CE802_P3_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 a) : Investigate the performance of a number of machine learning procedures on this dataset.\n",
    "    \n",
    "### Introduction\n",
    "\n",
    "The objective of this notebook is to find the amount of insurance claimed by a particular individial. We will use Regression Techniques to get the intended results.\n",
    "\n",
    "### Observations\n",
    "\n",
    "We can see that the data contains both Categorical and Numeric features. The given data doesn't contain any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F4</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>F12</th>\n",
       "      <th>F13</th>\n",
       "      <th>F14</th>\n",
       "      <th>F15</th>\n",
       "      <th>F16</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.56</td>\n",
       "      <td>12.42</td>\n",
       "      <td>-236.06</td>\n",
       "      <td>Rest</td>\n",
       "      <td>-98.88</td>\n",
       "      <td>529.56</td>\n",
       "      <td>4.54</td>\n",
       "      <td>379.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.30</td>\n",
       "      <td>High</td>\n",
       "      <td>-15085.87</td>\n",
       "      <td>-12.93</td>\n",
       "      <td>-39.42</td>\n",
       "      <td>1734.58</td>\n",
       "      <td>3616.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.72</td>\n",
       "      <td>12.46</td>\n",
       "      <td>-190.06</td>\n",
       "      <td>Rest</td>\n",
       "      <td>-59.22</td>\n",
       "      <td>493.11</td>\n",
       "      <td>0.05</td>\n",
       "      <td>402.78</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>Very low</td>\n",
       "      <td>-15782.44</td>\n",
       "      <td>-8.55</td>\n",
       "      <td>-35.61</td>\n",
       "      <td>1672.70</td>\n",
       "      <td>3342.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.34</td>\n",
       "      <td>2.74</td>\n",
       "      <td>-201.20</td>\n",
       "      <td>UK</td>\n",
       "      <td>-228.48</td>\n",
       "      <td>563.79</td>\n",
       "      <td>1.22</td>\n",
       "      <td>147.35</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.28</td>\n",
       "      <td>Low</td>\n",
       "      <td>-10526.01</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-29.10</td>\n",
       "      <td>1462.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.76</td>\n",
       "      <td>2.58</td>\n",
       "      <td>-282.26</td>\n",
       "      <td>UK</td>\n",
       "      <td>-173.28</td>\n",
       "      <td>536.94</td>\n",
       "      <td>0.25</td>\n",
       "      <td>113.49</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.26</td>\n",
       "      <td>Low</td>\n",
       "      <td>-8327.14</td>\n",
       "      <td>-19.23</td>\n",
       "      <td>-34.59</td>\n",
       "      <td>809.46</td>\n",
       "      <td>1742.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.10</td>\n",
       "      <td>9.82</td>\n",
       "      <td>-242.86</td>\n",
       "      <td>USA</td>\n",
       "      <td>-193.14</td>\n",
       "      <td>617.52</td>\n",
       "      <td>9.15</td>\n",
       "      <td>343.64</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.88</td>\n",
       "      <td>Very low</td>\n",
       "      <td>-14434.13</td>\n",
       "      <td>-9.45</td>\n",
       "      <td>-46.14</td>\n",
       "      <td>1435.90</td>\n",
       "      <td>373.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      F1     F2      F3    F4      F5      F6    F7      F8  F9  F10   F11  \\\n",
       "0  16.56  12.42 -236.06  Rest  -98.88  529.56  4.54  379.54   1    1  7.30   \n",
       "1  11.72  12.46 -190.06  Rest  -59.22  493.11  0.05  402.78   5    3 -1.28   \n",
       "2   4.34   2.74 -201.20    UK -228.48  563.79  1.22  147.35   4    4  8.28   \n",
       "3  12.76   2.58 -282.26    UK -173.28  536.94  0.25  113.49   4    3  6.26   \n",
       "4  11.10   9.82 -242.86   USA -193.14  617.52  9.15  343.64   8    6 -6.88   \n",
       "\n",
       "        F12       F13    F14    F15      F16   Target  \n",
       "0      High -15085.87 -12.93 -39.42  1734.58  3616.82  \n",
       "1  Very low -15782.44  -8.55 -35.61  1672.70  3342.88  \n",
       "2       Low -10526.01  -9.66 -29.10  1462.86     0.00  \n",
       "3       Low  -8327.14 -19.23 -34.59   809.46  1742.65  \n",
       "4  Very low -14434.13  -9.45 -46.14  1435.90   373.56  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making data frame from csv file\n",
    "data = pd.read_csv(\"D:\\\\Machine Learning\\CE802_P3_Data\\CE802_P3_Data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1500 entries, 0 to 1499\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   F1      1500 non-null   float64\n",
