Life_Expectancy_Classification_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mod 5 Project: Classifying Life Expectancy \n",
    "## (World Health Organization Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Group Partners: Filis, Manisha, Pablo**\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ran the following commands in terminal to install Profiling (visualizing data), and Altair (map):\n",
    "\n",
    "```\n",
    "conda install -c conda-forge altair vega_datasets notebook vega\n",
    "```\n",
    "\n",
    "```\n",
    "conda install -c anaconda pandas-profiling\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Manipulation\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "\n",
    "# #Making Map Visualizations\n",
    "# import altair as alt\n",
    "# alt.renderers.enable('notebook')\n",
    "# from vega_datasets import data\n",
    "\n",
    "# #Making Line Plot Visualizations\n",
    "# import plotly.plotly as py \n",
    "# import cufflinks as cf \n",
    "# from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \n",
    "# init_notebook_mode(connected=True) \n",
    "# cf.go_offline()\n",
    "\n",
    "#Displaying EDA Profile\n",
    "import pandas_profiling\n",
    "\n",
    "#Disabling warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "from random import randint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn import tree \n",
    "from sklearn import svm\n",
    "\n",
    "# Measuring models and feature importance\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# sklearn processing stuff\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import cross_validate, cross_val_score, cross_val_predict\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in csv and display first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Life Expectancy Data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensions of dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 22)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for NaN's in out entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population                         652\n",
       "Hepatitis B                        553\n",
       "GDP                                448\n",
       "Total expenditure                  226\n",
       "Alcohol                            194\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       " BMI                                34\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Diphtheria                          19\n",
       "Polio                               19\n",
       "Adult Mortality                     10\n",
       "Life expectancy                     10\n",
       "under-five deaths                    0\n",
       " HIV/AIDS                            0\n",
       "Measles                              0\n",
       "percentage expenditure               0\n",
       "infant deaths                        0\n",
       "Status                               0\n",
       "Year                                 0\n",
       "Country                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the distribution of our potential target variable, life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a219b4710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['Life expectancy '].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seeing any trend of the life expectancies with respect to years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# #This plot of life expectancies is of one country with respect to years. \n",
    "# df_group.loc['Albania'].iplot(y='Life expectancy ')\n",
    "# print('Albania')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_group = df.set_index(['Country', 'Year'])\n",
    "plt.rc('xtick',labelsize=20)\n",
    "plt.rc('ytick',labelsize=20)\n",
    "countries = df['Country'].unique()\n",
    "fig = plt.figure(figsize=(14,12))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.ylabel('Life Expectancy', fontsize=20)\n",
    "plt.xlabel('Years', fontsize=20)\n",
    "plt.title('Life Expectancies per Year', fontsize=20)\n",
    "\n",
    "for country in countries[:20]:\n",
    "    df_group.loc[country].plot(y='Life expectancy ', ax=ax)\n",
    "L = plt.legend(countries[:20])\n",
    "\n",
    "plt.savefig('Life Expectacies per Year.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding: Most countries appear relatively flat, indicating a small correlation with time dependence. We investigate this more below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using profiling to speed up exporatory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile = pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(profile.html))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying the columns in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
       "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
       "       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',\n",
       "       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',\n",
       "       ' thinness  1-19 years', ' thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Status`\n",
    "Developed or Developing status\n",
    "\n",
    "`Life expectancy` \n",
    "Life Expectancy in age\n",
    "\n",
    "`Adult Mortality`\n",
    "Adult Mortality Rates of both sexes (probability of dying between 15 and 60 years per 1000 population)\n",
    "\n",
    "`infant deaths`\n",
    "Number of Infant Deaths per 1000 population\n",
    "\n",
    "`Alcohol`\n",
    "Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)\n",
    "\n",
    "`percentage expenditure`\n",
    "Expenditure on health as a percentage of Gross Domestic Product per capita(%)\n",
    "\n",
    "`Hepatitis B`\n",
    "Hepatitis B (HepB) immunization coverage among 1-year-olds (%)\n",
    "\n",
    "`Measles` \n",
    "Measles - number of reported cases per 1000 population\n",
    "\n",
    "`BMI` \n",
    "Average Body Mass Index of entire population\n",
    "\n",
    "`under-five deaths` \n",
    "Number of under-five deaths per 1000 population\n",
    "\n",
    "`Polio`\n",
    "Polio (Pol3) immunization coverage among 1-year-olds (%)\n",
    "\n",
    "`Total expenditure`\n",
    "General government expenditure on health as a percentage of total government expenditure (%)\n",
    "\n",
    "`Diphtheria` \n",
    "Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%)\n",
    "\n",
    "`HIV/AIDS`\n",
    "Deaths per 1 000 live births HIV/AIDS (0-4 years)\n",
    "\n",
    "`GDP`\n",
    "Gross Domestic Product per capita (in USD)\n",
    "\n",
    "`Population`\n",
    "Population of the country\n",
    "\n",
    "`thinness 1-19 years`\n",
    "Prevalence of thinness among children and adolescents for Age 10 to 19 (% )\n",
    "\n",
    "`thinness 5-9 years`\n",
    "Prevalence of thinness among children for Age 5 to 9(%)\n",
    "\n",
    "`Income composition of resources`\n",
