happiness_report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries required to perform the regression\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Standard Error', 'Economy (GDP per Capita)', 'Family',\n",
       "       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',\n",
       "       'Generosity', 'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the dataset\n",
    "\n",
    "df = pd.read_csv(r\"D:\\world happiness report\\2015.csv\")\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arranging the variables and target\n",
    "\n",
    "X = df.drop(df.iloc[:,:4],axis=1)\n",
    "y = df.iloc[:,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting the data into train and test \n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3, random_state=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the data to the model -> training the model\n",
    "\n",
    "regressor = DecisionTreeRegressor(criterion='squared_error',max_depth=3,random_state=1)\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the values with the model which is trained on training dataset\n",
    "\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score 0.770938\n",
      "Train score 0.850822\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "\n",
    "# testing the model score \n",
    "print(\"Test score %3f\" %regressor.score(X_test, y_test))\n",
    "print(\"Train score %3f\" %regressor.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse 213.976809\n",
      "mae 7.698816\n"
     ]
    }
   ],
   "source": [
    "# metrics used to evaluate the performance of the model\n",
    "\n",
    "mse = mean_squared_error(y_test,y_pred)\n",
    "mae = mean_absolute_error(y_test,y_pred)\n",
    "print(\"mse %3f\" %mse)\n",
    "print(\"mae %3f\" %mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>154</td>\n",
       "      <td>3.465</td>\n",
       "      <td>0.03464</td>\n",
       "      <td>0.22208</td>\n",
       "      <td>0.77370</td>\n",
       "      <td>0.42864</td>\n",
       "      <td>0.59201</td>\n",
       "      <td>0.55191</td>\n",
       "      <td>0.22628</td>\n",
       "      <td>0.67042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.340</td>\n",
       "      <td>0.03656</td>\n",
       "      <td>0.28665</td>\n",
       "      <td>0.35386</td>\n",
       "      <td>0.31910</td>\n",
       "      <td>0.48450</td>\n",
       "      <td>0.08010</td>\n",
       "      <td>0.18260</td>\n",
       "      <td>1.63328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.006</td>\n",
       "      <td>0.05015</td>\n",
       "      <td>0.66320</td>\n",
       "      <td>0.47489</td>\n",
       "      <td>0.72193</td>\n",
       "      <td>0.15684</td>\n",
       "      <td>0.18906</td>\n",
       "      <td>0.47179</td>\n",
       "      <td>0.32858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.08658</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.41587</td>\n",
       "      <td>0.22396</td>\n",
       "      <td>0.11850</td>\n",
       "      <td>0.10062</td>\n",
       "      <td>0.19727</td>\n",
       "      <td>1.83302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>158</td>\n",
       "      <td>2.839</td>\n",
       "      <td>0.06727</td>\n",
       "      <td>0.20868</td>\n",
       "      <td>0.13995</td>\n",
       "      <td>0.28443</td>\n",
       "      <td>0.36453</td>\n",
       "      <td>0.10731</td>\n",
       "      <td>0.16681</td>\n",
       "      <td>1.56726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "153       Rwanda               Sub-Saharan Africa             154   \n",
       "154        Benin               Sub-Saharan Africa             155   \n",
       "155        Syria  Middle East and Northern Africa             156   \n",
       "156      Burundi               Sub-Saharan Africa             157   \n",
       "157         Togo               Sub-Saharan Africa             158   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "153            3.465         0.03464                   0.22208  0.77370   \n",
       "154            3.340         0.03656                   0.28665  0.35386   \n",
       "155            3.006         0.05015                   0.66320  0.47489   \n",
       "156            2.905         0.08658                   0.01530  0.41587   \n",
       "157            2.839         0.06727                   0.20868  0.13995   \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",
       "153                   0.42864  0.59201                        0.55191   \n",
       "154                   0.31910  0.48450                        0.08010   \n",
       "155                   0.72193  0.15684                        0.18906   \n",
       "156                   0.22396  0.11850                        0.10062   \n",
       "157                   0.28443  0.36453                        0.10731   \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  \n",
       "..          ...                ...  \n",
       "153     0.22628            0.67042  \n",
       "154     0.18260            1.63328  \n",
       "155     0.47179            0.32858  \n",
       "156     0.19727            1.83302  \n",
       "157     0.16681            1.56726  \n",
       "\n",
       "[158 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the other dataset to train the model\n",
