DSP303x_01_EN_1_Starter_Code.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xeq3pMIRIKnK"
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   "source": [
    "<h1 align=center><font size = 10> Linear Regression </font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LjHFM2kAIKns"
   },
   "source": [
    "In this notebook, we try to practice the linear regression algorithm that we learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library and apply the following algorithm, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "Lets first load required libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "vgdDORwkIKnx"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "matplotlib.style.use('ggplot') # Look Pretty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0i6ipCJvIKn_"
   },
   "source": [
    "### A Convenience Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TWlCdM1HIKoB"
   },
   "source": [
    "This convenience method will take care of plotting your test observations, comparing them to the regression line, and displaying the R2 coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "L8R7I3gfIKoC"
   },
   "outputs": [],
   "source": [
    "def drawLine(model, X_test, y_test, title, R2):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.scatter(X_test, y_test, c='g', marker='o')\n",
    "    ax.plot(X_test, model.predict(X_test), color='orange', linewidth=1, alpha=0.7)\n",
    "\n",
    "    title += \" R2: \" + str(R2)\n",
    "    ax.set_title(title)\n",
    "    print(title)\n",
    "    print(\"Intercept(s): \", model.intercept_)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "A6RyHyaoIKoK"
   },
   "outputs": [],
   "source": [
    "def drawPlane(model, X_test, y_test, title, R2):\n",
    "    # This convenience method will take care of plotting your\n",
    "    # test observations, comparing them to the regression plane,\n",
    "    # and displaying the R2 coefficient\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    ax.set_zlabel('prediction')\n",
    "\n",
    "    \n",
    "    # You might have passed in a DataFrame, a Series (slice),\n",
    "    # an NDArray, or a Python List... so let's keep it simple:\n",
    "    X_test = np.array(X_test)\n",
    "    col1 = X_test[:,0]\n",
    "    col2 = X_test[:,1]\n",
    "\n",
    "    \n",
    "    # Set up a Grid. We could have predicted on the actual\n",
    "    # col1, col2 values directly; but that would have generated\n",
    "    # a mesh with WAY too fine a grid, which would have detracted\n",
    "    # from the visualization\n",
    "    x_min, x_max = col1.min(), col1.max()\n",
    "    y_min, y_max = col2.min(), col2.max()\n",
    "    x = np.arange(x_min, x_max, (x_max-x_min) / 10)\n",
    "    y = np.arange(y_min, y_max, (y_max-y_min) / 10)\n",
    "    x, y = np.meshgrid(x, y)\n",
    "\n",
    "    \n",
    "    # Predict based on possible input values that span the domain\n",
    "    # of the x and y inputs:\n",
    "    z = model.predict(  np.c_[x.ravel(), y.ravel()]  )\n",
    "    z = z.reshape(x.shape)\n",
    "\n",
    "    \n",
    "    ax.scatter(col1, col2, y_test, c='g', marker='o')\n",
    "    ax.plot_wireframe(x, y, z, color='orange', alpha=0.7)\n",
    "\n",
    "    title += \" R2: \" + str(R2)\n",
    "    ax.set_title(title)\n",
    "    print(title)\n",
    "    print(\"Intercept(s): \", model.intercept_)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Li4XOmpIKod"
   },
   "source": [
    "# Mandatory part (85%  of the total score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "id": "zT7hZrQ9IKog",
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## About dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "pWGBWLfnIKoi"
   },
   "source": [
    "This project relates to the College data set, which can be found in the file College.csv in the data folder. It contains a number of variables for 777 different universities and colleges in the US. The variables are:\n",
    "\n",
    "**Private** : Public/private indicator\n",
    "\n",
    "**Apps** : Number of applications received\n",
    "\n",
    "**Accept** : Number of applicants accepted\n",
    "\n",
    "**Enroll** : Number of new students enrolled\n",
    "\n",
    "**Top10perc** : New students from the top 10% of high school class\n",
    "\n",
    "**Top25perc** : New students from the top 25% of high school class\n",
    "\n",
    "**F.Undergrad** : Number of full-time undergraduates\n",
    "\n",
    "**P.Undergrad** : Number of part-time undergraduates\n",
    "\n",
    "**Outstate** : Out-of-state tuition\n",
    "\n",
    "**Room.Board** : Room and board costs\n",
    "\n",
    "**Books** : Estimated book costs\n",
    "\n",
    "**Personal** : Estimated personal spending\n",
    "\n",
    "**PhD** : Percent of faculty with Ph.D.’s\n",
    "\n",
    "**Terminal** : Percent of faculty with a terminal degree\n",
    "\n",
    "**S.F.Ratio** : Student/faculty ratio\n",
    "\n",
