Ml_S1_31July2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file into a DataFrame: df\n",
    "df = pd.read_csv(\"gapminder_all.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 139 entries, 0 to 138\n",
      "Data columns (total 11 columns):\n",
      "index              139 non-null int64\n",
      "population         139 non-null float64\n",
      "fertility          139 non-null float64\n",
      "HIV                139 non-null float64\n",
      "CO2                139 non-null float64\n",
      "BMI_male           139 non-null float64\n",
      "GDP                139 non-null float64\n",
      "BMI_female         139 non-null float64\n",
      "life               139 non-null float64\n",
      "child_mortality    139 non-null float64\n",
      "Region             139 non-null object\n",
      "dtypes: float64(9), int64(1), object(1)\n",
      "memory usage: 12.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>index</th>\n",
       "      <th>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34811059.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.328945</td>\n",
       "      <td>24.59620</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>129.9049</td>\n",
       "      <td>75.3</td>\n",
       "      <td>29.5</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19842251.0</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.474353</td>\n",
       "      <td>22.25083</td>\n",
       "      <td>7103.0</td>\n",
       "      <td>130.1247</td>\n",
       "      <td>58.3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40381860.0</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.785170</td>\n",
       "      <td>27.50170</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>118.8915</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2975029.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.804106</td>\n",
       "      <td>25.35542</td>\n",
       "      <td>7383.0</td>\n",
       "      <td>132.8108</td>\n",
       "      <td>72.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>21370348.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.1</td>\n",
       "      <td>18.016313</td>\n",
       "      <td>27.56373</td>\n",
       "      <td>41312.0</td>\n",
       "      <td>117.3755</td>\n",
       "      <td>81.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  population  fertility  HIV        CO2  BMI_male      GDP  \\\n",
       "0      0  34811059.0       2.73  0.1   3.328945  24.59620  12314.0   \n",
       "1      1  19842251.0       6.43  2.0   1.474353  22.25083   7103.0   \n",
       "2      2  40381860.0       2.24  0.5   4.785170  27.50170  14646.0   \n",
       "3      3   2975029.0       1.40  0.1   1.804106  25.35542   7383.0   \n",
       "4      4  21370348.0       1.96  0.1  18.016313  27.56373  41312.0   \n",
       "\n",
       "   BMI_female  life  child_mortality                      Region  \n",
       "0    129.9049  75.3             29.5  Middle East & North Africa  \n",
       "1    130.1247  58.3            192.0          Sub-Saharan Africa  \n",
       "2    118.8915  75.5             15.4                     America  \n",
       "3    132.8108  72.5             20.0       Europe & Central Asia  \n",
       "4    117.3755  81.5              5.2         East Asia & Pacific  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of y before reshaping: (139,)\n",
      "Dimensions of X before reshaping: (139,)\n",
      "Dimensions of y after reshaping: (139, 1)\n",
      "Dimensions of X after reshaping: (139, 1)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create arrays for features and target variable\n",
    "y = df['life'].values\n",
    "X = df['fertility'].values\n",
    "\n",
    "# Print the dimensions of X and y before reshaping\n",
    "print(\"Dimensions of y before reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X before reshaping: {}\".format(X.shape))\n",
    "\n",
    "# Reshape X and y\n",
    "y = y.reshape(-1, 1)\n",
    "X = X.reshape(-1, 1)\n",
    "\n",
    "# Print the dimensions of X and y after reshaping\n",
    "print(\"Dimensions of y after reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X after reshaping: {}\".format(X.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the differences in shape before and after applying the .reshape() method. Getting the feature and target variable arrays into the right format for scikit-learn is an important precursor to model building."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Gapminder data\n",
    "As always, it is important to explore your data before building models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc753467f90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#constructe a heatmap showing the correlation between the different features of the Gapminder dataset\n",
    "\n",
    "sns.heatmap(df.corr(), square=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start to bulid a Linear Regression with one variable \n",
    "which is also called as “univariate linear regression”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6192442167740035\n"
     ]
    },
    {
     "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": [
    "# Import LinearRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "# Create the regressor: reg\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Create the prediction space\n",
    "prediction_space = np.linspace(min(X), max(X)).reshape(-1,1)\n",
    "\n",
    "# Fit the model to the data\n",
    "reg.fit(X, y)\n",
    "\n",
    "# Compute predictions over the prediction space: y_pred\n",
    "y_pred = reg.predict(prediction_space)\n",
    "\n",
    "# Print R^2 \n",
    "print(reg.score(X, y))\n",
    "\n",
    "# Plot regression line\n",
    "plt.scatter(X,y)\n",
    "plt.plot(prediction_space, y_pred, color='black', linewidth=3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fantastic! Notice how the line captures the underlying trend in the data. And the performance is quite decent for this basic regression model with only one feature!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}