      " 1   F2      1500 non-null   float64\n",
      " 2   F3      1500 non-null   float64\n",
      " 3   F4      1500 non-null   object \n",
      " 4   F5      1500 non-null   float64\n",
      " 5   F6      1500 non-null   float64\n",
      " 6   F7      1500 non-null   float64\n",
      " 7   F8      1500 non-null   float64\n",
      " 8   F9      1500 non-null   int64  \n",
      " 9   F10     1500 non-null   int64  \n",
      " 10  F11     1500 non-null   float64\n",
      " 11  F12     1500 non-null   object \n",
      " 12  F13     1500 non-null   float64\n",
      " 13  F14     1500 non-null   float64\n",
      " 14  F15     1500 non-null   float64\n",
      " 15  F16     1500 non-null   float64\n",
      " 16  Target  1500 non-null   float64\n",
      "dtypes: float64(13), int64(2), object(2)\n",
      "memory usage: 199.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rest      407\n",
       "UK        380\n",
       "USA       360\n",
       "Europe    353\n",
       "Name: F4, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 4 distinct categories.\n",
    "data['F4'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Very low     312\n",
       "Low          310\n",
       "High         301\n",
       "Very high    290\n",
       "Medium       287\n",
       "Name: F12, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 5 distinct values.\n",
    "data['F12'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the get_dummies function to transform the categorical values to numeric.\n",
    "\n",
    "data=pd.get_dummies(data, columns=[\"F4\", \"F12\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['TARGET']=data['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>F5</th>\n",
       "      <th>F6</th>\n",
       "      <th>F7</th>\n",
       "      <th>F8</th>\n",
       "      <th>F9</th>\n",
       "      <th>F10</th>\n",
       "      <th>F11</th>\n",
       "      <th>...</th>\n",
       "      <th>F15</th>\n",
       "      <th>F16</th>\n",
       "      <th>F4_Rest</th>\n",
       "      <th>F4_UK</th>\n",
       "      <th>F4_USA</th>\n",
       "      <th>F12_Low</th>\n",
       "      <th>F12_Medium</th>\n",
       "      <th>F12_Very high</th>\n",
       "      <th>F12_Very low</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.56</td>\n",
       "      <td>12.42</td>\n",
       "      <td>-236.06</td>\n",
       "      <td>-98.88</td>\n",
       "      <td>529.56</td>\n",
       "      <td>4.54</td>\n",
       "      <td>379.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.30</td>\n",
       "      <td>...</td>\n",
       "      <td>-39.42</td>\n",
       "      <td>1734.58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3616.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.72</td>\n",
       "      <td>12.46</td>\n",
       "      <td>-190.06</td>\n",
       "      <td>-59.22</td>\n",
       "      <td>493.11</td>\n",
       "      <td>0.05</td>\n",
       "      <td>402.78</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>...</td>\n",
       "      <td>-35.61</td>\n",
       "      <td>1672.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3342.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.34</td>\n",
       "      <td>2.74</td>\n",
       "      <td>-201.20</td>\n",
       "      <td>-228.48</td>\n",
       "      <td>563.79</td>\n",
       "      <td>1.22</td>\n",
       "      <td>147.35</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.28</td>\n",
       "      <td>...</td>\n",
       "      <td>-29.10</td>\n",
       "      <td>1462.86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.76</td>\n",
       "      <td>2.58</td>\n",
       "      <td>-282.26</td>\n",
       "      <td>-173.28</td>\n",
       "      <td>536.94</td>\n",
       "      <td>0.25</td>\n",
       "      <td>113.49</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.26</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.59</td>\n",
       "      <td>809.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1742.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.10</td>\n",
       "      <td>9.82</td>\n",
       "      <td>-242.86</td>\n",
       "      <td>-193.14</td>\n",
       "      <td>617.52</td>\n",
       "      <td>9.15</td>\n",
       "      <td>343.64</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.88</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.14</td>\n",
       "      <td>1435.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>373.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      F1     F2      F3      F5      F6    F7      F8  F9  F10   F11  ...  \\\n",