    "**perc.alumni** : Percent of alumni who donate\n",
    "\n",
    "**Expend** : Instructional expenditure per student\n",
    "\n",
    "**Grad.Rate** : Graduation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c35zRlIJIKok"
   },
   "source": [
    "## The problem:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "THBnqaCIIKom"
   },
   "source": [
    "The type of dataset and problem is a classic regression. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify the **number of people accepted**.\n",
    "\n",
    "To solve the problem we will have to analyze the data, do any required transformation and normalization, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate until we find the most performant for our type of dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v9hG45wDIKoo"
   },
   "source": [
    "First, as is your habit, inspect your dataset in a text editor, or spread sheet application. The first thing you should notice is that the first column is both unique (the name of each) college, as well as unlabeled. This is a HINT that it must be the index column. If you do not indicate to Pandas that you already have an index column, it'll create one for you, which would be undesirable since you already have one. \n",
    "\n",
    "**1. Review the `.read_csv()` documentation and discern how to load up a dataframe while indicating which existing column is to be taken as an index. Then, load up the College dataset into a variable called `X`:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "qK_sefriIKoq"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Worcester State College</td>\n",
       "      <td>No</td>\n",
       "      <td>2197</td>\n",
       "      <td>1515</td>\n",
       "      <td>543</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>3089</td>\n",
       "      <td>2029</td>\n",
       "      <td>6797</td>\n",
       "      <td>3900</td>\n",
       "      <td>500</td>\n",
       "      <td>1200</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4469</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>Xavier University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1959</td>\n",
       "      <td>1805</td>\n",
       "      <td>695</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>2849</td>\n",
       "      <td>1107</td>\n",
       "      <td>11520</td>\n",
       "      <td>4960</td>\n",
       "      <td>600</td>\n",
       "      <td>1250</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>13.3</td>\n",
       "      <td>31</td>\n",
       "      <td>9189</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>Xavier University of Louisiana</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2097</td>\n",
       "      <td>1915</td>\n",
       "      <td>695</td>\n",
       "      <td>34</td>\n",
       "      <td>61</td>\n",
       "      <td>2793</td>\n",
       "      <td>166</td>\n",
       "      <td>6900</td>\n",
       "      <td>4200</td>\n",
       "      <td>617</td>\n",
       "      <td>781</td>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20</td>\n",
       "      <td>8323</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>Yale University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10705</td>\n",
       "      <td>2453</td>\n",
       "      <td>1317</td>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>5217</td>\n",
       "      <td>83</td>\n",
       "      <td>19840</td>\n",
       "      <td>6510</td>\n",
       "      <td>630</td>\n",
       "      <td>2115</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>5.8</td>\n",
       "      <td>49</td>\n",
       "      <td>40386</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>York College of Pennsylvania</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2989</td>\n",
       "      <td>1855</td>\n",
       "      <td>691</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>2988</td>\n",
       "      <td>1726</td>\n",
       "      <td>4990</td>\n",
       "      <td>3560</td>\n",
       "      <td>500</td>\n",
       "      <td>1250</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>18.1</td>\n",
       "      <td>28</td>\n",
       "      <td>4509</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Unnamed: 0 Private   Apps  Accept  Enroll  Top10perc  \\\n",
       "0      Abilene Christian University     Yes   1660    1232     721         23   \n",
       "1                Adelphi University     Yes   2186    1924     512         16   \n",
       "2                    Adrian College     Yes   1428    1097     336         22   \n",
       "3               Agnes Scott College     Yes    417     349     137         60   \n",
       "4         Alaska Pacific University     Yes    193     146      55         16   \n",
       "..                              ...     ...    ...     ...     ...        ...   \n",
       "772         Worcester State College      No   2197    1515     543          4   \n",