    "Human Development Index in terms of income composition of resources (index ranging from 0 to 1) url: http://hdr.undp.org/en/content/human-development-index-hdi\n",
    "\n",
    "`Schooling`\n",
    "Number of years of Schooling(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</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>BMI</th>\n",
       "      <th>under-five deaths</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>Life expectancy</th>\n",
       "      <td>0.170033</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.696359</td>\n",
       "      <td>-0.196557</td>\n",
       "      <td>0.404877</td>\n",
       "      <td>0.381864</td>\n",
       "      <td>0.256762</td>\n",
       "      <td>-0.157586</td>\n",
       "      <td>0.567694</td>\n",
       "      <td>-0.222529</td>\n",
       "      <td>0.465556</td>\n",
       "      <td>0.218086</td>\n",
       "      <td>0.479495</td>\n",
       "      <td>-0.556556</td>\n",
       "      <td>0.461455</td>\n",
       "      <td>-0.021538</td>\n",
       "      <td>-0.477183</td>\n",
       "      <td>-0.471584</td>\n",
       "      <td>0.724776</td>\n",
       "      <td>0.751975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "Life expectancy   0.170033               1.0        -0.696359      -0.196557   \n",
       "\n",
       "                   Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "Life expectancy   0.404877                0.381864     0.256762 -0.157586   \n",
       "\n",
       "                      BMI   under-five deaths      Polio  Total expenditure  \\\n",
       "Life expectancy   0.567694           -0.222529  0.465556           0.218086   \n",
       "\n",
       "                  Diphtheria    HIV/AIDS       GDP  Population  \\\n",
       "Life expectancy      0.479495  -0.556556  0.461455   -0.021538   \n",
       "\n",
       "                   thinness  1-19 years   thinness 5-9 years  \\\n",
       "Life expectancy               -0.477183            -0.471584   \n",
       "\n",
       "                  Income composition of resources  Schooling  \n",
       "Life expectancy                          0.724776   0.751975  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the global life expectancy for 2015 using Altair mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum year is 2015\n",
      "Minimum year is 2000\n"
     ]
    }
   ],
   "source": [
    "print('Maximum year is', df['Year'].max())\n",
    "print('Minimum year is', df['Year'].min())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's filter by 2015 to make a map of life expectency per country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2015 = df[df['Year']==2015]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading in the country codes tsv to make the map (file generated from UN stats webpage: \n",
    "https://unstats.un.org/unsd/methodology/m49/  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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 or Area</th>\n",
       "      <th>M49 code</th>\n",
       "      <th>ISO-alpha3 code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aland Islands</td>\n",
       "      <td>248</td>\n",
       "      <td>ALA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>8</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>12</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>16</td>\n",
       "      <td>ASM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country or Area  M49 code ISO-alpha3 code\n",
       "0     Afghanistan         4             AFG\n",
       "1   Aland Islands       248             ALA\n",
       "2         Albania         8             ALB\n",
       "3         Algeria        12             DZA\n",
       "4  American Samoa        16             ASM"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_codes = pd.read_csv('country_code_2.tsv',sep='\\t')\n",
    "country_codes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Matching countries to country names in country codes tsv data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2015.replace(\"Côte d'Ivoire\", \"Cote d'Ivoire\", inplace=True)\n",
    "df_2015.replace('Swaziland', 'Eswatini', inplace=True)\n",
    "df_2015.replace('The former Yugoslav republic of Macedonia', 'North Macedonia', inplace=True)\n",
    "\n",
    "df_2015_map = df_2015.merge(country_codes, how=\"left\", left_on=\"Country\",right_on=\"Country or Area\")\n",
    "df_2015_map.rename(columns={'M49 code': 'id'}, inplace=True)\n",
    "df_2015_map.drop(columns=['ISO-alpha3 code', 'Country or Area'], inplace=True)\n",
    "\n",
    "df_2015_map['tooltip']=df_2015_map['Country']+': '+df_2015_map['Life expectancy '].astype(str) + ' yr'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# source = alt.topo_feature(data.world_110m.url,'countries')\n",
    "\n",
    "# map_plot = alt.Chart(source).mark_geoshape().encode(\n",
    "#     color=alt.Color('Life expectancy :Q', legend=alt.Legend(title='Years')),\n",
    "#     tooltip='tooltip:N'\n",
    "# ).transform_lookup(\n",
    "#    lookup='id',\n",
    "#    from_=alt.LookupData(df_2015_map, 'id', ['Life expectancy ', 'tooltip'])\n",
    "# ).project(\n",
    "#    type='equirectangular'\n",
    "# ).properties(\n",
    "#     width=900,\n",
    "#     height=540,\n",
    "#     title=('Life Expectency in Years')\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# map_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding: The country with the highest life-expectancy is Slovenia (Europe) at 88 years, and the lowest is Sierra Leonne (Africa) at 52 years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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>BMI</th>\n",
       "      <th>under-five deaths</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",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th>2015</th>\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>19.1</td>\n",
       "      <td>83</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>2014</th>\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>18.6</td>\n",
       "      <td>86</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>2013</th>\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>18.1</td>\n",
       "      <td>89</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>2012</th>\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>17.6</td>\n",
       "      <td>93</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>2011</th>\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>17.2</td>\n",
       "      <td>97</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",
       "</div>"
      ],
      "text/plain": [
       "                      Status  Life expectancy   Adult Mortality  \\\n",
       "Country     Year                                                  \n",
       "Afghanistan 2015  Developing              65.0            263.0   \n",
       "            2014  Developing              59.9            271.0   \n",
       "            2013  Developing              59.9            268.0   \n",
