happiness-index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #The World Happiness Report is a landmark survey of the state of global happiness. The scores are based on answers to the main life evaluation question asked in the poll.The happiness score  is estimated on six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity . Based on the records we need to bultd the Best Regression Model to predict the  \"Happiness score\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages, Loading   data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Necessary Libararires \n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Extracting the data from the csv file into a dataframe. Post loading Checking for vital statisitcs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  \n",
       "3     0.34699            2.46531  \n",
       "4     0.45811            2.45176  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#readinng data from .CSV file\n",
    "df=pd.read_csv('happiness_score_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  DATA Quality  information  on the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of dataset \n",
      "  (158, 12) \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Standard Error                 158 non-null    float64\n",
      " 5   Economy (GDP per Capita)       158 non-null    float64\n",
      " 6   Family                         158 non-null    float64\n",
      " 7   Health (Life Expectancy)       158 non-null    float64\n",
      " 8   Freedom                        158 non-null    float64\n",
      " 9   Trust (Government Corruption)  158 non-null    float64\n",
      " 10  Generosity                     158 non-null    float64\n",
      " 11  Dystopia Residual              158 non-null    float64\n",
      "dtypes: float64(9), int64(1), object(2)\n",
      "memory usage: 14.9+ KB\n",
      "Details dataset\n",
      " None \n",
      "\n",
      "Checking for NULL/Missing values \n",
      " Country                          0\n",
      "Region                           0\n",
      "Happiness Rank                   0\n",
      "Happiness Score                  0\n",
      "Standard Error                   0\n",
      "Economy (GDP per Capita)         0\n",
      "Family                           0\n",
      "Health (Life Expectancy)         0\n",
      "Freedom                          0\n",
      "Trust (Government Corruption)    0\n",
      "Generosity                       0\n",
      "Dystopia Residual                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# more infor on the dataset \n",
    "print('Size of dataset \\n ',df.shape,'\\n')\n",
    "print('Details dataset\\n' ,df.info(),'\\n')\n",
    "print('Checking for NULL/Missing values \\n',df.isna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features of the data set \n",
    "### Its has 158 rows and 12 columns  \n",
    "### Its has null or missing values \n",
    "### Its a Regression type problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA MANIPULATON for Catgorical values in Column REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Manipulation \n",
    "lab_enc =LabelEncoder()\n",
    "#Each and Every data in the features are categorical . \n",
    "# Categorical data will not have any outliers \n",
    "# so All the Care\n",
    "df['Region']  = lab_enc.fit_transform(df['Region'])\n",
    " # Data cinvertion completed   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA Visualization \n",
    "#### Removing  Country and Happiness rank as we are intersted to get Happiness score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Region</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.120253</td>\n",
       "      <td>5.375734</td>\n",
       "      <td>0.047885</td>\n",
       "      <td>0.846137</td>\n",
       "      <td>0.991046</td>\n",
       "      <td>0.630259</td>\n",
       "      <td>0.428615</td>\n",
       "      <td>0.143422</td>\n",
       "      <td>0.237296</td>\n",
       "      <td>2.098977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.993321</td>\n",
       "      <td>1.145010</td>\n",
       "      <td>0.017146</td>\n",
       "      <td>0.403121</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0.247078</td>\n",
       "      <td>0.150693</td>\n",
       "      <td>0.120034</td>\n",
       "      <td>0.126685</td>\n",
       "      <td>0.553550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.839000</td>\n",
       "      <td>0.018480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.328580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.526000</td>\n",
       "      <td>0.037268</td>\n",
       "      <td>0.545808</td>\n",
