03 - Lesson - Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45aef260",
   "metadata": {},
   "source": [
    "# Linear Regression\n",
    "### Goal of Lesson\n",
    "- Learn about linear regression\n",
    "- Understand difference from discrete classifier\n",
    "- Understand it is supervised learning task\n",
    "- Get insight into how similar a linear classifier is to discrete classifier\n",
    "- Hands-on experience with linear regression\n",
    "\n",
    "### What is Linear Regression?\n",
    "- Supervised learning task of learning a mapping from input point to a continuous value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f352a49",
   "metadata": {},
   "source": [
    "### Linear Classifier\n",
    "- $h(x_1, x_2) = w_0 + w_1 x_1 + w_2 x_2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c29408",
   "metadata": {},
   "source": [
    "### Differently\n",
    "- Weight vector $w: (w_0, w_1, w_2)$\n",
    "- Input vector $x: (1, x_1, x_2)$\n",
    "- Function ([dot-product](https://en.wikipedia.org/wiki/Dot_product)): $x\\cdot w: w_0 + w_1 x_1 + w_2 x_2$\n",
    "- $h_w(x) = w\\cdot x$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d057f9f",
   "metadata": {},
   "source": [
    "### In general\n",
    "- Weight vector $w: (w_0, w_1, \\dots, w_n)$\n",
    "- Input vector $x: (1, x_1, \\dots, x_n)$\n",
    "- Function ([dot-product](https://en.wikipedia.org/wiki/Dot_product)): $x\\cdot w: w_0 + w_1 x_1 + \\cdots + w_n x_n$\n",
    "- $h_w(x) = w\\cdot x$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b26214",
   "metadata": {},
   "source": [
    "### Measure accuracy\n",
    "- Before we measured classification (right or wrong)\n",
    "- Now we measure discrete values\n",
    "- We will use r-squared ([wiki](https://en.wikipedia.org/wiki/Coefficient_of_determination))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc79924",
   "metadata": {},
   "source": [
    "> #### Programming Notes:\n",
    "> - Libraries used\n",
    ">     - [**pandas**](https://pandas.pydata.org) - a data analysis and manipulation tool\n",
    ">     - [**numpy**](http://numpy.org) - scientific computing with Python ([Lecture on NumPy](https://youtu.be/BpzpU8_j0-c))\n",
    ">     - [**matplotlib**](http://matplotlib.org) - visualization with Python ([Lecture on **visualization**](https://youtu.be/htIh8YHh4xs))\n",
    ">     - [**sklearn**](https://scikit-learn.org/stable/) - tools for predictive data analysis\n",
    "> - Functionality and concepts used\n",
    ">     - [**CSV**](https://en.wikipedia.org/wiki/Comma-separated_values) file ([Lecture on CSV](https://youtu.be/LEyojSOg4EI))\n",
    ">     - [**read_csv()**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) read a comma-separated values (csv) file into **pandas** DataFrame.\n",
    ">     - [**isnull()**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isnull.html) Detect missing values\n",
    ">     - [**sum()**](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html) Return the sum of the values over the requested axis (can sum number of True-statements).\n",
    ">     - [**corr()**](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html) compute pairwise correlation of columns\n",
    ">     - [**train_test_split**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) from [**sklearn**](https://scikit-learn.org/stable/)\n",
    ">     - [**LinearRegression**](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) to train (fit) the model\n",
    ">     - [**metrics.r2_score**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) to get the accuracy of the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e98e8a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af87bd02",
   "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>Transaction</th>\n",
       "      <th>House age</th>\n",
       "      <th>Distance to MRT station</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>House unit price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.583</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012.833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction  House age  Distance to MRT station  \\\n",
       "0     2012.917       32.0                 84.87882   \n",
       "1     2012.917       19.5                306.59470   \n",
       "2     2013.583       13.3                561.98450   \n",
       "3     2013.500       13.3                561.98450   \n",
       "4     2012.833        5.0                390.56840   \n",
       "\n",
       "   Number of convenience stores  Latitude  Longitude  House unit price  \n",
       "0                            10  24.98298  121.54024              37.9  \n",
       "1                             9  24.98034  121.53951              42.2  \n",
       "2                             5  24.98746  121.54391              47.3  \n",
       "3                             5  24.98746  121.54391              54.8  \n",
       "4                             5  24.97937  121.54245              43.1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('files/house_prices.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4e00c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff7c5c60850>"
      ]
     },
     "execution_count": 3,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(x=data['Longitude'], y=data['House unit price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ba4f627",
   "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>Transaction</th>\n",
       "      <th>House age</th>\n",