       "            2012  Developing              59.5            272.0   \n",
       "            2011  Developing              59.2            275.0   \n",
       "\n",
       "                  infant deaths  Alcohol  percentage expenditure  Hepatitis B  \\\n",
       "Country     Year                                                                \n",
       "Afghanistan 2015             62     0.01               71.279624         65.0   \n",
       "            2014             64     0.01               73.523582         62.0   \n",
       "            2013             66     0.01               73.219243         64.0   \n",
       "            2012             69     0.01               78.184215         67.0   \n",
       "            2011             71     0.01                7.097109         68.0   \n",
       "\n",
       "                  Measles    BMI   under-five deaths   Polio  \\\n",
       "Country     Year                                               \n",
       "Afghanistan 2015      1154   19.1                  83    6.0   \n",
       "            2014       492   18.6                  86   58.0   \n",
       "            2013       430   18.1                  89   62.0   \n",
       "            2012      2787   17.6                  93   67.0   \n",
       "            2011      3013   17.2                  97   68.0   \n",
       "\n",
       "                  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "Country     Year                                                          \n",
       "Afghanistan 2015               8.16         65.0        0.1  584.259210   \n",
       "            2014               8.18         62.0        0.1  612.696514   \n",
       "            2013               8.13         64.0        0.1  631.744976   \n",
       "            2012               8.52         67.0        0.1  669.959000   \n",
       "            2011               7.87         68.0        0.1   63.537231   \n",
       "\n",
       "                  Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "Country     Year                                                           \n",
       "Afghanistan 2015  33736494.0                   17.2                 17.3   \n",
       "            2014    327582.0                   17.5                 17.5   \n",
       "            2013  31731688.0                   17.7                 17.7   \n",
       "            2012   3696958.0                   17.9                 18.0   \n",
       "            2011   2978599.0                   18.2                 18.2   \n",
       "\n",
       "                  Income composition of resources  Schooling  \n",
       "Country     Year                                              \n",
       "Afghanistan 2015                            0.479       10.1  \n",
       "            2014                            0.476       10.0  \n",
       "            2013                            0.470        9.9  \n",
       "            2012                            0.463        9.8  \n",
       "            2011                            0.454        9.5  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group = df.set_index(['Country','Year'])\n",
    "df_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop all NaN's and check remaining length of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nona = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1649, 22)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename \"Life expectancy\" column as \"target\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "      <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nona.rename(columns={'Life expectancy ':'target'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
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       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
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       "      <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",
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       "      <td>59.9</td>\n",
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       "      <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",
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       "      <td>67.0</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
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       "    <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",
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       "      <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",
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       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  target  Adult Mortality  infant deaths  \\\n",
       "0  Afghanistan  2015  Developing    65.0            263.0             62   \n",
       "1  Afghanistan  2014  Developing    59.9            271.0             64   \n",
       "2  Afghanistan  2013  Developing    59.9            268.0             66   \n",
       "3  Afghanistan  2012  Developing    59.5            272.0             69   \n",
       "4  Afghanistan  2011  Developing    59.2            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles   ...  Polio  \\\n",
       "0     0.01               71.279624         65.0      1154  ...    6.0   \n",
       "1     0.01               73.523582         62.0       492  ...   58.0   \n",
       "2     0.01               73.219243         64.0       430  ...   62.0   \n",
       "3     0.01               78.184215         67.0      2787  ...   67.0   \n",
       "4     0.01                7.097109         68.0      3013  ...   68.0   \n",
       "\n",
       "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4               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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spliting the data at the median value, and anything a above that age will be 1 for long life expectancy and 0 for low life expectancy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1649.000000\n",
       "mean       69.302304\n",
       "std         8.796834\n",
       "min        44.000000\n",
       "25%        64.400000\n",
       "50%        71.700000\n",
       "75%        75.000000\n",
       "max        89.000000\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona['target'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_age = np.median(df_nona['target'])\n",
    "df_nona['target'] = df_nona.target.apply(lambda x: 1 if x >= median_age  else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    828\n",
       "0    821\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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>0</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>0</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>0</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  target  Adult Mortality  infant deaths  \\\n",
       "0  Afghanistan  2015  Developing       0            263.0             62   \n",