       "0  16.56  12.42 -236.06  -98.88  529.56  4.54  379.54   1    1  7.30  ...   \n",
       "1  11.72  12.46 -190.06  -59.22  493.11  0.05  402.78   5    3 -1.28  ...   \n",
       "2   4.34   2.74 -201.20 -228.48  563.79  1.22  147.35   4    4  8.28  ...   \n",
       "3  12.76   2.58 -282.26 -173.28  536.94  0.25  113.49   4    3  6.26  ...   \n",
       "4  11.10   9.82 -242.86 -193.14  617.52  9.15  343.64   8    6 -6.88  ...   \n",
       "\n",
       "     F15      F16  F4_Rest  F4_UK  F4_USA  F12_Low  F12_Medium  F12_Very high  \\\n",
       "0 -39.42  1734.58        1      0       0        0           0              0   \n",
       "1 -35.61  1672.70        1      0       0        0           0              0   \n",
       "2 -29.10  1462.86        0      1       0        1           0              0   \n",
       "3 -34.59   809.46        0      1       0        1           0              0   \n",
       "4 -46.14  1435.90        0      0       1        0           0              0   \n",
       "\n",
       "   F12_Very low   TARGET  \n",
       "0             0  3616.82  \n",
       "1             1  3342.88  \n",
       "2             0     0.00  \n",
       "3             0  1742.65  \n",
       "4             1   373.56  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create X and Y Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.drop(['TARGET'],axis='columns')\n",
    "y=data['TARGET']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train,y_test = train_test_split(x, y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_train=StandardScaler(with_mean=False).fit_transform(X_train)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=StandardScaler(with_mean=False).fit_transform(X_test)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Dimention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1200, 21), (1200,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300, 21), (300,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Instantiate Linear Model Regresser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Linear model to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
      "         normalize=False)\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_train,y_train)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.794089868408217"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The coefficient of R-Square\n",
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the performance of Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [ 365.21917218  379.63339477   -9.97560189  433.78525045   -5.33897195\n",
      "    5.11937939 -301.04004907  231.33009079 -240.30470326   10.42838007\n",
      "  -13.96622022  -15.23265128   -0.54807772 -258.6889933   249.59482974\n",
      "   72.84002241 -151.61681293 -129.07394936  -66.28226809   66.16697111\n",
      " -177.06567141]\n",
      "Intercept: 1736.6527021684274\n",
      "Mean Absolute Error (MAE) : 409.61\n",
      "Mean Squared Error (MSE) : 270126.09\n",
      "Coefficient of determination : 0.78\n",
      "Root Mean Squared Error: 519.7365558701076\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficients:\", model.coef_)\n",
    "print(\"Intercept:\",model.intercept_)\n",
    "print('Mean Absolute Error (MAE) : %.2f'% mean_absolute_error(y_test,y_pred))\n",
    "print('Mean Squared Error (MSE) : %.2f'%  mean_squared_error(y_test,y_pred))\n",
    "\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination : %.2f'%  r2_score(y_test,y_pred))\n",
    "\n",
    "print(\"Root Mean Squared Error:\", np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "471     1753.12\n",
       "9       1088.68\n",
       "1499       0.00\n",
       "54       456.27\n",
       "1411     599.04\n",
       "         ...   \n",
       "220       94.21\n",
       "946        0.00\n",
       "992      789.66\n",
       "1496    1528.48\n",
       "443     1230.70\n",
       "Name: TARGET, Length: 300, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1491.33845227,  1603.99055361,  -194.3795622 ,   653.15340717,\n",
       "        1267.11256251,  -938.91407561,   739.88886098, -1799.2179285 ,\n",
       "        1702.41256441,   -61.79158689,   766.69975096,   168.19195528,\n",
       "        1136.98879651,  1742.25347654,  2801.23116914,   978.59747836,\n",
       "         298.56411243,   403.47858899,   -55.97047371,  -124.71849813,\n",
       "        1002.3067824 ,  2873.94613199,  1149.94568614,  -232.88709314,\n",
       "        1384.58311758,  1158.98933818,  1375.91567001,   552.37929544,\n",
       "         508.68056829,  1100.12072659,  1023.29218989,   115.21899999,\n",
       "        2471.18976625,  1124.72686115,   -32.61674049,   593.46516726,\n",