       "773               Xavier University     Yes   1959    1805     695         24   \n",
       "774  Xavier University of Louisiana     Yes   2097    1915     695         34   \n",
       "775                 Yale University     Yes  10705    2453    1317         95   \n",
       "776    York College of Pennsylvania     Yes   2989    1855     691         28   \n",
       "\n",
       "     Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  \\\n",
       "0           52         2885          537      7440        3300    450   \n",
       "1           29         2683         1227     12280        6450    750   \n",
       "2           50         1036           99     11250        3750    400   \n",
       "3           89          510           63     12960        5450    450   \n",
       "4           44          249          869      7560        4120    800   \n",
       "..         ...          ...          ...       ...         ...    ...   \n",
       "772         26         3089         2029      6797        3900    500   \n",
       "773         47         2849         1107     11520        4960    600   \n",
       "774         61         2793          166      6900        4200    617   \n",
       "775         99         5217           83     19840        6510    630   \n",
       "776         63         2988         1726      4990        3560    500   \n",
       "\n",
       "     Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0        2200   70        78       18.1           12    7041         60  \n",
       "1        1500   29        30       12.2           16   10527         56  \n",
       "2        1165   53        66       12.9           30    8735         54  \n",
       "3         875   92        97        7.7           37   19016         59  \n",
       "4        1500   76        72       11.9            2   10922         15  \n",
       "..        ...  ...       ...        ...          ...     ...        ...  \n",
       "772      1200   60        60       21.0           14    4469         40  \n",
       "773      1250   73        75       13.3           31    9189         83  \n",
       "774       781   67        75       14.4           20    8323         49  \n",
       "775      2115   96        96        5.8           49   40386         99  \n",
       "776      1250   75        75       18.1           28    4509         99  \n",
       "\n",
       "[777 rows x 19 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X = pd.read_csv('College.csv')\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yk8IBfDRIKo3"
   },
   "source": [
    "This line isn't necessary for your purposes, but we'd just like to show you an additional way to encode features directly. The `.map()` method is like `.apply()`, but instead of taking in a lambda/function, you simply provide a mapping of keys:values. If you decide to embark on the \"Data Scientist Challenge\", this line of code will save you the trouble of converting it through other means.\n",
    "\n",
    "**2. Transform the Private column to {'Yes':1, 'No':0} using map function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "paEmSbI5IKo5"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>1</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>1</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>1</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>1</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Worcester State College</td>\n",
       "      <td>0</td>\n",
       "      <td>2197</td>\n",
       "      <td>1515</td>\n",
       "      <td>543</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>3089</td>\n",
       "      <td>2029</td>\n",
       "      <td>6797</td>\n",
       "      <td>3900</td>\n",
       "      <td>500</td>\n",
       "      <td>1200</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4469</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>Xavier University</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1805</td>\n",
       "      <td>695</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>2849</td>\n",
       "      <td>1107</td>\n",
       "      <td>11520</td>\n",
       "      <td>4960</td>\n",
       "      <td>600</td>\n",
       "      <td>1250</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>13.3</td>\n",
       "      <td>31</td>\n",
       "      <td>9189</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>Xavier University of Louisiana</td>\n",
       "      <td>1</td>\n",
       "      <td>2097</td>\n",
       "      <td>1915</td>\n",
       "      <td>695</td>\n",
       "      <td>34</td>\n",
       "      <td>61</td>\n",
       "      <td>2793</td>\n",
       "      <td>166</td>\n",
       "      <td>6900</td>\n",
       "      <td>4200</td>\n",
       "      <td>617</td>\n",
       "      <td>781</td>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20</td>\n",
       "      <td>8323</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>Yale University</td>\n",