       "      <td>0.856823</td>\n",
       "      <td>0.439185</td>\n",
       "      <td>0.328330</td>\n",
       "      <td>0.061675</td>\n",
       "      <td>0.150553</td>\n",
       "      <td>1.759410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.232500</td>\n",
       "      <td>0.043940</td>\n",
       "      <td>0.910245</td>\n",
       "      <td>1.029510</td>\n",
       "      <td>0.696705</td>\n",
       "      <td>0.435515</td>\n",
       "      <td>0.107220</td>\n",
       "      <td>0.216130</td>\n",
       "      <td>2.095415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.243750</td>\n",
       "      <td>0.052300</td>\n",
       "      <td>1.158448</td>\n",
       "      <td>1.214405</td>\n",
       "      <td>0.811013</td>\n",
       "      <td>0.549092</td>\n",
       "      <td>0.180255</td>\n",
       "      <td>0.309883</td>\n",
       "      <td>2.462415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.587000</td>\n",
       "      <td>0.136930</td>\n",
       "      <td>1.690420</td>\n",
       "      <td>1.402230</td>\n",
       "      <td>1.025250</td>\n",
       "      <td>0.669730</td>\n",
       "      <td>0.551910</td>\n",
       "      <td>0.795880</td>\n",
       "      <td>3.602140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Region  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "count  158.000000       158.000000      158.000000                158.000000   \n",
       "mean     5.120253         5.375734        0.047885                  0.846137   \n",
       "std      2.993321         1.145010        0.017146                  0.403121   \n",
       "min      0.000000         2.839000        0.018480                  0.000000   \n",
       "25%      3.000000         4.526000        0.037268                  0.545808   \n",
       "50%      4.500000         5.232500        0.043940                  0.910245   \n",
       "75%      8.000000         6.243750        0.052300                  1.158448   \n",
       "max      9.000000         7.587000        0.136930                  1.690420   \n",
       "\n",
       "           Family  Health (Life Expectancy)     Freedom  \\\n",
       "count  158.000000                158.000000  158.000000   \n",
       "mean     0.991046                  0.630259    0.428615   \n",
       "std      0.272369                  0.247078    0.150693   \n",
       "min      0.000000                  0.000000    0.000000   \n",
       "25%      0.856823                  0.439185    0.328330   \n",
       "50%      1.029510                  0.696705    0.435515   \n",
       "75%      1.214405                  0.811013    0.549092   \n",
       "max      1.402230                  1.025250    0.669730   \n",
       "\n",
       "       Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
       "count                     158.000000  158.000000         158.000000  \n",
       "mean                        0.143422    0.237296           2.098977  \n",
       "std                         0.120034    0.126685           0.553550  \n",
       "min                         0.000000    0.000000           0.328580  \n",
       "25%                         0.061675    0.150553           1.759410  \n",
       "50%                         0.107220    0.216130           2.095415  \n",
       "75%                         0.180255    0.309883           2.462415  \n",
       "max                         0.551910    0.795880           3.602140  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_v= df.drop(['Country','Happiness Rank',],axis =1)\n",
    "df_v.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Checking correlarion and plotting heat map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_v' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-8dcc9edc2ada>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Data is shewed in some segments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#Checking correlarion and heat map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf_v\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCorr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_v\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_v\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCorr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_v' is not defined"
     ]
    }
   ],
   "source": [
    "# Data is shewed in some segments \n",
    "#Checking correlarion and heat map \n",
    "df_v.Corr = df_v.Corr()\n",
    "plt.figure(figsize=[18,20])\n",
    "sns.heatmap(df_v.Corr,cannot=True)\n",
    "plt.title('Corr- Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying to sort the values as per the Heat map correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Happiness Score                  1.000000\n",