       "        1251.13124735,  3230.00022198,   258.99093514, -1341.98415334,\n",
       "        1700.41932333,  1772.29038555,   734.5902719 ,  1846.54821892,\n",
       "       -1107.23991275,  1962.17165485, -1007.85695809,  -149.34189196,\n",
       "        1200.60489286,  1744.82333661,  1081.34578521,   517.96666696,\n",
       "        1585.12181767,    38.88887507,  1787.32626065,   693.06357448,\n",
       "        2435.03879604,  1526.69134714,   477.22913846,     6.27574441,\n",
       "         962.20130535,  2817.60095259,  2477.43851511,   775.66897406,\n",
       "         817.94502372,  1233.65618377,  1158.9589087 ,  -717.67360959,\n",
       "         206.63192456,  4287.52014527, -1043.99922046,   843.7961679 ,\n",
       "         798.13309328,   136.34824509,  2585.51336602,  1371.06898376,\n",
       "        1039.92503716,   860.08963195,  -419.10776365,  1252.1272001 ,\n",
       "        1998.53034539,  -167.90257785,   343.33611032,   709.41568137,\n",
       "        1556.68654046,  -301.97603234,    27.32229089,  -190.21976424,\n",
       "         415.72476475,  1637.41807622,  1140.20674252,  1451.89948166,\n",
       "        -733.33505079,  2033.46521364,   422.7831511 ,  1811.1109077 ,\n",
       "        2011.68307918,  1944.63579261,  1635.17891418,   628.63607737,\n",
       "         474.78472629,   563.83831233,   930.5570738 ,  -143.63669706,\n",
       "        1675.14474595,  2707.86955347,   491.81533818, -1191.72358374,\n",
       "         181.33417344,   652.97921917,  1524.28522934,   688.00487103,\n",
       "        1129.72350819,  -403.42873931,   148.04223147,   393.62950538,\n",
       "        1074.09384661,  2640.37583353,  1853.94349112,  1548.31088971,\n",
       "         345.15675039,  1882.3863864 ,   133.81131235,   947.72868963,\n",
       "         238.01972384,  1352.1830158 ,  1679.21992133,   666.35931654,\n",
       "         434.7792484 ,  -307.48231525,   364.26078826,   735.94397519,\n",
       "        2560.65493845,  1954.34223677,  2086.34175471,  1388.1040671 ,\n",
       "         120.04170845,  -323.89950427,  1392.78692547,   944.0579002 ,\n",
       "        2219.78672443,   659.24997562,   544.99873732,  -727.90893331,\n",
       "         769.42145101,  2110.0345482 ,  1848.26218345,   944.7228664 ,\n",
       "        1180.76254797,   399.39248659,  -106.74667398,  -156.09815481,\n",
       "         552.46228174,  1535.44270096,   430.02079424,   143.04222009,\n",
       "         533.22935337,   948.45602692,  1601.44354682,  1737.95739041,\n",
       "        1435.86125755,  -532.47357886,  2851.99734722,  1098.02456832,\n",
       "         552.39598613,  3535.20944923,   333.79947484,   832.48286173,\n",
       "        1947.07932769,  1281.02601075,   643.16733802,   414.18653059,\n",
       "         745.21548037,  2381.96438462,   282.25851528,  1693.441428  ,\n",
       "        1072.88570985,   861.88159328,  1366.20739592,  1132.61553013,\n",
       "        1892.88119482,   973.83794227,  1249.09363909,   215.37999663,\n",
       "        1689.31616941,   300.47027642,   -44.2452828 ,   600.32404514,\n",
       "        2090.71772711,  1599.47285877,  1162.17160872,  1566.7278175 ,\n",
       "           6.06526032,   946.23299868,  2599.06844783,   694.09045121,\n",
       "         956.24538238,  1617.13163404,  1484.94281697,  2911.90504507,\n",
       "         361.66327661,  2971.54931076,    -8.90027982,  1621.89481193,\n",
       "        2212.64045326,    62.68116017,   715.51353303,  1828.93256542,\n",
       "        1750.4850654 ,   -97.20387136,  1271.86960844,   512.25002896,\n",
       "        2322.30708439,  1305.9928055 ,  1340.61080228,   632.77488632,\n",
       "        1014.365624  ,  1775.15301573,   410.17295041,  1168.6643362 ,\n",
       "        -230.88598241,   332.53057151,   806.91860864,   554.15799191,\n",
       "         552.3055425 ,   367.73468135,  -499.31833572,  1685.37384555,\n",
       "        1013.00747751,  -460.69481091,  2304.44012982,   554.74179679,\n",
       "        2850.61520402,   854.18386692,  -596.70295486,   516.51407748,\n",
       "         513.87593576,  -631.82537904,   837.78775912,  2733.29433717,\n",
       "         844.42022814,   969.98418605,  1797.04474162,   123.28909272,\n",
       "         300.99466237,  1944.87256362,  -262.17948236,  1035.52935755,\n",
       "         551.75852919,   552.6409161 , -1255.83520266,  3144.47834017,\n",
       "        2679.69165863,  2451.58780341,   717.41784575,  -374.23069914,\n",
       "       -1300.64208671, -1451.82334175,  1143.63000011,   855.78674117,\n",
       "        1405.42513625,   583.559542  ,  2284.23296134,   637.1541254 ,\n",