       "      <td>1</td>\n",
       "      <td>10705</td>\n",
       "      <td>2453</td>\n",
       "      <td>1317</td>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>5217</td>\n",
       "      <td>83</td>\n",
       "      <td>19840</td>\n",
       "      <td>6510</td>\n",
       "      <td>630</td>\n",
       "      <td>2115</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>5.8</td>\n",
       "      <td>49</td>\n",
       "      <td>40386</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>York College of Pennsylvania</td>\n",
       "      <td>1</td>\n",
       "      <td>2989</td>\n",
       "      <td>1855</td>\n",
       "      <td>691</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>2988</td>\n",
       "      <td>1726</td>\n",
       "      <td>4990</td>\n",
       "      <td>3560</td>\n",
       "      <td>500</td>\n",
       "      <td>1250</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>18.1</td>\n",
       "      <td>28</td>\n",
       "      <td>4509</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Unnamed: 0  Private   Apps  Accept  Enroll  \\\n",
       "0      Abilene Christian University        1   1660    1232     721   \n",
       "1                Adelphi University        1   2186    1924     512   \n",
       "2                    Adrian College        1   1428    1097     336   \n",
       "3               Agnes Scott College        1    417     349     137   \n",
       "4         Alaska Pacific University        1    193     146      55   \n",
       "..                              ...      ...    ...     ...     ...   \n",
       "772         Worcester State College        0   2197    1515     543   \n",
       "773               Xavier University        1   1959    1805     695   \n",
       "774  Xavier University of Louisiana        1   2097    1915     695   \n",
       "775                 Yale University        1  10705    2453    1317   \n",
       "776    York College of Pennsylvania        1   2989    1855     691   \n",
       "\n",
       "     Top10perc  Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  \\\n",
       "0           23         52         2885          537      7440        3300   \n",
       "1           16         29         2683         1227     12280        6450   \n",
       "2           22         50         1036           99     11250        3750   \n",
       "3           60         89          510           63     12960        5450   \n",
       "4           16         44          249          869      7560        4120   \n",
       "..         ...        ...          ...          ...       ...         ...   \n",
       "772          4         26         3089         2029      6797        3900   \n",
       "773         24         47         2849         1107     11520        4960   \n",
       "774         34         61         2793          166      6900        4200   \n",
       "775         95         99         5217           83     19840        6510   \n",
       "776         28         63         2988         1726      4990        3560   \n",
       "\n",
       "     Books  Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0      450      2200   70        78       18.1           12    7041         60  \n",
       "1      750      1500   29        30       12.2           16   10527         56  \n",
       "2      400      1165   53        66       12.9           30    8735         54  \n",
       "3      450       875   92        97        7.7           37   19016         59  \n",
       "4      800      1500   76        72       11.9            2   10922         15  \n",
       "..     ...       ...  ...       ...        ...          ...     ...        ...  \n",
       "772    500      1200   60        60       21.0           14    4469         40  \n",
       "773    600      1250   73        75       13.3           31    9189         83  \n",
       "774    617       781   67        75       14.4           20    8323         49  \n",
       "775    630      2115   96        96        5.8           49   40386         99  \n",
       "776    500      1250   75        75       18.1           28    4509         99  \n",
       "\n",
       "[777 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X['Private'] = X['Private'].map({'Yes': 1, 'No': 0})\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fGiM_AlHIKpJ"
   },
   "source": [
    "**3. Create your linear regression model here and store it in a variable called `model`.**\n",
    "\n",
    "Don't actually train or do anything else with it yet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "XStZviNaIKpN"
   },
   "outputs": [],
   "source": [
    "#TO DO:\n",
    "from sklearn import linear_model\n",
    "model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q2ybf3CXIKpk"
   },
   "source": [
    "The first relationship we're interested in is the number of accepted students, as a function of the amount charged for room and board.\n",