       "Economy (GDP per Capita)         0.780966\n",
       "Family                           0.740605\n",
       "Health (Life Expectancy)         0.724200\n",
       "Freedom                          0.568211\n",
       "Dystopia Residual                0.530474\n",
       "Trust (Government Corruption)    0.395199\n",
       "Generosity                       0.180319\n",
       "Region                          -0.176791\n",
       "Standard Error                  -0.177254\n",
       "Name: Happiness Score, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_v_corr['Happiness Score'].sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As ,per the values we can easily observe that , GDP, FAMILY.HEALTH,Freedom pays most imp role in happiness score\n",
    "\n",
    "###  Plotting this features in shared plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Freedom', ylabel='Happiness Score'>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots( 1,4, sharey = True)\n",
    "df_v.plot(kind ='scatter',x='Economy (GDP per Capita)',y ='Happiness Score' ,ax= axs[0],figsize =(16,8))\n",
    "df_v.plot(kind ='scatter',x='Family',y ='Happiness Score' ,ax= axs[1])\n",
    "df_v.plot(kind ='scatter',x='Health (Life Expectancy)',y ='Happiness Score' ,ax= axs[2])\n",
    "df_v.plot(kind ='scatter',x='Freedom',y ='Happiness Score' ,ax= axs[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_v= df.drop(['Country','Happiness Rank',],axis =1)\n",
    "#Visualizartion\n",
    "plt.figure(figsize =(20,25))\n",
    "plt_num =4\n",
    "for i in df_v:\n",
    "    if plt_num<=12 :\n",
    "        ax  =plt.subplot(4,3,plt_num)\n",
    "        sns.distplot(df_v[i])\n",
    "        plt.xlabel(i, fontsize =20)\n",
    "    plt_num +=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Scale the data using standard scaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data using standard scaler \n",
    "# Setting X and y axis and splitting the data into train and test respectively.\n",
    "x = df_v.drop(['Region','Happiness Score'],axis =1) \n",
    "y = df_v['Happiness Score']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Standard Error                   1.983439\n",
       "Economy (GDP per Capita)        -0.317575\n",
       "Family                          -1.006893\n",
       "Health (Life Expectancy)        -0.705328\n",
       "Freedom                         -0.413462\n",
       "Trust (Government Corruption)    1.385463\n",
       "Generosity                       1.001961\n",
       "Dystopia Residual               -0.238911\n",
       "dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#skewness check\n",
    "x.skew(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Multicollinearity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.138096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.106075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.944623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.336063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.749657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.440034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.289276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.056090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF\n",
       "0  1.138096\n",
       "1  4.106075\n",
       "2  1.944623\n",
       "3  3.336063\n",
       "4  1.749657\n",
       "5  1.440034\n",
       "6  1.289276\n",
       "7  1.056090"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "VIF = pd.DataFrame()\n",
    "VIF['VIF'] =[variance_inflation_factor (x_scaled,i) for i in range (x_scaled.shape[1])]\n",
    "VIF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The is no Multi-collinearity  in  the data as novalue are above 5 \n",
    "### Finding Best Randome state  and Splitting data into test and train on 75:25 principle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "my_acc = 0\n",
    "rs = 0\n",
    "for i in range (1,200) :\n",
    "    x_train,x_test,y_train,y_test = train_test_split(x_scaled,y,test_size=.15,random_state=i)   \n",
    "    lr = LinearRegression()\n",
    "    lr.fit(x_train,y_train)\n",
    "    y_pred =lr.predict(x_test)\n",
    "    acc= r2_score(y_test,y_pred)\n",
    "    if acc>my_acc :\n",
    "        my_acc =acc\n",
    "        rs=i  \n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Thus SPlitting data \n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_scaled,y,test_size=0.25,random_state=54)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling with Different Regression Techiniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_SCORE: 0.9999999507491493\n",