       "         840.5770143 ,    69.13770409,   932.70710926,   998.69177759,\n",
       "         334.07151462,   429.95190017,  1660.180519  ,  2174.87884424,\n",
       "        1988.80577075,  1920.28403329,   498.22947637,  1241.78751458,\n",
       "        -266.41578402,   909.36115012,  2353.51074878,   878.29697188,\n",
       "        1040.42563752,  -868.31246149,  2672.73410734, -1369.48171326,\n",
       "        -687.92901622,  1885.78877496,   773.27108798,   318.041431  ,\n",
       "        2366.43392951,   941.9106547 ,   967.37680265,   507.04776375,\n",
       "        3188.78303162,  1524.63302189,  2065.97797588,    80.64064367,\n",
       "         720.55392739,  1294.13281623,  1301.90060556,  1376.52714849])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha=0.5)    \n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Instantiate Decision Tree Regresser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtr = DecisionTreeRegressor(splitter='best',min_samples_split=28,max_depth=6,criterion='friedman_mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Decision Tree to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='friedman_mse', max_depth=6,\n",
       "           max_features=None, max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=28,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6700264276881002"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The R^2 Score\n",
    "dtr.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dtr = dtr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import Regression Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate the performance of Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 581.9255631321874\n",
      "Mean Squared Error (MSE): 642478.8460383507\n",
      "Coefficient of determination (R^2): 0.4697365332340846\n",
      "Root Mean Squared Error: 801.5477815067238\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE):\",mean_absolute_error(y_test, y_pred_dtr))\n",
    "print(\"Mean Squared Error (MSE):\", mean_squared_error(y_test, y_pred_dtr))\n",
    "print(\"Coefficient of determination (R^2):\", r2_score(y_test, y_pred_dtr))\n",
    "print(\"Root Mean Squared Error:\", np.sqrt(mean_squared_error(y_test, y_pred_dtr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred_dtr) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Instantiate Random Forest Regresser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=40, min_samples_split= 10, max_features= 'auto', max_depth= 5, criterion= 'mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit the Random Forest to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=10,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7359764266960418"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Do prediction using our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_r = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import Regression Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Calculate the performance of Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 546.71\n",
      "Mean Squared Error (MSE): 491179.15\n",
      "Coefficient of determination (R^2): 0.59\n",
      "Root Mean Squared Error: 700.8417458372437\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error (MAE): %.2f'% mean_absolute_error(y_test,y_pred_r))\n",
    "print('Mean Squared Error (MSE): %.2f'% mean_squared_error(y_test,y_pred_r))\n",
    "print('Coefficient of determination (R^2): %.2f'% r2_score(y_test,y_pred_r))\n",
    "print(\"Root Mean Squared Error:\", np.sqrt(mean_squared_error(y_test, y_pred_r)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred_r) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-------------------------+----------+--------+\n",
      "|    | Algorithm               |      R^2 |    MSE |\n",
      "|----+-------------------------+----------+--------|\n",
      "|  0 | Linear Regression Model | 0.79409  | 270126 |\n",
      "|  1 | Decision Tree Regressor | 0.670026 | 642479 |\n",
      "|  2 | Random Forest Regressor | 0.735976 | 491179 |\n",
      "+----+-------------------------+----------+--------+\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate \n",
    "\n",
    "values = {'Algorithm':['Linear Regression Model', 'Decision Tree Regressor', 'Random Forest Regressor'], \n",
    "        'R^2':['0.794089868408217','0.6700264276881002' ,'0.7359764266960418'], \n",
    "        'MSE':['270126.09', '642478.8460383507', '491179.15'],\n",
    "       } \n",
    "\n",
    "df = pd.DataFrame(values) \n",
    "  \n",
    "# displaying the DataFrame \n",
    "print(tabulate(df, headers = 'keys', tablefmt = 'psql'))"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}