    "\n",
    "df_2016 = pd.read_csv(r\"D:\\world happiness report\\2016.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting the variables and targets\n",
    "\n",
    "X1 = df.drop(df.iloc[:,:4],axis=1)\n",
    "y1 = df.iloc[:,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train,X1_test,y1_train,y1_test = train_test_split(X1,y1,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score 0.770938\n",
      "Train score 0.850822\n"
     ]
    }
   ],
   "source": [
    "# fitting the splitted data to the existing model\n",
    "\n",
    "regressor.fit(X1_train,y1_train)\n",
    "print(\"Test score %3f\" %regressor.score(X1_test,y1_test))\n",
    "print(\"Train score %3f\" %regressor.score(X1_train,y1_train))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IN THIS CASE WE'RE NOT GOING TO TRAIN THIS DATASET TO THE MODEL INSTEAD WE TRY TO PREDICT THE TARGET TO CHECK THE PERFORMANCE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load another dataset to test the accuracy of the model predictions\n",
    "\n",
    "test_data = pd.read_csv(r\"D:\\world happiness report\\2017.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
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       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>7.561772</td>\n",
       "      <td>7.426227</td>\n",
       "      <td>1.564980</td>\n",
       "      <td>1.516912</td>\n",
       "      <td>0.858131</td>\n",
       "      <td>0.620071</td>\n",
       "      <td>0.290549</td>\n",
       "      <td>0.367007</td>\n",
       "      <td>2.276716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>7.527542</td>\n",
       "      <td>7.410458</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>0.617951</td>\n",
       "      <td>0.245483</td>\n",
       "      <td>0.382612</td>\n",
       "      <td>2.430182</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>151</td>\n",
       "      <td>3.471</td>\n",
       "      <td>3.543030</td>\n",
       "      <td>3.398970</td>\n",
       "      <td>0.368746</td>\n",
       "      <td>0.945707</td>\n",
       "      <td>0.326425</td>\n",
       "      <td>0.581844</td>\n",
       "      <td>0.252756</td>\n",
       "      <td>0.455220</td>\n",
       "      <td>0.540061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Syria</td>\n",
       "      <td>152</td>\n",
       "      <td>3.462</td>\n",
       "      <td>3.663669</td>\n",
       "      <td>3.260331</td>\n",
       "      <td>0.777153</td>\n",
       "      <td>0.396103</td>\n",
       "      <td>0.500533</td>\n",
       "      <td>0.081539</td>\n",
       "      <td>0.493664</td>\n",
       "      <td>0.151347</td>\n",
       "      <td>1.061574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>153</td>\n",
       "      <td>3.349</td>\n",
       "      <td>3.461430</td>\n",
       "      <td>3.236570</td>\n",
       "      <td>0.511136</td>\n",
       "      <td>1.041990</td>\n",
       "      <td>0.364509</td>\n",
       "      <td>0.390018</td>\n",
       "      <td>0.354256</td>\n",
       "      <td>0.066035</td>\n",
       "      <td>0.621130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>154</td>\n",
       "      <td>2.905</td>\n",
       "      <td>3.074690</td>\n",
       "      <td>2.735310</td>\n",
       "      <td>0.091623</td>\n",
       "      <td>0.629794</td>\n",
       "      <td>0.151611</td>\n",
       "      <td>0.059901</td>\n",
       "      <td>0.204435</td>\n",
       "      <td>0.084148</td>\n",
       "      <td>1.683024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>155</td>\n",
       "      <td>2.693</td>\n",
       "      <td>2.864884</td>\n",
       "      <td>2.521116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018773</td>\n",
       "      <td>0.270842</td>\n",
       "      <td>0.280876</td>\n",
       "      <td>0.056565</td>\n",
       "      <td>2.066005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Happiness Rank  Happiness Score  \\\n",
       "0                      Norway               1            7.537   \n",
       "1                     Denmark               2            7.522   \n",
       "2                     Iceland               3            7.504   \n",
       "3                 Switzerland               4            7.494   \n",
       "4                     Finland               5            7.469   \n",
       "..                        ...             ...              ...   \n",
       "150                    Rwanda             151            3.471   \n",
       "151                     Syria             152            3.462   \n",
       "152                  Tanzania             153            3.349   \n",
       "153                   Burundi             154            2.905   \n",
       "154  Central African Republic             155            2.693   \n",
       "\n",
       "     Upper Confidence Interval  Lower Confidence Interval  \\\n",
       "0                     7.594445                   7.479556   \n",
       "1                     7.581728                   7.462272   \n",
       "2                     7.622030                   7.385970   \n",
       "3                     7.561772                   7.426227   \n",
       "4                     7.527542                   7.410458   \n",