      "Mean Absolute Error_LR: 0.00020492016952041356\n",
      "Mean Squared Error_LR: 6.111732876106153e-08\n",
      "Root Mean Squared Error_LR: 0.00024721919173288615\n"
     ]
    }
   ],
   "source": [
    "#Using  Regression models \n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "lr_y_pred = lr.predict(x_test)\n",
    "R2_lr_score =r2_score(y_test,lr_y_pred)\n",
    "\n",
    "print('R2_SCORE:',R2_lr_score)\n",
    "print('Mean Absolute Error_LR:',mean_absolute_error(y_test,lr_y_pred))\n",
    "print('Mean Squared Error_LR:', mean_squared_error(y_test,lr_y_pred))\n",
    "print('Root Mean Squared Error_LR:', np.sqrt(mean_squared_error(y_test,lr_y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2_SCORE_LR: 0.9999999565851334\n",
    "### Mean Absolute Error_LR: 0.00022425002245555215\n",
    "### Mean Squared Error_LR: 7.201008141468724e-08\n",
    "### Root Mean Squared Error_LR: 0.0002683469422495573"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X-Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_SCORE: 0.893068467629666\n",
      "Mean Absolute Error_LR: 0.3298359360694885\n",
      "Mean Squared Error_LR: 0.17736201824551417\n",
      "Root Mean Squared Error_LR: 0.42114370260697737\n"
     ]
    }
   ],
   "source": [
    "#Using  XGB models \n",
    "import xgboost as  xgb\n",
    "xgb = xgb.XGBRegressor()\n",
    "xgb.fit(x_train,y_train)\n",
    "xgb_y_pred = xgb.predict(x_test)\n",
    "R2_xgb_score =r2_score(y_test,xgb_y_pred)\n",
    "R2_xgb_score\n",
    "print('R2_SCORE:',R2_xgb_score)\n",
    "print('Mean Absolute Error_XGB:',mean_absolute_error(y_test,xgb_y_pred))\n",
    "print('Mean Squared Error_XGB:', mean_squared_error(y_test,xgb_y_pred))\n",
    "print('Root Mean Squared Error_XGB:', np.sqrt(mean_squared_error(y_test,xgb_y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2_SCORE: 0.893068467629666\n",
    "### Mean Absolute Error_XGB: 0.3298359360694885\n",
    "### Mean Squared Error_XGB: 0.17736201824551417\n",
    "### Root Mean Squared Error_XGB: 0.42114370260697737"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Decision Tree  Regressior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_SCORE_DT: 0.8062912357113492\n",
      "Mean Absolute Error_DT: 0.4863999999999999\n",
      "Mean Squared Error_DT: 0.32129509999999983\n",
      "Root Mean Squared Error_DT: 0.5668289865559099\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor()\n",
    "dt.fit(x_train,y_train)\n",
    "y_pred_dt =dt.predict(x_test)\n",
    "R2_DT_score =r2_score(y_test,y_pred_dt)\n",
    "print('R2_SCORE_DT:',R2_DT_score)\n",
    "print('Mean Absolute Error_DT:',mean_absolute_error(y_test,y_pred_dt))\n",
    "print('Mean Squared Error_DT:', mean_squared_error(y_test,y_pred_dt))\n",
    "print('Root Mean Squared Error_DT:', np.sqrt(mean_squared_error(y_test,y_pred_dt)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2_SCORE_DT: 0.8062912357113492\n",
    "### Mean Absolute Error_DT: 0.4863999999999999\n",
    "### Mean Squared Error_DT: 0.32129509999999983\n",
    "### Root Mean Squared Error_DT: 0.5668289865559099"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector  Regressior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_SCORE: 0.8535346701575224\n",
      "Mean Absolute Error_LR: 0.2947743366686372\n",
      "Mean Squared Error_LR: 0.24293476328282454\n",
      "Root Mean Squared Error_LR: 0.49288412764342954\n"
     ]
    }
   ],
   "source": [
    "# USING SVM Regression\n",
    "from sklearn.svm import SVR\n",
    "svr = SVR()\n",
    "svr.fit(x_train, y_train)\n",
    "svr_y_pred = svr.predict(x_test)\n",
    "R2_svr_score =r2_score(y_test,svr_y_pred)\n",
    "R2_svr_score\n",
    "print('R2_SCORE:',R2_svr_score)\n",
    "print('Mean Absolute Error_LR:',mean_absolute_error(y_test,svr_y_pred))\n",
    "print('Mean Squared Error_LR:', mean_squared_error(y_test,svr_y_pred))\n",
    "print('Root Mean Squared Error_LR:', np.sqrt(mean_squared_error(y_test,svr_y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R2_SCORE: 0.8535346701575224\n",
    "### Mean Absolute Error_LR: 0.2947743366686372\n",
    "### Mean Squared Error_LR: 0.24293476328282454\n",
    "### Root Mean Squared Error_LR: 0.49288412764342954"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Cross Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " CV score LR 0.9999981871007761\n",
      "\n",
      " CV score XGB -2.9624014458112002\n",
      "\n",
      " CV score SVR 0.028707305604232625\n",
      "\n",
      " CV score DTR -7.631289700203668\n"
     ]
    }
   ],
   "source": [
    "#Cross Validation \n",
    "cvLR =cross_val_score(lr,x,y,cv=5)\n",
    "cvXGB =cross_val_score(xgb,x,y,cv=5)\n",
    "cvSVR=cross_val_score(svr,x,y,cv=5)\n",