       "1  Afghanistan  2014  Developing       0            271.0             64   \n",
       "2  Afghanistan  2013  Developing       0            268.0             66   \n",
       "3  Afghanistan  2012  Developing       0            272.0             69   \n",
       "4  Afghanistan  2011  Developing       0            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles   ...  Polio  \\\n",
       "0     0.01               71.279624         65.0      1154  ...    6.0   \n",
       "1     0.01               73.523582         62.0       492  ...   58.0   \n",
       "2     0.01               73.219243         64.0       430  ...   62.0   \n",
       "3     0.01               78.184215         67.0      2787  ...   67.0   \n",
       "4     0.01                7.097109         68.0      3013  ...   68.0   \n",
       "\n",
       "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4               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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check on Country Target Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azerbaijan\n",
      "Bangladesh\n",
      "Belarus\n",
      "Brazil\n",
      "Bulgaria\n",
      "Cabo Verde\n",
      "Colombia\n",
      "Dominican Republic\n",
      "El Salvador\n",
      "Guatemala\n",
      "Honduras\n",
      "Iraq\n",
      "Latvia\n",
      "Lithuania\n",
      "Maldives\n",
      "Mauritius\n",
      "Morocco\n",
      "Nicaragua\n",
      "Romania\n",
      "Russian Federation\n",
      "Samoa\n",
      "Sri Lanka\n",
      "Suriname\n",
      "Thailand\n",
      "Tonga\n",
      "Trinidad and Tobago\n",
      "Turkey\n",
      "Ukraine\n",
      "Vanuatu\n"
     ]
    }
   ],
   "source": [
    "#Check to see whether each country's life expectancy stays the same, or shifts between class targets (0,1)\n",
    "count = 0 \n",
    "for country in df_nona['Country'].unique(): \n",
    "    check = df_nona.loc[df_nona['Country']== country,'target'].nunique()\n",
    "    if (check == 1):\n",
    "        count +=1\n",
    "    else:\n",
    "        print(country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7819548872180451"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of countries that only belong to 1 target class\n",
    "count / df_nona['Country'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding #1:\n",
    "### Life expectancy does not change over the 15 year time period for approximately 78% of the countries in our dataset.\n",
    "<br/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get dummies for status column (categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>target</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>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",
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       "      <th>Developed</th>\n",
       "      <th>Developing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
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       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  target  Adult Mortality  infant deaths  \\\n",
       "0  Afghanistan  2015  Developing       0            263.0             62   \n",
       "1  Afghanistan  2014  Developing       0            271.0             64   \n",
       "2  Afghanistan  2013  Developing       0            268.0             66   \n",
       "3  Afghanistan  2012  Developing       0            272.0             69   \n",
       "4  Afghanistan  2011  Developing       0            275.0             71   \n",
       "\n",
       "   Alcohol  percentage expenditure  Hepatitis B  Measles   ...  Diphtheria   \\\n",
       "0     0.01               71.279624         65.0      1154  ...         65.0   \n",
       "1     0.01               73.523582         62.0       492  ...         62.0   \n",
       "2     0.01               73.219243         64.0       430  ...         64.0   \n",
       "3     0.01               78.184215         67.0      2787  ...         67.0   \n",
       "4     0.01                7.097109         68.0      3013  ...         68.0   \n",
       "\n",
       "    HIV/AIDS         GDP  Population   thinness  1-19 years  \\\n",
       "0        0.1  584.259210  33736494.0                   17.2   \n",
       "1        0.1  612.696514    327582.0                   17.5   \n",
       "2        0.1  631.744976  31731688.0                   17.7   \n",
       "3        0.1  669.959000   3696958.0                   17.9   \n",
       "4        0.1   63.537231   2978599.0                   18.2   \n",
       "\n",
       "    thinness 5-9 years  Income composition of resources  Schooling  Developed  \\\n",
       "0                 17.3                            0.479       10.1          0   \n",
       "1                 17.5                            0.476       10.0          0   \n",
       "2                 17.7                            0.470        9.9          0   \n",
       "3                 18.0                            0.463        9.8          0   \n",
       "4                 18.2                            0.454        9.5          0   \n",
       "\n",
       "   Developing  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#developed = 0, developing =1\n",
    "#look into dropping one\n",
    "df_nona = pd.concat([df_nona, pd.get_dummies(df_nona['Status'])], axis=1)\n",
    "df_nona.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping old status column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Afghanistan</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
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       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
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       "      <td>612.696514</td>\n",
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       "      <td>64.0</td>\n",
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       "      <td>18.1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</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>17.6</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</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>17.2</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  target  Adult Mortality  infant deaths  Alcohol  \\\n",
       "0  Afghanistan  2015       0            263.0             62     0.01   \n",
       "1  Afghanistan  2014       0            271.0             64     0.01   \n",
       "2  Afghanistan  2013       0            268.0             66     0.01   \n",
       "3  Afghanistan  2012       0            272.0             69     0.01   \n",
       "4  Afghanistan  2011       0            275.0             71     0.01   \n",
       "\n",
       "   percentage expenditure  Hepatitis B  Measles    BMI   ...  Diphtheria   \\\n",
       "0               71.279624         65.0      1154   19.1  ...         65.0   \n",
       "1               73.523582         62.0       492   18.6  ...         62.0   \n",
