College.ipynb

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      "name": "College.ipynb",
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      "authorship_tag": "ABX9TyNKP+ZrPOKX/Xolu4rvf+6R",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/grbruns/cst383/blob/master/College.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# Exploration and prediction with US college data\n",
        "\n",
        "### Glenn Bruns, CSUMB\n",
        "\n",
        "The 1995 issue of US News and World Report contains data about 777 US Colleges.  The data set consists of 18 variables that describe, for each college, whether it is public or private, the number of applications received, the number of fulltime undergrads, etc.  We will explore and visualize this data set and then build a predictive model.\n",
        "\n",
        "The data set is available on [this Kaggle page](https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data) and also through a github link a provide below.\n",
        "\n",
        "The text cells in this notebook are formatted using \"markdown\".  See the [Markdown Guide](https://colab.research.google.com/notebooks/markdown_guide.ipynb) for basic information about Colaboratory markdown.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0jXVTpk5utA",
        "colab_type": "text"
      },
      "source": [
        "First we import some of the Python packages we will use.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FXebNIh53WJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12QFZc9F6G54",
        "colab_type": "text"
      },
      "source": [
        "Set the graphics style."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2idtP6X6KmB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sns.set()\n",
        "sns.set_context('notebook')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZQvudmq6q15",
        "colab_type": "text"
      },
      "source": [
        "## Read the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uACrWELtIDPn",
        "colab_type": "text"
      },
      "source": [
        "Using the Pandas function 'read_csv', you can read data using a web link."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNYq5GdJ6vtJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/grbruns/cst383/master/College.csv', index_col=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taUXUUMVHyfI",
        "colab_type": "text"
      },
      "source": [
        "The 'index_col=0' option says that the college name (which is column 0 of the data), should be used as the index into the data set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE0oGioL6cLR",
        "colab_type": "text"
      },
      "source": [
        "## Preliminary exploration\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8erc381GC5g",
        "colab_type": "text"
      },
      "source": [
        "As is usually the case, the data has the form of a table, with each row an *example*, and each column a *feature*. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PLuR8QV6oAR",
        "colab_type": "code",
        "outputId": "ccccbd21-9b0d-4daf-95ca-2a425de524a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 777 entries, Abilene Christian University to York College of Pennsylvania\n",
            "Data columns (total 18 columns):\n",
            "Private        777 non-null object\n",
            "Apps           777 non-null int64\n",
            "Accept         777 non-null int64\n",
            "Enroll         777 non-null int64\n",
            "Top10perc      777 non-null int64\n",
            "Top25perc      777 non-null int64\n",
            "F.Undergrad    777 non-null int64\n",
            "P.Undergrad    777 non-null int64\n",
            "Outstate       777 non-null int64\n",
            "Room.Board     777 non-null int64\n",
            "Books          777 non-null int64\n",
            "Personal       777 non-null int64\n",
            "PhD            777 non-null int64\n",
            "Terminal       777 non-null int64\n",
            "S.F.Ratio      777 non-null float64\n",
            "perc.alumni    777 non-null int64\n",
            "Expend         777 non-null int64\n",
            "Grad.Rate      777 non-null int64\n",
            "dtypes: float64(1), int64(16), object(1)\n",
            "memory usage: 115.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Z494wYwGbdv",
        "colab_type": "text"
      },
      "source": [
        "We see that this data set consists of 777 examples.  Each example consists of 18 features.  Most of the features have integer values.  The student/faculty ratio value is a floating point number.  The documentation supplied with the data set defines the features as follows:\n",
        "\n",
        "Feature | Description\n",
        ":------ | :----------\n",
        "`Private` | A factor with levels No and Yes indicating private or public university\n",
        "`Apps` | Number of applications received\n",
        "`Accept` | Number of applications accepted\n",
        "`Enroll` | Number of new students enrolled\n",
        "`Top10perc` | Pct. new students from top 10% of H.S. class\n",
        "`Top25perc` | Pct. new students from top 25% of H.S. class\n",
        "`F.Undergrad` | Number of fulltime undergraduates\n",
        "`P.Undergrad` | Number of parttime undergraduates\n",
        "`Outstate` | Out-of-state tuition\n",
        "`Room.Board` | Room and board costs\n",
        "`Books` | Estimated book costs\n",
        "`Personal` | Estimated personal spending\n",
        "`PhD` | Pct. of faculty with Ph.D.’s\n",
        "`Terminal` | Pct. of faculty with terminal degree\n",
        "`S.F.Ratio` | Student/faculty ratio\n",
        "`perc.alumni` | Pct. alumni who donate\n",
        "`Expend` | Instructional expenditure per student\n",
        "`Grad.Rate` | Graduation rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0VFSYEYHkBY",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the first few rows of the data set."