    "\n",
    "**4. Using indexing, create two slices (series). One will just store the room and board column (X_rb), the other will store the accepted students' column (y_acc).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "GtPhwgEFIKpn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3300\n",
       "1      6450\n",
       "2      3750\n",
       "3      5450\n",
       "4      4120\n",
       "       ... \n",
       "772    3900\n",
       "773    4960\n",
       "774    4200\n",
       "775    6510\n",
       "776    3560\n",
       "Name: Room.Board, Length: 777, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_rb = X['Room.Board']\n",
    "X_rb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1232\n",
       "1      1924\n",
       "2      1097\n",
       "3       349\n",
       "4       146\n",
       "       ... \n",
       "772    1515\n",
       "773    1805\n",
       "774    1915\n",
       "775    2453\n",
       "776    1855\n",
       "Name: Accept, Length: 777, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_acc = X['Accept']\n",
    "y_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OhXU1-91IKq3"
   },
   "source": [
    "**5. Then use train_test_split to cut your data up into `X_train`, `X_test`, `y_train`, `y_test`, with a `test_size` of 30% and a random_state of 7**\n",
    "\n",
    "**Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "fc_Pt4JHIKq5"
   },
   "outputs": [],
   "source": [
    "#TO DO:\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_rb.values.reshape(-1,1), y_acc.values.reshape(-1,1), test_size=0.3, random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pLTLlYTMIKrC"
   },
   "source": [
    "**6. Fit and score your model appropriately. Store the score in the `score` variable and print it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "KxcRHlx8IKrJ",
    "outputId": "52c2b01e-840b-4fa6-faa5-2492a813198b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.003\n"
     ]
    }
   ],
   "source": [
    "#TO DO:\n",
    "model.fit (X_train, y_train)\n",
    "score = model.score(X_test, y_test)\n",
    "print('score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K6NXfOvsIKrY"
   },
   "source": [
    "**7. Draw the model, X_test, y_test, score(R2 score) using the drawLine function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "YWcNJfE9IKra",
    "outputId": "34d687d6-e3c8-4b4e-a9da-92729fa22020"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score R2: -0.0026669864145500988\n",
      "Intercept(s):  [1340.61956519]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TO DO:\n",
    "drawLine(model, X_test, y_test, 'Score', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osgjDaMcIKrg"
   },
   "source": [
    "**8. Duplicate the process above (5,6, 7); this time, model the number of accepted students, as a function of the number of enrolled students per college.**\n",
    "\n",
    "Don't forget to split data with random_state = 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       721\n",
       "1       512\n",
       "2       336\n",
       "3       137\n",
       "4        55\n",
       "       ... \n",
       "772     543\n",
       "773     695\n",
       "774     695\n",
       "775    1317\n",
       "776     691\n",
       "Name: Enroll, Length: 777, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_enl = X['Enroll']\n",
    "X_enl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "SDmjrQvPIKrh",
    "outputId": "72bccb71-3479-417f-c98b-b363a6d523de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.858\n",
      "Score R2: 0.8578204867356156\n",
      "Intercept(s):  [151.72611242]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_enl.values.reshape(-1,1), y_acc.values.reshape(-1,1), test_size=0.3, random_state=7)\n",
    "model.fit (X_train, y_train)\n",
    "score = model.score(X_test, y_test)\n",
    "print('score: %.3f' % score)\n",
    "drawLine(model, X_test, y_test, 'Score', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "kGYb64A2IKrs"
   },
   "source": [
    "**9. Duplicate the process above (5, 6, 7); this time, model the number of accepted students, as function of the number of failed undergraduate students per college.**\n",
    "\n",
    "Don't forget to split data with random_state = 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2885\n",
       "1      2683\n",
       "2      1036\n",
       "3       510\n",
       "4       249\n",
       "       ... \n",
       "772    3089\n",
       "773    2849\n",
       "774    2793\n",
       "775    5217\n",
       "776    2988\n",
       "Name: F.Undergrad, Length: 777, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_FU = X['F.Undergrad']\n",
    "X_FU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "xgTP_pBHIKrw",
    "outputId": "ab24aebe-bae0-4937-eece-51f305be49a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.778\n",