       "2               73.219243         64.0       430   18.1  ...         64.0   \n",
       "3               78.184215         67.0      2787   17.6  ...         67.0   \n",
       "4                7.097109         68.0      3013   17.2  ...         68.0   \n",
       "\n",
       "    HIV/AIDS         GDP  Population   thinness  1-19 years  \\\n",
       "0        0.1  584.259210  33736494.0                   17.2   \n",
       "1        0.1  612.696514    327582.0                   17.5   \n",
       "2        0.1  631.744976  31731688.0                   17.7   \n",
       "3        0.1  669.959000   3696958.0                   17.9   \n",
       "4        0.1   63.537231   2978599.0                   18.2   \n",
       "\n",
       "    thinness 5-9 years  Income composition of resources  Schooling  Developed  \\\n",
       "0                 17.3                            0.479       10.1          0   \n",
       "1                 17.5                            0.476       10.0          0   \n",
       "2                 17.7                            0.470        9.9          0   \n",
       "3                 18.0                            0.463        9.8          0   \n",
       "4                 18.2                            0.454        9.5          0   \n",
       "\n",
       "   Developing  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nona.drop(['Status'], axis=1, inplace=True)\n",
    "df_nona.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5181347150259067"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries = df.Country.unique()\n",
    "# Choose random countries\n",
    "num_countries_to_withhold = 100\n",
    "withhold_countries = set([])\n",
    "while len(withhold_countries) < num_countries_to_withhold:\n",
    "    # Get a random integer so we can choose a random country\n",
    "    withhold_countries.add(all_countries[randint(0, len(all_countries) - 1)])\n",
    "\n",
    "withhold_countries = list(withhold_countries)\n",
    "training_countries = [country for country in all_countries if (country not in withhold_countries)]\n",
    "\n",
    "\n",
    "df_country_split_a = df_nona[df_nona['Country'].isin(training_countries)]\n",
    "df_country_split_b = df_nona[df_nona['Country'].isin(withhold_countries)]\n",
    "\n",
    "num_countries_to_withhold/len(all_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5181347150259067"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_countries_to_withhold/len(all_countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We only train the model on 50% of our countries to check whether the country has an effect on life-expectency. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define X and y\n",
    "We train on group a and predict on group b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'target'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'target'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-b5888c8f60cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_country_split_a\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_country_split_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'target'"
     ]
    }
   ],
   "source": [
    "y = df_country_split_a['target']\n",
    "X = df_country_split_a.drop(['target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['Country'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\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=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=10, splitter='best')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier(random_state=10)  \n",
    "classifier.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is :91.70731707317074\n",
      "\n",
      "AUC is :0.92\n",
      "\n",
      "Confusion Matrix\n",
      "----------------\n"
     ]
    },
    {
     "data": {
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       "      <th>Predicted</th>\n",
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       "      <td>205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0    1  All\n",
       "True                    \n",
       "0           92    9  101\n",
       "1            8   96  104\n",
       "All        100  105  205"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = accuracy_score(y_test,y_pred) * 100\n",
    "print(\"Accuracy is :{0}\".format(acc))\n",
    "\n",
    "# Check the AUC for predictions\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "print(\"\\nAUC is :{0}\".format(round(roc_auc,2)))\n",
    "\n",
    "# Create and print a confusion matrix \n",
    "print('\\nConfusion Matrix')\n",
    "print('----------------')\n",
    "pd.crosstab(y_test, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model misclassified 33 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00118206, 0.33107893, 0.        , 0.01837252, 0.0128686 ,\n",
       "       0.01336809, 0.        , 0.0081295 , 0.00851855, 0.        ,\n",
       "       0.00611588, 0.        , 0.00616728, 0.032197  , 0.02776819,\n",
       "       0.01770074, 0.00524218, 0.46671023, 0.04458025, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier.feature_importances_\n",
    "def plot_feature_importances(model):\n",
    "    n_features = X_train.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), X_train.columns.values) \n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "\n",
    "plot_feature_importances(classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Income composition of resources which is the Human Development Index combines education, life expectency, and GNI index (economic factor). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classification  For \"A\" Country Split \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>target</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>BMI</th>\n",
       "      <th>...</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",
       "      <th>Developed</th>\n",
       "      <th>Developing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>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>19.1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</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>18.6</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</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>18.1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</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>17.6</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</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>17.2</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  target  Adult Mortality  infant deaths  Alcohol  \\\n",
       "0  Afghanistan  2015       0            263.0             62     0.01   \n",
       "1  Afghanistan  2014       0            271.0             64     0.01   \n",
       "2  Afghanistan  2013       0            268.0             66     0.01   \n",
       "3  Afghanistan  2012       0            272.0             69     0.01   \n",