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>Abilene Christian University</th>\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>Adelphi University</th>\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>Adrian College</th>\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>Agnes Scott College</th>\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>Alaska Pacific University</th>\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Private  Apps  ...  Expend  Grad.Rate\n",
              "Abilene Christian University     Yes  1660  ...    7041         60\n",
              "Adelphi University               Yes  2186  ...   10527         56\n",
              "Adrian College                   Yes  1428  ...    8735         54\n",
              "Agnes Scott College              Yes   417  ...   19016         59\n",
              "Alaska Pacific University        Yes   193  ...   10922         15\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIRBNMrrn_Hs",
        "colab_type": "text"
      },
      "source": [
        "How many public and private colleges are represented in this data set?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gLDfp8pHJ-u",
        "colab_type": "code",
        "outputId": "915cb8b7-be97-4a8d-a088-40442dc46aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "df['Private'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    565\n",
              "No     212\n",
              "Name: Private, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIyb7a0dHQa_",
        "colab_type": "text"
      },
      "source": [
        "Is the data set complete?  None and nan values would suggest data is missing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StPFjK7Uusau",
        "colab_type": "code",
        "outputId": "b6efa12e-c35a-4aff-9bf1-f25e9acefd71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Private        0\n",
              "Apps           0\n",
              "Accept         0\n",
              "Enroll         0\n",
              "Top10perc      0\n",
              "Top25perc      0\n",
              "F.Undergrad    0\n",
              "P.Undergrad    0\n",
              "Outstate       0\n",
              "Room.Board     0\n",
              "Books          0\n",
              "Personal       0\n",
              "PhD            0\n",
              "Terminal       0\n",
              "S.F.Ratio      0\n",
              "perc.alumni    0\n",
              "Expend         0\n",
              "Grad.Rate      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJCrEFEIuxlS",
        "colab_type": "text"
      },
      "source": [
        "The output indicates that there is no obviously missing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6-dMC7Yu9Da",
        "colab_type": "text"
      },
      "source": [
        "## Data Exploration and Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-Fb17W2vDXL",
        "colab_type": "text"
      },
      "source": [
        "Let's start diving into the data to answer some basic questions.  First, how big are the colleges?  A *histogram* shows use the distribution of a single numeric variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uwd92ti4vhhJ",
        "colab_type": "code",
        "outputId": "2f579d96-afe1-4c42-b397-5d96c2ca756b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))           # set the figure size\n",
        "plt.hist(df['F.Undergrad'])         # create a histogram using matplotlib\n",
        "plt.title('Histogram of number of full-time undergrads');    # set the plot title"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIA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\nAgAArCJGAACAVcQIAACw6v8A0eq7RwqrId0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaSel--IwgpC",
        "colab_type": "text"
      },
      "source": [
        "This plot shows that there are many small colleges, and a few very large colleges.  The bar on the left represents colleges that have less than about 3000 undergraduates.  The height of the bar is about 550, so about of the colleges in the data set have less than about 3000 undergrads.  The next bar is for colleges with about 3000 to 6000 undergrads.  As you can see from the height of the bar, there are about 100 colleges of this size."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOgspMHzzP1b",
        "colab_type": "text"
      },
      "source": [
        "The histogram of college sizes can also be created with the Seaborn visualization package."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnIEXz2Yv7GB",
        "colab_type": "code",
        "outputId": "f16901bc-15aa-4063-9d94-a8356c69e1da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))           # set the figure size\n",
        "sns.distplot(df['F.Undergrad'])     # create a histogram with Seaborn\n",
        "plt.title('Histogram of number of full-time undergrads');    # set the plot title"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y1Hkvv/xy/umf/okf/OAH1NfXU1BQwE9+8pN+v5f+lJSU8Prrr3PzzTczZswYvvzlL/Pz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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niVyBgwR0BKh",
        "colab_type": "text"
      },
      "source": [