      "Score R2: 0.7779917973754006\n",
      "Intercept(s):  [380.9429105]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_FU.values.reshape(-1,1), y_acc.values.reshape(-1,1), test_size=0.3, random_state=7)\n",
    "model.fit (X_train, y_train)\n",
    "score = model.score(X_test, y_test)\n",
    "print('score: %.3f' % score)\n",
    "drawLine(model, X_test, y_test, 'Score', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R5f6ejNMIKr4"
   },
   "source": [
    "**10. Duplicate the process above (5, 6). This time is going to be a bit more complicated. Instead of modeling one feature as a function of another, you will attempt to do multivariate linear regression to model one feature as a function of TWO other features. Let's print out the model score.**\n",
    "\n",
    "Model the number of accepted students as a function of the amount charged for room and board _and_ the number of enrolled students. To do this, instead of creating a regular slice for a single-feature input, simply create a slice that contains both columns you wish to use as inputs. Your training labels will remain a single slice.\n",
    "    \n",
    "Don't forget to split data with random_state = 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "qBjcmfi8IKr7",
    "outputId": "e9e748a3-d40f-4ab6-af37-47ea6ce1ff3e"
   },
   "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>Room.Board</th>\n",
       "      <th>Enroll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3300</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6450</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3750</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5450</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>3900</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>4960</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>4200</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>6510</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>3560</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Room.Board  Enroll\n",
       "0          3300     721\n",
       "1          6450     512\n",
       "2          3750     336\n",
       "3          5450     137\n",
       "4          4120      55\n",
       "..          ...     ...\n",
       "772        3900     543\n",
       "773        4960     695\n",
       "774        4200     695\n",
       "775        6510    1317\n",
       "776        3560     691\n",
       "\n",
       "[777 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_rb_and_enl = X[['Room.Board','Enroll']]\n",
    "X_rb_and_enl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3300,  721],\n",
       "       [6450,  512],\n",
       "       [3750,  336],\n",
       "       ...,\n",
       "       [4200,  695],\n",
       "       [6510, 1317],\n",
       "       [3560,  691]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_rb_and_enl = np.asanyarray(X_rb_and_enl)\n",
    "X_rb_and_enl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.876\n"
     ]
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_rb_and_enl, y_acc.values.reshape(-1,1), test_size=0.3, random_state=7)\n",
    "model.fit (X_train, y_train)\n",
    "score = model.score(X_test, y_test)\n",
    "print('score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score R2: 0.8764371774214047\n",
      "Intercept(s):  [-1176.62848249]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawPlane(model, X_test, y_test, 'Score', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oXuVXeO_IKsG"
   },
   "source": [
    "# Advanced part (15%  of the total score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bXOb7jlqIKsO"
   },
   "source": [
    "**11. Duplicate the process above (5,6), try to use your own features set and your own model with modified parameters.**\n",
    "\n",
    "    Requirement: Score > 0.875\n",
    "    \n",
    "    Don't forget to split data with random_state = 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "1xwrjqo1IKsQ",
    "outputId": "a62ca6b5-4f28-44ea-ac51-d92f053849f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.898\n"
     ]
    }
   ],
   "source": [
    "#TO DO:\n",
    "X_set = X[['Apps','Enroll']]\n",
    "X_set = np.asanyarray(X_set)\n",
    "#TO DO:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_set, y_acc.values.reshape(-1,1), test_size=0.3, random_state=7)\n",
    "model.fit (X_train, y_train)\n",
    "score = model.score(X_test, y_test)\n",
    "print('score: %.3f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score R2: 0.8984748010217437\n",
      "Intercept(s):  [41.75950065]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawPlane(model, X_test, y_test, 'Score', score)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "name": "DSP303x_01_EN_1_Starter_Code.ipynb",
   "provenance": []
  },
  "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": "3.8.8"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "58px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}