       "4  Afghanistan  2011       0            275.0             71     0.01   \n",
       "\n",
       "   percentage expenditure  Hepatitis B  Measles    BMI   ...  Diphtheria   \\\n",
       "0               71.279624         65.0      1154   19.1  ...         65.0   \n",
       "1               73.523582         62.0       492   18.6  ...         62.0   \n",
       "2               73.219243         64.0       430   18.1  ...         64.0   \n",
       "3               78.184215         67.0      2787   17.6  ...         67.0   \n",
       "4                7.097109         68.0      3013   17.2  ...         68.0   \n",
       "\n",
       "    HIV/AIDS         GDP  Population   thinness  1-19 years  \\\n",
       "0        0.1  584.259210  33736494.0                   17.2   \n",
       "1        0.1  612.696514    327582.0                   17.5   \n",
       "2        0.1  631.744976  31731688.0                   17.7   \n",
       "3        0.1  669.959000   3696958.0                   17.9   \n",
       "4        0.1   63.537231   2978599.0                   18.2   \n",
       "\n",
       "    thinness 5-9 years  Income composition of resources  Schooling  Developed  \\\n",
       "0                 17.3                            0.479       10.1          0   \n",
       "1                 17.5                            0.476       10.0          0   \n",
       "2                 17.7                            0.470        9.9          0   \n",
       "3                 18.0                            0.463        9.8          0   \n",
       "4                 18.2                            0.454        9.5          0   \n",
       "\n",
       "   Developing  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_split_a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_country_split_a['target']\n",
    "X = df_country_split_a.drop(['target', 'Country','Adult Mortality','Income composition of resources','Year', 'Developing'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Searching to find Optimal Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "forest=RandomForestClassifier(oob_score=True)\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [20,40,60,80,100],\n",
    "    'min_samples_split': [2, 4, 6],\n",
    "    'min_samples_leaf': [2, 4, 6]\n",
    "}\n",
    "\n",
    "gs = GridSearchCV(forest, param_grid, cv=5, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=True, random_state=None,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'min_samples_leaf': [2, 4, 6],\n",
       "                         'min_samples_split': [2, 4, 6],\n",
       "                         'n_estimators': [20, 40, 60, 80, 100]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=True, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.050828</td>\n",
       "      <td>0.016085</td>\n",
       "      <td>0.004947</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.886792</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.019402</td>\n",
       "      <td>17</td>\n",
       "      <td>0.988067</td>\n",
       "      <td>0.990453</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>0.990499</td>\n",
       "      <td>0.990499</td>\n",
       "      <td>0.989046</td>\n",
       "      <td>0.001911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.084668</td>\n",
       "      <td>0.028245</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.896226</td>\n",
       "      <td>0.933962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>10</td>\n",
       "      <td>0.992840</td>\n",
       "      <td>0.988067</td>\n",
       "      <td>0.992857</td>\n",
       "      <td>0.992874</td>\n",
       "      <td>0.992874</td>\n",
       "      <td>0.991902</td>\n",
       "      <td>0.001918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.132193</td>\n",
       "      <td>0.025768</td>\n",
       "      <td>0.010234</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.905660</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929524</td>\n",
       "      <td>0.016282</td>\n",
       "      <td>5</td>\n",
       "      <td>0.992840</td>\n",
       "      <td>0.995227</td>\n",
       "      <td>0.995238</td>\n",
       "      <td>0.995249</td>\n",
       "      <td>0.995249</td>\n",
       "      <td>0.994761</td>\n",
       "      <td>0.000960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.132931</td>\n",
       "      <td>0.022666</td>\n",
       "      <td>0.009816</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.915094</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931429</td>\n",
       "      <td>0.013731</td>\n",
       "      <td>4</td>\n",
       "      <td>0.995227</td>\n",
       "      <td>0.992840</td>\n",
       "      <td>0.988095</td>\n",
       "      <td>0.997625</td>\n",
       "      <td>0.992874</td>\n",
       "      <td>0.993332</td>\n",
       "      <td>0.003159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.145344</td>\n",
       "      <td>0.005973</td>\n",
       "      <td>0.011205</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.915094</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.013019</td>\n",
       "      <td>1</td>\n",
       "      <td>0.995227</td>\n",
       "      <td>0.992840</td>\n",
       "      <td>0.988095</td>\n",
       "      <td>0.995249</td>\n",
       "      <td>0.997625</td>\n",
       "      <td>0.993807</td>\n",
       "      <td>0.003232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.050828      0.016085         0.004947        0.001878   \n",
       "1       0.084668      0.028245         0.007353        0.002831   \n",
       "2       0.132193      0.025768         0.010234        0.002271   \n",
       "3       0.132931      0.022666         0.009816        0.001076   \n",
       "4       0.145344      0.005973         0.011205        0.001162   \n",
       "\n",
       "  param_min_samples_leaf param_min_samples_split param_n_estimators  \\\n",
       "0                      2                       2                 20   \n",
       "1                      2                       2                 40   \n",
       "2                      2                       2                 60   \n",
       "3                      2                       2                 80   \n",
       "4                      2                       2                100   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'min_samples_leaf': 2, 'min_samples_split': 2...           0.886792   \n",
       "1  {'min_samples_leaf': 2, 'min_samples_split': 2...           0.896226   \n",
       "2  {'min_samples_leaf': 2, 'min_samples_split': 2...           0.905660   \n",
       "3  {'min_samples_leaf': 2, 'min_samples_split': 2...           0.915094   \n",
       "4  {'min_samples_leaf': 2, 'min_samples_split': 2...           0.915094   \n",
       "\n",
       "   split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0           0.943396  ...         0.920000        0.019402               17   \n",