        "A nice feature of using Seaborn for a histogram is that is automatically adds a density plot, which is the smoothed line superimposed on the histogram.  The bars in this plot represent a narrower range of college sizes, which helps in getting a clearer view of the size of the smaller colleges."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr-FKOYQ0oAB",
        "colab_type": "text"
      },
      "source": [
        "Let's use a histogram again to get an idea of the distribution of college tuition.  The values in the 'Outstate' column of the data represent the out-of-state yearly tuition for each college."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3sZUGk7zmTA",
        "colab_type": "code",
        "outputId": "03737924-a951-44bc-c08b-a5554e5fa098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))        # set the figure size\n",
        "sns.distplot(df['Outstate'])     # create a histogram with Seaborn\n",
        "plt.title('Histogram of out-of-state tuition')    # set the plot title\n",
        "plt.xlabel('Out-of-state tuition ($)');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7Gz5/ENlpfOQ8EskEAWXFJnx6sR8eX0DqcohIAgw7RDPU2eeEIABmY6LUpdA4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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBpBEc8I1szK",
        "colab_type": "text"
      },
      "source": [
        "We find that in 1995 a typical college tuition was about $10,000/year."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LROFlAYY2H6K",
        "colab_type": "text"
      },
      "source": [
        "Histograms show the distribution of numeric data.  Most of the variables in this dataset are numeric.  Let's create a new variable so that we can try visualizing non-numeric (\"categorical\") data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsrwZLxK2h64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create a new numeric variable showing the percent of applicants who were accepted\n",
        "df['perc.accept'] = (100*df.Accept/df.Apps).round(1)\n",
        "\n",
        "# create a new categorical variable showing how selective a college is\n",
        "breaks = [0,50,75,100]\n",
        "df['Selective'] = pd.cut(df['perc.accept'],\n",
        "\t\t\t   include_lowest=True, bins=breaks,\n",
        "\t\t\t   labels=['high', 'medium', 'low'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-ue7FYr3E6h",
        "colab_type": "text"
      },
      "source": [
        "The `Apps` column in the data gives the number of applicants, and the `Accept` column gives the number of accepted applicants.  \n",
        "Let's look at the distribution of the new `perc.accept` values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEiiEqM63-05",
        "colab_type": "code",
        "outputId": "f3e510d1-716d-4d96-97ae-300558f0e9cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))        # set the figure size\n",
        "sns.distplot(df['perc.accept'])  # create a histogram with Seaborn\n",
        "plt.title('Histogram of percent accepted')    # set the plot title\n",
        "plt.xlabel('Percentage of applicants accepted.');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j3dEDj9cndSkkMYYWIqIw5vH68GmlFZmpGigVkfmRrktQQQRg6+Bk3GgXme9w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\nS5CkRQghhBAu4f8D0Sn+E27aTiIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7S66ukd4UKx",
        "colab_type": "text"
      },
      "source": [
        "We see that colleges commonly accept 80% of applicants, and relatively few colleges accept less than 40% of applicants.  I wonder if colleges today are more selective."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-qUp1dp4rKE",
        "colab_type": "text"
      },
      "source": [
        "Now let's visualize the distribution of values of the new `Selective` column.  As a first step, let's count how many colleges are in each category of selectivity."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2wwCn-u4McI",
        "colab_type": "code",
        "outputId": "e96795ba-2130-459a-cc51-4f094e1f0328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "df['Selective'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "low       445\n",
              "medium    274\n",
              "high       58\n",
              "Name: Selective, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48veBCwE5JVy",
        "colab_type": "text"
      },
      "source": [
        "The possible values of the `Selective` variable are low, medium, and high.  We defined the value to be low when 75% or more of applicants are accepted and high when 50% or less of applicants are accepted.  The `value_counts()` function counts the number of colleges associated with each of these values.  It is easy to plot these counts with a Pandas bar plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6jgI-86485B",
        "colab_type": "code",
        "outputId": "9aa8551f-178e-477c-b5cb-a516db199470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))        # set the figure size\n",
        "df['Selective'].value_counts().plot.bar()   # a Pandas bar plot\n",
        "plt.title('Counts of colleges by selectivity');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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21fz58/Wd73yn2bZ/5swZDRkyRN26ddOqVauabbstzZw5c7Rx40Y99NBDbTrI0PJwmgYA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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vO0tcUqz67aL",