       "..                         ...                        ...   \n",
       "150                   3.543030                   3.398970   \n",
       "151                   3.663669                   3.260331   \n",
       "152                   3.461430                   3.236570   \n",
       "153                   3.074690                   2.735310   \n",
       "154                   2.864884                   2.521116   \n",
       "\n",
       "     Economy (GDP per Capita)    Family  Health (Life Expectancy)   Freedom  \\\n",
       "0                    1.616463  1.533524                  0.796667  0.635423   \n",
       "1                    1.482383  1.551122                  0.792566  0.626007   \n",
       "2                    1.480633  1.610574                  0.833552  0.627163   \n",
       "3                    1.564980  1.516912                  0.858131  0.620071   \n",
       "4                    1.443572  1.540247                  0.809158  0.617951   \n",
       "..                        ...       ...                       ...       ...   \n",
       "150                  0.368746  0.945707                  0.326425  0.581844   \n",
       "151                  0.777153  0.396103                  0.500533  0.081539   \n",
       "152                  0.511136  1.041990                  0.364509  0.390018   \n",
       "153                  0.091623  0.629794                  0.151611  0.059901   \n",
       "154                  0.000000  0.000000                  0.018773  0.270842   \n",
       "\n",
       "     Generosity  Trust (Government Corruption)  Dystopia Residual  \n",
       "0      0.362012                       0.315964           2.277027  \n",
       "1      0.355280                       0.400770           2.313707  \n",
       "2      0.475540                       0.153527           2.322715  \n",
       "3      0.290549                       0.367007           2.276716  \n",
       "4      0.245483                       0.382612           2.430182  \n",
       "..          ...                            ...                ...  \n",
       "150    0.252756                       0.455220           0.540061  \n",
       "151    0.493664                       0.151347           1.061574  \n",
       "152    0.354256                       0.066035           0.621130  \n",
       "153    0.204435                       0.084148           1.683024  \n",
       "154    0.280876                       0.056565           2.066005  \n",
       "\n",
       "[155 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding a new column Standard Error\n",
    "test_data[\"Standard Error\"] = test_data[\"Upper Confidence Interval\"]-test_data[\"Lower Confidence Interval\"]\n",
    "test_data.drop(test_data.iloc[:,3:5],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arranging the columns in the same order as the above datasets\n",
    "new_cols = ['Country', 'Happiness Rank', 'Happiness Score', 'Standard Error',\n",
    "       'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n",
    "       'Freedom', 'Trust (Government Corruption)', 'Generosity',\n",
    "       'Dystopia Residual']\n",
    "test_data = test_data.reindex(columns=new_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = test_data.drop(test_data.iloc[:,:3],axis =1)\n",
    "y2 = test_data.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_train,X2_test,y2_train,y2_test = train_test_split(X2,y2,test_size=0.5,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = regressor.predict(X2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "232.67755190834134\n"
     ]
    }
   ],
   "source": [
    "mse1 = mean_squared_error(y2_test,y_pred1)\n",
    "print(mse1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.704249259345405\n"
     ]
    }
   ],
   "source": [
    "mae1 = mean_absolute_error(y2_test,y_pred1)\n",
    "print(mae1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Real_values  Predicted_values\n",
      "0        118.000        143.750000\n",
      "1          4.465          3.806850\n",
      "2         76.000         97.818182\n",
      "3          5.311          4.903242\n",
      "4         52.000         48.047619\n",
      "..           ...               ...\n",
      "151        5.971          4.642000\n",
      "152       63.000         48.047619\n",
      "153        5.715          6.066667\n",
      "154       75.000         42.714286\n",
      "155        5.324          6.226000\n",
      "\n",
      "[156 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# ravel converts arrays to one-dimensional \n",
    "y2_test = np.ravel(y2_test)\n",
    "y_pred1 = np.ravel(y_pred1)\n",
    "data = {'Real_values': y2_test,\n",
    "        'Predicted_values': y_pred1}\n",
    "\n",
    "# here shows the diference between the real values and predicted values\n",
    "pred_data = pd.DataFrame(data)\n",
    "print(pred_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the trained model to a file\n",
    "filename = 'decision_tree_model.sav'\n",
    "pickle.dump(regressor, open(filename, 'wb'))\n",
    "\n",
    "# Load the saved model from the file\n",
    "#loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  }
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