       "1           0.933962  ...         0.923810        0.016847               10   \n",
       "2           0.924528  ...         0.929524        0.016282                5   \n",
       "3           0.924528  ...         0.931429        0.013731                4   \n",
       "4           0.924528  ...         0.933333        0.013019                1   \n",
       "\n",
       "   split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0            0.988067            0.990453            0.985714   \n",
       "1            0.992840            0.988067            0.992857   \n",
       "2            0.992840            0.995227            0.995238   \n",
       "3            0.995227            0.992840            0.988095   \n",
       "4            0.995227            0.992840            0.988095   \n",
       "\n",
       "   split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.990499            0.990499          0.989046         0.001911  \n",
       "1            0.992874            0.992874          0.991902         0.001918  \n",
       "2            0.995249            0.995249          0.994761         0.000960  \n",
       "3            0.997625            0.992874          0.993332         0.003159  \n",
       "4            0.995249            0.997625          0.993807         0.003232  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_cv_df=pd.DataFrame(gs.cv_results_)\n",
    "gs_cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# gs_cv_df=pd.DataFrame(gs.cv_results_)\n",
    "# gs_cv_df=gs_cv_df[gs_cv_df['params']=={'min_samples_leaf': 2, 'min_samples_split': 4, 'n_estimators': 100}]\n",
    "# gs_cv_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.147840</td>\n",
       "      <td>0.013979</td>\n",
       "      <td>0.011097</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 2...</td>\n",
       "      <td>0.915094</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.013019</td>\n",
       "      <td>1</td>\n",
       "      <td>0.995227</td>\n",
       "      <td>0.992840</td>\n",
       "      <td>0.988095</td>\n",
       "      <td>0.995249</td>\n",
       "      <td>0.997625</td>\n",
       "      <td>0.993807</td>\n",
       "      <td>0.003232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.112420</td>\n",
       "      <td>0.014155</td>\n",
       "      <td>0.008496</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 4...</td>\n",
       "      <td>0.905660</td>\n",
       "      <td>0.933962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.017836</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997613</td>\n",
       "      <td>0.983294</td>\n",
       "      <td>0.990476</td>\n",
       "      <td>0.992874</td>\n",
       "      <td>0.992874</td>\n",
       "      <td>0.991426</td>\n",
       "      <td>0.004680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.086996</td>\n",
       "      <td>0.013510</td>\n",
       "      <td>0.007405</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>{'min_samples_leaf': 2, 'min_samples_split': 6...</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.017275</td>\n",
       "      <td>1</td>\n",
       "      <td>0.983294</td>\n",
       "      <td>0.980907</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>0.988124</td>\n",
       "      <td>0.988124</td>\n",
       "      <td>0.985232</td>\n",
       "      <td>0.002808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "4        0.147840      0.013979         0.011097        0.001674   \n",
       "8        0.112420      0.014155         0.008496        0.000225   \n",
       "12       0.086996      0.013510         0.007405        0.000498   \n",
       "\n",
       "   param_min_samples_leaf param_min_samples_split param_n_estimators  \\\n",
       "4                       2                       2                100   \n",
       "8                       2                       4                 80   \n",
       "12                      2                       6                 60   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "4   {'min_samples_leaf': 2, 'min_samples_split': 2...           0.915094   \n",
       "8   {'min_samples_leaf': 2, 'min_samples_split': 4...           0.905660   \n",
       "12  {'min_samples_leaf': 2, 'min_samples_split': 6...           0.924528   \n",
       "\n",
       "    split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "4            0.924528  ...         0.933333        0.013019                1   \n",
       "8            0.933962  ...         0.933333        0.017836                1   \n",
       "12           0.943396  ...         0.933333        0.017275                1   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "4             0.995227            0.992840            0.988095   \n",
       "8             0.997613            0.983294            0.990476   \n",
       "12            0.983294            0.980907            0.985714   \n",
       "\n",
       "    split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "4             0.995249            0.997625          0.993807         0.003232  \n",
       "8             0.992874            0.992874          0.991426         0.004680  \n",
       "12            0.988124            0.988124          0.985232         0.002808  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_cv_df=pd.DataFrame(gs.cv_results_)\n",
    "gs_cv_df=gs_cv_df[gs_cv_df['rank_test_score']==1]\n",
    "gs_cv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest with Optimized Hyperparameters and Multiple Metric Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#running random forest with optimized paramaters from grid search\n",
    "forest2 = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=2, min_samples_split=2,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
    "                       n_jobs=None, oob_score=True, random_state=10, verbose=0,\n",
    "                       warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = ['accuracy','precision', 'recall', 'f1']                \n",
    "rfc_cv=cross_validate(forest2, X_train,y_train, cv=5, scoring=scoring, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rfc_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>train_precision</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>train_recall</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>train_f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_accuracy  train_accuracy  test_precision  \\\n",
       "0      0.18        0.08           0.90            1.00            0.85   \n",
       "1      0.14        0.05           0.94            1.00            0.94   \n",
       "2      0.14        0.04           0.96            0.99            0.96   \n",
       "3      0.16        0.05           0.93            0.99            0.89   \n",
       "4      0.14        0.04           0.93            1.00            0.89   \n",
       "\n",
       "   train_precision  test_recall  train_recall  test_f1  train_f1  \n",