        "colab_type": "text"
      },
      "source": [
        "Are larger colleges more expensive than public ones?  This is a basic question, but one that involves looking at multiple variables at a time.  If both variables are numeric, a basic visualization tool is the scatterplot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80s6U-IP5xfQ",
        "colab_type": "code",
        "outputId": "bbcf6beb-4ba6-471b-e312-204edfd55388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "plt.figure(figsize=(8,8))        # set the figure size\n",
        "plt.scatter(df['F.Undergrad'], df['Outstate'])  # a matplotlib scatterplot\n",
        "plt.title('Out-of-state tuition by number of full-time undergrads');\n",
        "plt.xlabel('Number of full-time undergrads')\n",
        "plt.ylabel('Out-of-state tuition ($)');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sUfBiYi0Ccun/12VRVaNDbZ0ejw65GdlT0k0qfktBiL2tC9ZaMYiIyLewRcGL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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v64XaVb_8VAb",
        "colab_type": "text"
      },
      "source": [
        "Looking at the left of this plot we see that tuition at small college varies widely.  Looking at the right we wee that, on average, larger colleges tend to have lower tuition.  (Remember that this is 1995 data.)\n",
        "A great feature of a scatter plot is that it shows all the data.  When looking at a scatter plot it is wise to look for unusual points, like the point at the bottom right.  Assuming the data is correct, this point represents a very large and very inexpensize college."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yv2_zGsG9N1B",
        "colab_type": "text"
      },
      "source": [
        "Are private colleges more expensive than public ones?  This question again involves two variables, but this time one of the variables is numeric and one is categorical.  An easy way to visualize these kinds of situations is with a Seaborn bar plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOZzEKHD8D-A",
        "colab_type": "code",
        "outputId": "59d37297-ea39-4056-f237-d9dbd10507fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))        \n",
        "sns.barplot(x='Private', y='Outstate', data=df)     # a Seaborn bar plot\n",
        "plt.title('Average out-of-state tuition for private and public colleges')\n",
        "plt.ylabel('Out-of-state tuition ($)');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAMoqu4JC+fLlVbduXYcUYLFY9Nprr+npp59WxYoVlZ6ernfeeUcJCQmqUaOG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\nCAoAAMDU/wN4SQiwgxgWggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iCQQS61-zOi",
        "colab_type": "text"
      },
      "source": [
        "It's possible to visualize more than two variables at a time.  For example, we can visualize two numeric variables plus a single categorical variable by coloring the dots in the scatter plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rro8BqaZ9_Ko",
        "colab_type": "code",
        "outputId": "4864137d-03a0-4b16-e8b5-c06cd9fd8c8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "plt.figure(figsize=(9,7))       \n",
        "sns.scatterplot(x='F.Undergrad', y='Outstate', data=df, hue='Private')\n",
        "plt.title('Out-of-state tuition by college size')\n",
        "plt.ylabel('Out-of-state tuition ($)')\n",
        "plt.xlabel('Number of full-time undergrads');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2jaKiIurXr8+zzz4LeMf5KvtYTaQsm+axodwzvDO6DtgKW0HHFrWZ+cFvvlVZ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\nVSPjDjQzRAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5C9byJdDAMA5",
        "colab_type": "text"
      },
      "source": [
        "Seaborn made it easy to create this fancy plot.  Notice that the plot legend was created automatically.  The unusual plot on the lower-right is now revealed to be an exceptionally cheap private college.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D17U6T0rAsOK",
        "colab_type": "text"
      },
      "source": [
        "In this plot above we used color to distinguish private and public colleges, but the style of plotted point can also be used, either in addition to color or as an alternative.   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMjwxXn3BSl6",
        "colab_type": "text"
      },
      "source": [
        "## Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1FYWTt_gYJZ",
        "colab_type": "text"
      },
      "source": [
        "If you have data about a college, can you predict whether the college is public or private?  Can you predict the tuition of the college?  We will use machine learning to try to answer these questions.\n",
        "\n",
        "The basic idea is that we will create a *predictive model* by showing a machine learning algorithm a bunch of example colleges, and for each college, provide a label that shows whether the college is public or private.  After the algorithm has seen enough examples, it will find patterns in the data and will be able to predict whether a new college is public or private.\n",
        "\n",
        "We will use the Scikit-Learn package, which supports many machine-learning algorithms.\n",
        "\n",