    "cvDTR=cross_val_score(dt,x,y,cv=5)\n",
    "print ('\\n CV score LR',cvLR.mean())\n",
    "print ('\\n CV score XGB',cvXGB.mean())\n",
    "print ('\\n CV score SVR',cvSVR.mean())\n",
    "print ('\\n CV score DTR',cvDTR.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SUPPORT VERCTOR MACINE REGRESSOR is the best one \n",
    "## HYPER PARAMETER TUNING TO BE DONE on SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={'C': [1, 1.1, 1.2, 1.21, 1.23, 1.25, 2, 27],\n",
       "                         'coef0': [0.01, 10, 0.5], 'gamma': ('auto', 'scale')})"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param ={'C' :[1,1.1,1.2,1.21,1.23,1.25,2,27] ,'coef0' : [0.01,10,0.5],'gamma' : ('auto','scale')}\n",
    "GSV = GridSearchCV(SVR(),param,cv=5)\n",
    "GSV.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['C', 'cache_size', 'coef0', 'degree', 'epsilon', 'gamma', 'kernel', 'max_iter', 'shrinking', 'tol', 'verbose'])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVR().get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.25, 'coef0': 0.01, 'gamma': 'auto'}"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSV.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling Using best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_SCORE_TUNED: 0.8535346701575224\n",
      "Mean Absolute Error_SVR_TUNED : 0.2755345532897583\n",
      "Mean Squared Error_SVR_TUNED : 0.21287245749013367\n",
      "Root Mean Squared Error_SVR_TUNED : 0.4613810328677737\n"
     ]
    }
   ],
   "source": [
    "#Using better parameters\n",
    "from sklearn.svm import SVR\n",
    "svr_t = SVR(C= 1.25,coef0= 0.01,gamma= 'auto')  #checking \n",
    "svr_t.fit(x_train, y_train)\n",
    "svr_t_y_pred = svr_t.predict(x_test)\n",
    "R2_svr_t_score =r2_score(y_test,svr_t_y_pred)\n",
    "R2_svr_t_score\n",
    "print('R2_SCORE_TUNED:',R2_svr_score)\n",
    "print('Mean Absolute Error_SVR_TUNED :',mean_absolute_error(y_test,svr_t_y_pred))\n",
    "print('Mean Squared Error_SVR_TUNED :', mean_squared_error(y_test,svr_t_y_pred))\n",
    "print('Root Mean Squared Error_SVR_TUNED :', np.sqrt(mean_squared_error(y_test,svr_t_y_pred)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EFFICIANCY ACHIEVED \n",
    "\n",
    "R2_SCORE_TUNED: 0.8535346701575224\n",
    "Mean Absolute Error_SVR_TUNED : 0.2755345532897583\n",
    "Mean Squared Error_SVR_TUNED : 0.21287245749013367\n",
    "Root Mean Squared Error_SVR_TUNED : 0.4613810328677737"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.45446902 6.21107373 4.56239521 6.63415462 4.734931   4.6529242\n",
      " 6.14459861 4.89533926 3.64491688 4.99973494 6.08917874 4.90400262\n",
      " 3.55919275 4.40809594 7.39364674 4.84928828 5.83657595 4.38987712\n",
      " 5.85814372 4.72491155 6.80948209 7.41826733 5.85020032 3.91704142\n",
      " 3.85871806 6.85227967 4.26229314 5.92237836 5.79879124 3.80876145\n",
      " 4.948738   5.64732085 4.99239585 4.99927375 5.99023903 5.36229862\n",
      " 4.68496482 4.78198397 5.72255731 5.63724125]\n",
      "[7.41240977 6.18251742 4.61623688 6.63377947 4.77071705 4.66352662\n",
      " 6.12422921 4.90056163 3.72607836 4.98295826 6.06273746 4.9083198\n",
      " 3.61198471 4.47111852 7.38381332 4.83989594 5.83887193 4.39281959\n",
      " 5.84913141 4.80365086 6.7435094  7.39668543 5.83687548 4.05363871\n",
      " 3.86821031 6.78779727 4.26885122 5.93412087 5.7994043  3.80018211\n",
      " 4.99295255 5.6299656  4.99013064 4.99720177 5.99716408 5.37032315\n",
      " 4.75578849 4.77471751 5.68110704 5.60011486]\n",
      "[5.129 2.839 5.14  4.971 3.681 6.505 7.187 4.867 5.889 5.878 3.896 4.788\n",
      " 6.485 7.527 5.057 4.517 5.102 5.605 4.959 6.853 5.759 3.989 6.411 5.194]\n"
     ]
    }
   ],
   "source": [
    "#Checking efficiancy in random \n",
    "\n",
    "print(svr_t_y_pred[:50],)\n",
    "print(svr_y_pred[:50],)\n",
    "print(y_test[:50].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Happiness_Report.pk']"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dumping the model \n",
    "import joblib as jl\n",
    "jl.dump(svr_t,'Happiness_Report.pk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Model\n",
    "hrpt = jl.load('Happiness_Report.pk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hrpt = hrpt.predict(x_test)\n",
    "df_hrpt = pd.DataFrame({})\n",
    "\n",
    "df_hrpt['Y_hrpt'] = Y_hrpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_hrpt = hrpt.predict(x)\n",
    "df['Calxulated Happiness Score'] = Y_hrpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving in the CSV FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Happiness_Report_Pred.csv')"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}