       "      <th>Distance to MRT station</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>House unit price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Transaction</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017549</td>\n",
       "      <td>0.060880</td>\n",
       "      <td>0.009635</td>\n",
       "      <td>0.035058</td>\n",
       "      <td>-0.041082</td>\n",
       "      <td>0.087491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House age</th>\n",
       "      <td>0.017549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025622</td>\n",
       "      <td>0.049593</td>\n",
       "      <td>0.054420</td>\n",
       "      <td>-0.048520</td>\n",
       "      <td>-0.210567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance to MRT station</th>\n",
       "      <td>0.060880</td>\n",
       "      <td>0.025622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.602519</td>\n",
       "      <td>-0.591067</td>\n",
       "      <td>-0.806317</td>\n",
       "      <td>-0.673613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <td>0.009635</td>\n",
       "      <td>0.049593</td>\n",
       "      <td>-0.602519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.444143</td>\n",
       "      <td>0.449099</td>\n",
       "      <td>0.571005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>0.035058</td>\n",
       "      <td>0.054420</td>\n",
       "      <td>-0.591067</td>\n",
       "      <td>0.444143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412924</td>\n",
       "      <td>0.546307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>-0.041082</td>\n",
       "      <td>-0.048520</td>\n",
       "      <td>-0.806317</td>\n",
       "      <td>0.449099</td>\n",
       "      <td>0.412924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.523287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House unit price</th>\n",
       "      <td>0.087491</td>\n",
       "      <td>-0.210567</td>\n",
       "      <td>-0.673613</td>\n",
       "      <td>0.571005</td>\n",
       "      <td>0.546307</td>\n",
       "      <td>0.523287</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Transaction  House age  Distance to MRT station  \\\n",
       "Transaction                      1.000000   0.017549                 0.060880   \n",
       "House age                        0.017549   1.000000                 0.025622   \n",
       "Distance to MRT station          0.060880   0.025622                 1.000000   \n",
       "Number of convenience stores     0.009635   0.049593                -0.602519   \n",
       "Latitude                         0.035058   0.054420                -0.591067   \n",
       "Longitude                       -0.041082  -0.048520                -0.806317   \n",
       "House unit price                 0.087491  -0.210567                -0.673613   \n",
       "\n",
       "                              Number of convenience stores  Latitude  \\\n",
       "Transaction                                       0.009635  0.035058   \n",
       "House age                                         0.049593  0.054420   \n",
       "Distance to MRT station                          -0.602519 -0.591067   \n",
       "Number of convenience stores                      1.000000  0.444143   \n",
       "Latitude                                          0.444143  1.000000   \n",
       "Longitude                                         0.449099  0.412924   \n",
       "House unit price                                  0.571005  0.546307   \n",
       "\n",
       "                              Longitude  House unit price  \n",
       "Transaction                   -0.041082          0.087491  \n",
       "House age                     -0.048520         -0.210567  \n",
       "Distance to MRT station       -0.806317         -0.673613  \n",
       "Number of convenience stores   0.449099          0.571005  \n",
       "Latitude                       0.412924          0.546307  \n",
       "Longitude                      1.000000          0.523287  \n",
       "House unit price               0.523287          1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e16d600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transaction                     0\n",
       "House age                       0\n",
       "Distance to MRT station         0\n",
       "Number of convenience stores    0\n",
       "Latitude                        0\n",
       "Longitude                       0\n",
       "House unit price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ccc6927",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca90302a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "414"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad054bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ec3079b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin = LinearRegression()\n",
    "lin.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f9c2fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lin.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8899b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6800088002014439"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a38c8f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.67857744e+00, -2.58421926e-01, -4.28607244e-03,  1.21784177e+00,\n",
       "        2.23558483e+02, -1.45002564e+00])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34e32a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff7c6574fa0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(x=X['Longitude'], y=X['Latitude'], c=data['House unit price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f029cb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}