        "Before using machine learning, we will perform three steps of data preparation:\n",
        "1. make all variables numeric\n",
        "2. randomly split the rows of the data set into two groups: the *training data* used to train the machine the machine learning algorith, and the *test data* used to test the quality of the predictions made by the machine learning algorithm.\n",
        "3. *scale* the training and test data so that values of all columns cover about the same range of values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsePme00HJSI",
        "colab_type": "text"
      },
      "source": [
        "Here are Python packages we will use for machine learning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPEfrt-qHMj6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import graphviz \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "from sklearn.neighbors import KNeighborsRegressor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtqFIW5sqDzn",
        "colab_type": "text"
      },
      "source": [
        "### Predicting whether a college is public or private\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2lq8LLksYaw",
        "colab_type": "text"
      },
      "source": [
        "We will see how well we can predict whether a college is private based on its\n",
        "out-of-state tuition and number of full-time undergraduates.  In other words, we will use out-of-state tuition and number of full-time undergraduates as our *predictor* variables, and whether the college is Private as our *target* variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-A3t0pYqHr_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictors = ['F.Undergrad', 'Outstate']\n",
        "target = 'Private'\n",
        "X = df[predictors].values\n",
        "y = (df[target] == 'Yes').astype('int').values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EguQDWNtJt3",
        "colab_type": "text"
      },
      "source": [
        "We randomly split the rows of the data set into two groups: the training data used to train the machine the machine learning algorith, and the test data used to test the quality of the predictions made by the machine learning algorithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNXzXcfRtUa3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWbUxwAKs8wv",
        "colab_type": "text"
      },
      "source": [
        "We will use the Scikit-Learn package for machine learning.  With Scikit-Learn, we need to *standardize* the data by making sure all variables cover about the same range.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_CHAh5qtk-I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxWFcLOht2MQ",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the first rows of our training data set.  The first column contains information about the number of full-time undergraduates.  The data has been scaled, so a number that is negative represents a college that is smaller than average.  Similar, the second column is scaled data about the college tuition."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwBLCqJzt9M_",
        "colab_type": "code",
        "outputId": "ac60683e-7e06-4e02-edb7-7279aaa3acea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "print(X_train[:10,:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.388525   -0.15998176]\n",
            " [-0.43238556 -0.14882234]\n",
            " [-0.75388346 -0.42855168]\n",
            " [-0.57931843 -1.09067696]\n",
            " [-0.66660095 -0.4756692 ]\n",
            " [-0.65958326  1.68132171]\n",
            " [-0.12799327  0.58695508]\n",
            " [-0.12514234 -1.20797481]\n",
            " [-0.47054425  0.76426579]\n",
            " [-0.55387931 -0.26240039]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2mVw4GSu2Zg",
        "colab_type": "text"
      },
      "source": [
        "We are ready to build a predictive model.  We will build a *classifier* that will predict whether a college is private or not.  There are many kinds of classifiers; we will build a *decision tree* classifier.  To build the classifier, we need to feed it the training data set we created earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEAPI7hmumcw",
        "colab_type": "code",
        "outputId": "3ca432ba-ce45-4c84-8618-798fd688d9e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "source": [
        "clf = DecisionTreeClassifier(max_depth=2, random_state=0)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=2, 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='deprecated',\n",
              "                       random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFa0z4CXwx9f",
        "colab_type": "text"
      },
      "source": [
        "The exciting part is to use this classifier to make predictions.  We will ask it to make predictions for the colleges in the test data set.  It has not seen information about these colleges before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLQJttC0vhOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = clf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AHoLcBAxJqX",
        "colab_type": "text"
      },
      "source": [
        "How many of the predictions are correct?  Let's compare the first ten predictions with the correct answers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDuFR9WHxTVe",
