'] = label_encoder.inverse_transform(test_data['GBC_Segmentation'])\n",
    "# Tried inverse transform, did not work\n",
    "\n",
    "# Define the labels to be mapping\n",
    "label_mapping = {\n",
    "    0 : 'A',\n",
    "    1 : 'B',\n",
    "    2 : 'C',\n",
    "    3 : 'D'\n",
    "}\n",
    "\n",
    "# Mannually transform the labels to original labels\n",
    "test_data['LogReg_Segmentation'] = test_data['LogReg_Segmentation'].map(label_mapping)\n",
    "test_data['RFC_Segmentation'] = test_data['RFC_Segmentation'].map(label_mapping)\n",
    "test_data['GBC_Segmentation'] = test_data['GBC_Segmentation'].map(label_mapping)\n",
    "\n",
    "# Display the first 5 rows again to verify it works\n",
    "print(test_data[['ID', 'LogReg_Segmentation', 'RFC_Segmentation', 'GBC_Segmentation']].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   LogReg_Segmentation  RFC_Segmentation  GBC_Segmentation\n",
      "A                  713               629               718\n",
      "B                  244               568               472\n",
      "C                  783               638               609\n",
      "D                  887               792               828\n"
     ]
    }
   ],
   "source": [
    "# Now, with the Segmentations predicted by LogReg, RFC, and GBC,\n",
    "# let us do a comparison consistency of predictions\n",
    "consistency_comparison = test_data[['LogReg_Segmentation', 'RFC_Segmentation', 'GBC_Segmentation']].apply(pd.Series.value_counts)\n",
    "print(consistency_comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the results are in, we do not know which method we can trust than other methods to potentially decrease costs in developing these models when new customers come in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.44      0.43      1972\n",
      "           1       0.36      0.14      0.20      1858\n",
      "           2       0.48      0.61      0.54      1970\n",
      "           3       0.61      0.74      0.67      2268\n",
      "\n",
      "    accuracy                           0.50      8068\n",
      "   macro avg       0.47      0.48      0.46      8068\n",
      "weighted avg       0.47      0.50      0.47      8068\n",
      "\n",
      "Logistic Regression Confusion Matrix:\n",
      "[[ 876  189  412  495]\n",
      " [ 586  260  749  263]\n",
      " [ 282  202 1193  293]\n",
      " [ 395   77  128 1668]]\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation Accuracy for Logistic Regression\n",
    "logreg_cv_scores = cross_val_score(logreg_model, X_train_class, Y_train_class, cv = 5, scoring = 'accuracy')\n",
    "Y_train_logreg_pred = cross_val_predict(logreg_model, X_train_class, Y_train_class, cv = 5)\n",
    "\n",
    "print(\"Logistic Regression Classification Report:\")\n",
    "print(classification_report(Y_train_class, Y_train_logreg_pred))\n",
    "print(\"Logistic Regression Confusion Matrix:\")\n",
    "print(confusion_matrix(Y_train_class, Y_train_logreg_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.41      0.41      0.41      1972\n",
      "           1       0.35      0.32      0.34      1858\n",
      "           2       0.51      0.50      0.51      1970\n",
      "           3       0.62      0.67      0.64      2268\n",
      "\n",
      "    accuracy                           0.49      8068\n",
      "   macro avg       0.47      0.48      0.47      8068\n",
      "weighted avg       0.48      0.49      0.48      8068\n",
      "\n",
      "Random Forest Classifier Confusion Matrix:\n",
      "[[ 807  460  262  443]\n",
      " [ 461  603  560  234]\n",
      " [ 259  456  988  267]\n",
      " [ 435  182  130 1521]]\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation for Random Forest Classifier\n",
    "rfc_cv_scores = cross_val_score(rfc_model, X_train_class, Y_train_class, cv = 5, scoring = 'accuracy')\n",
    "Y_train_rfc_pred = cross_val_predict(rfc_model, X_train_class, Y_train_class, cv = 5)\n",
    "\n",
    "print(\"Random Forest Classifier Classification Report:\")\n",
    "print(classification_report(Y_train_class, Y_train_rfc_pred))\n",
    "print(\"Random Forest Classifier Confusion Matrix:\")\n",
    "print(confusion_matrix(Y_train_class, Y_train_rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.45      0.45      1972\n",
      "           1       0.41      0.33      0.37      1858\n",
      "           2       0.58      0.58      0.58      1970\n",
      "           3       0.63      0.73      0.68      2268\n",
      "\n",
      "    accuracy                           0.53      8068\n",
      "   macro avg       0.52      0.52      0.52      8068\n",
      "weighted avg       0.52      0.53      0.53      8068\n",