       "0             1.00         0.96           1.0     0.90      1.00  \n",
       "1             0.99         0.94           1.0     0.94      1.00  \n",
       "2             0.99         0.96           1.0     0.96      0.99  \n",
       "3             0.99         0.98           1.0     0.94      0.99  \n",
       "4             1.00         0.98           1.0     0.94      1.00  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_cv_df=pd.DataFrame.from_dict(rfc_cv)\n",
    "rfc_cv_df=rfc_cv_df.round(2)\n",
    "rfc_cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit_time           0.152\n",
       "score_time         0.052\n",
       "test_accuracy      0.932\n",
       "train_accuracy     0.996\n",
       "test_precision     0.906\n",
       "train_precision    0.994\n",
       "test_recall        0.964\n",
       "train_recall       1.000\n",
       "test_f1            0.936\n",
       "train_f1           0.996\n",
       "dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_cv_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Train Scores:  Accuracy:  0.9960000000000001 Precision:  0.994 Recall:  1.0  F1:  0.9960000000000001\n",
      "Average Test Scores:  Accuracy:  0.932 Precision:  0.906 Recall:  0.9640000000000001 F1:  0.9359999999999999\n"
     ]
    }
   ],
   "source": [
    "##train and validation set scores\n",
    "print('Average Train Scores: ', 'Accuracy: ', rfc_cv_df.train_accuracy.mean(), 'Precision: ', rfc_cv_df.train_precision.mean(), 'Recall: ', rfc_cv_df.train_recall.mean(), ' F1: ', rfc_cv_df.train_f1.mean())\n",
    "print('Average Test Scores: ', 'Accuracy: ', rfc_cv_df.test_accuracy.mean(), 'Precision: ', rfc_cv_df.test_precision.mean(), 'Recall: ', rfc_cv_df.test_recall.mean(), 'F1: ', rfc_cv_df.test_f1.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "## attempt to generate cross-validated prediction labels to generate a confusion matrix\n",
    "y_cv_predict=cross_val_predict(forest2, X_train,y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0\n",
       "0  1\n",
       "1  0\n",
       "2  1\n",
       "3  0\n",
       "4  0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_df=pd.DataFrame(y_cv_predict)\n",
    "y_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create and print a confusion matrix from cross-validated predictions\n",
    "# print('\\nConfusion Matrix')\n",
    "# print('----------------')\n",
    "# pd.crosstab(y_train, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Test Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=True, random_state=10, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forest2.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oob_df=pd.DataFrame(forest2.oob_decision_function_)\n",
    "# oob_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9142857142857143"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest2.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test=forest2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is :91.42857142857143\n",
      "\n",
      "AUC is :0.91\n",
      "\n",
      "Confusion Matrix\n",
      "----------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted   0   1  All\n",
       "True                  \n",
       "0          74   8   82\n",
       "1           7  86   93\n",
       "All        81  94  175"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = accuracy_score(y_pred_test,y_test) * 100\n",
    "print(\"Accuracy is :{0}\".format(acc))\n",
    "\n",
    "# Check the AUC for predictions\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred_test)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "print(\"\\nAUC is :{0}\".format(round(roc_auc,2)))\n",
    "\n",
    "# Create and print a confusion matrix \n",
    "print('\\nConfusion Matrix')\n",
    "print('----------------')\n",
    "pd.crosstab(y_test, y_pred_test, rownames=['True'], colnames=['Predicted'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances(model):\n",
    "    n_features = X_train.shape[1]\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center') \n",
    "    plt.yticks(np.arange(n_features), X_train.columns.values) \n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "\n",
    "plot_feature_importances(forest2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classification for \"B\" Country Split\n",
    "\n",
    "We want to see how the model performs when presented with data from countries it has never seen. If the performance doesn't drop much, the country has little effect on the life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_country_split_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is :83.66701791359326\n",
      "\n",
      "AUC is :0.84\n",
      "\n",
      "Confusion Matrix\n",
      "----------------\n"
     ]
    },
    {
     "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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>363</td>\n",
       "      <td>113</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>431</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>405</td>\n",
       "      <td>544</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted    0    1  All\n",
       "True                    \n",
       "0          363  113  476\n",
       "1           42  431  473\n",
       "All        405  544  949"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_2 = df_country_split_b['target']\n",
    "X_test_2 = df_country_split_b.drop(['Country', 'target','Adult Mortality','Income composition of resources', 'Year', 'Developing'], axis=1)\n",
    "y_pred_2 = forest2.predict(X_test_2)\n",
    "\n",
    "acc = accuracy_score(y_test_2,y_pred_2) * 100\n",
    "print(\"Accuracy is :{0}\".format(acc))\n",
    "\n",
    "# Check the AUC for predictions\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test_2, y_pred_2)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "print(\"\\nAUC is :{0}\".format(round(roc_auc,2)))\n",
    "\n",
    "# Create and print a confusion matrix \n",
    "print('\\nConfusion Matrix')\n",
    "print('----------------')\n",
    "pd.crosstab(y_test_2, y_pred_2, rownames=['True'], colnames=['Predicted'], margins=True)"
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<<<<<<< HEAD:classifying-life-expectancy-mg-7-7-updated.ipynb
    "### Conclusions:\n",
    "Our model, in addition to life expectancy, is able to draw attention to \n",
    "some modifiable features/areas countries' can focus on regardless of their \n",
    "classification \n",
    "\n",
    "Features of high importance in our classification model included \n",
    "schooling length (in years), vaccination rates (diphtheria, pertussis, tetanus, measles, mumps, rubella, polio), HIV/AID prevalence,  and weight\n"
   ]
=======
    "## Conclusions"
   ]
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>>>>>>> 01940c51d2725f06ef53a3f30719c5128bf074ba:Life_Expectancy_Classification_Project.ipynb
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