GeronBook/Ch1/model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the data\n",
    "country_stats = pd.read_csv('data/country_stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>GDP per capita</th>\n",
       "      <th>Life satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>6100.354</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>8957.894</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Russia</td>\n",
       "      <td>11162.652</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Poland</td>\n",
       "      <td>14901.547</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>17463.284</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  GDP per capita  Life satisfaction\n",
       "0  South Africa        6100.354                4.7\n",
       "1        Turkey        8957.894                5.5\n",
       "2        Russia       11162.652                5.8\n",
       "3        Poland       14901.547                6.1\n",
       "4       Hungary       17463.284                5.6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
    "y = np.c_[country_stats[\"Life satisfaction\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 9,
     "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": [
    "# Visualize the data\n",
    "country_stats.plot(kind='scatter', x = \"GDP per capita\", y = \"Life satisfaction\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we can see a fairly linear association and as such a linear model will fit decently\n",
    "\n",
    "# Choose model\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1db6af81948>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# plot with the line of best fit found by our model\n",
    "\n",
    "plt.plot(X, y_pred, 'r', label='Line of Best Fit')\n",
    "plt.scatter(X, y, color='k', s=3.5)\n",
    "plt.ylabel('Life Satisfaction')\n",
    "plt.xlabel('GDP Per Capita')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'For our line of best fit, the slope is 4.082156611743397e-05 and the intercept is 5.027635740227981'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"For our line of best fit, the slope is {} and the intercept is {}\".format(model.coef_[0][0], model.intercept_[0])"
   ]
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   "source": []
  }
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