      "\n",
      "Gradient Boosting Classifier Confusion Matrix:\n",
      "[[ 896  418  222  436]\n",
      " [ 458  621  543  236]\n",
      " [ 199  353 1138  280]\n",
      " [ 446  111   58 1653]]\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation for Gradient Boosting Classifier\n",
    "gbc_cv_scores = cross_val_score(gbc_model, X_train_class, Y_train_class, cv = 5, scoring = 'accuracy')\n",
    "Y_train_gbc_pred = cross_val_predict(gbc_model, X_train_class, Y_train_class, cv = 5)\n",
    "\n",
    "print(\"Gradient Boosting Classifier Classification Report:\")\n",
    "print(classification_report(Y_train_class, Y_train_gbc_pred))\n",
    "print(\"Gradient Boosting Classifier Confusion Matrix:\")\n",
    "print(confusion_matrix(Y_train_class, Y_train_gbc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logistic Regression CV:</th>\n",
       "      <th>Random Forest Classifier CV:</th>\n",
       "      <th>Gradient Boosting Classifier CV:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4954</td>\n",
       "      <td>0.4857</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Logistic Regression CV:  Random Forest Classifier CV:  \\\n",
       "0                   0.4954                        0.4857   \n",
       "\n",
       "   Gradient Boosting Classifier CV:  \n",
       "0                             0.534  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean Cross-Validation Accuracy table\n",
    "cv_summary_table = {\n",
    "    'Logistic Regression CV:' : round(logreg_cv_scores.mean(), 4),\n",
    "    'Random Forest Classifier CV:' : round(rfc_cv_scores.mean(), 4),\n",
    "    'Gradient Boosting Classifier CV:' : round(gbc_cv_scores.mean(), 4)\n",
    "}\n",
    "\n",
    "# DataFrame\n",
    "cv_summary_df = pd.DataFrame(cv_summary_table, index = [0])\n",
    "cv_summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cross-validation accuracy scores for the three models indicate their performance in predicting customer segments. The Gradient Boosting Classifier achieved the highest cross-validation accuracy score of 0.534, suggesting it has the best predictive power among the three models. The Logistic Regression and Random Forest Classifier had similar but lower accuracy scores of 0.4954 and 0.4857, respectively. This implies that the Gradient Boosting Classifier is the most reliable model for predicting customer segments in this dataset, outperforming the other two models in terms of accuracy. Yet, its accuracy score is too low to say that we are confident the future customer will land in certain segments, only some-what certain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For linear regression\n",
    "\n",
    "feature_names = X_train.columns\n",
    "\n",
    "coefficients = lr_model.coef_\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Coefficient': coefficients\n",
    "})\n",
    "\n",
    "importance_df['Abs_Coefficient'] = np.abs(importance_df['Coefficient'])\n",
    "importance_df = importance_df.sort_values(by='Abs_Coefficient', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(importance_df['Feature'], importance_df['Coefficient'], color='skyblue')\n",
    "plt.xlabel('Coefficient Value')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importance in Linear Regression')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For Decision Tree Regressor\n",
    "\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importances = dt_model.feature_importances_\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': importances\n",
    "})\n",
    "\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance Value')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importance in Decision Tree Regressor')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Random Forest Regressor\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': importances\n",
    "})\n",
    "\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance Value')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importance in Random Forest Regressor')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Gradient Boosting Regressor\n",
    "\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importances = gbm_model.feature_importances_\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': importances\n",
    "})\n",
    "\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance Value')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importance in Gradient Boosting Regressor')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Successfully completed at June 3rd, 2024.\n",
    "### Writers: Nathaniel Zhu, Youyi Fu"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}