Life_Expectancy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0   \n",
       "3  Afghanistan  2012  Developing              59.5            272.0   \n",
       "4  Afghanistan  2011  Developing              59.2            275.0   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n",
       "0             62     0.01               71.279624         65.0      1154  ...   \n",
       "1             64     0.01               73.523582         62.0       492  ...   \n",
       "2             66     0.01               73.219243         64.0       430  ...   \n",
       "3             69     0.01               78.184215         67.0      2787  ...   \n",
       "4             71     0.01                7.097109         68.0      3013  ...   \n",
       "\n",
       "   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0    6.0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1   58.0               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2   62.0               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3   67.0               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4   68.0               7.87         68.0        0.1   63.537231   2978599.0   \n",
       "\n",
       "    thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                   17.2                 17.3   \n",
       "1                   17.5                 17.5   \n",
       "2                   17.7                 17.7   \n",
       "3                   17.9                 18.0   \n",
       "4                   18.2                 18.2   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:/Users/DITU/Desktop/Life Expectancy Data.csv\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df=df.dropna() #to drop null values\n",
    "x=df.iloc[:,16:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x.drop(\"Population\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       65.0\n",
       "1       59.9\n",
       "2       59.9\n",
       "3       59.5\n",
       "4       59.2\n",
       "5       58.8\n",
       "6       58.6\n",
       "7       58.1\n",
       "8       57.5\n",
       "9       57.3\n",
       "10      57.3\n",
       "11      57.0\n",
       "12      56.7\n",
       "13      56.2\n",
       "14      55.3\n",
       "15      54.8\n",
       "16      77.8\n",
       "17      77.5\n",
       "18      77.2\n",
       "19      76.9\n",
       "20      76.6\n",
       "21      76.2\n",
       "22      76.1\n",
       "23      75.3\n",
       "24      75.9\n",
       "25      74.2\n",
       "26      73.5\n",
       "27      73.0\n",
       "28      72.8\n",
       "29      73.3\n",
       "        ... \n",
       "2853    69.6\n",
       "2854    69.4\n",
       "2855    69.3\n",
       "2856    69.1\n",
       "2857    69.0\n",
       "2907    61.1\n",
       "2908    63.0\n",
       "2909    59.2\n",
       "2910    58.2\n",
       "2911    58.0\n",
       "2912    57.4\n",
       "2913    55.7\n",
       "2914    52.6\n",
       "2915    58.0\n",
       "2916    49.3\n",
       "2923    59.2\n",
       "2924    58.0\n",
       "2925    56.6\n",
       "2926    54.9\n",
       "2927    52.4\n",
       "2928    50.0\n",
       "2929    48.2\n",
       "2930    46.6\n",
       "2931    45.4\n",
       "2932    44.6\n",
       "2933    44.3\n",
       "2934    44.5\n",
       "2935    44.8\n",
       "2936    45.3\n",
       "2937    46.0\n",
       "Name: Life expectancy , Length: 1649, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "x_train , x_test, y_train,y_test=train_test_split(x,y,test_size=0.12)\n",
    "model = LinearRegression().fit(x_train, y_train)\n",
    "#model.fit(x_train.values.reshape(-1,1),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5921608848633901\n"
     ]
    }
   ],
   "source": [
    "model_confidence=model.score(x_test,y_test)\n",
    "print(model_confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}