        "colab_type": "code",
        "outputId": "1f8db08b-c0c6-4222-c95d-aa7d4bad0d6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(predictions[:10])\n",
        "print(y_test[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 1 1 1 1 0 1 0]\n",
            "[0 0 0 1 1 1 1 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE6pO2Q1xyG5",
        "colab_type": "text"
      },
      "source": [
        "The first prediction is 0, and the correct answer is 0, so the first prediction is correct.  In fact, all 10 predictions are correct.  We can easily calculate the *accuracy* of our classifier, which is the fraction of predictions that are correct.  An accuracy value ranges from 0 to 1, with 1 meaning that all predictions are correct."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_a5HP-b6yKRP",
        "colab_type": "code",
        "outputId": "3e106a68-6c90-4678-9c5b-37074a4d2d7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "accuracy = (predictions == y_test).mean()\n",
        "print('accuracy: {0:.3f}'. format(accuracy))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d3liUBQy36J",
        "colab_type": "text"
      },
      "source": [
        "Our predictions were correct about 92% of the time.  We can display our decision tree classifier to see how it makes its predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fq8DHL5xyOI2",
        "colab_type": "code",
        "outputId": "92010489-ad67-46a5-f3bd-a8891afdce94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "# create the decision tree image\n",
        "dot_data = export_graphviz(clf, precision=2,\n",
        "                     feature_names=predictors,  \n",
        "                     proportion=True,\n",
        "                     class_names=['No', 'Yes'],  \n",
        "                     filled=True, rounded=True,  \n",
        "                     special_characters=True)\n",
        "\n",
        "# plot it\n",
        "graph = graphviz.Source(dot_data)  \n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f80ce523e48>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"614pt\" height=\"314pt\"\n viewBox=\"0.00 0.00 614.00 314.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 310)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-310 610,-310 610,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#83c2ef\" stroke=\"#000000\" d=\"M357,-306C357,-306 241,-306 241,-306 235,-306 229,-300 229,-294 229,-294 229,-235 229,-235 229,-229 235,-223 241,-223 241,-223 357,-223 357,-223 363,-223 369,-229 369,-235 369,-235 369,-294 369,-294 369,-300 363,-306 357,-306\"/>\n<text text-anchor=\"start\" x=\"247.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Outstate ≤ &#45;0.61</text>\n<text text-anchor=\"start\" x=\"269.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.4</text>\n<text text-anchor=\"start\" x=\"238.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 100.0%</text>\n<text text-anchor=\"start\" x=\"237\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.27, 0.73]</text>\n<text text-anchor=\"start\" x=\"262\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Yes</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#efb489\" stroke=\"#000000\" d=\"M279.5,-187C279.5,-187 158.5,-187 158.5,-187 152.5,-187 146.5,-181 146.5,-175 146.5,-175 146.5,-116 146.5,-116 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gZmRc4n0W0A",
        "colab_type": "text"
      },
      "source": [
        "A decision is made by working downward from the top node of the tree.  The nodes of the bottom level represent a prediction.  If class = Yes, then the prediction is that the college is private; otherwise the prediction is that the college is public.  For example, at the top node of the tree we look at whether the out-of-state tuition value (after scaling) is less than -0.61.  If so move to the child node that the 'True' arrow points to, and otherwise move to the child node that the 'False' arrow points to."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CRcimoV1e5R",
        "colab_type": "text"
      },
      "source": [
        "Summarizing the tree: a college is predicted to be private if the out-of-state is tuition is below -0.61 and the number of undergraduates is below -0.35, or if the out-of-state-tuition is above -0.61 and number of undergraduates is less than 0.51.  Otherwise a college is predicted to be public."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NxI7g1b2KVn",
        "colab_type": "text"
      },
      "source": [
        "### Predicting a college's tuition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3i34Q_s2San",
        "colab_type": "text"
      },
      "source": [
        "Let's try to predict a college's tuition based on how selective it is and how many full-time undergraduates it has.  We will perform data preparation similarly to what we did when predicting public or private."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8NPLsVB1aLn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictors = ['perc.accept', 'F.Undergrad']\n",
        "target = 'Outstate'\n",
        "X = df[predictors].values\n",
        "y = df[target].values\n",
        "\n",
        "# split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)\n",
        "\n",
        "# scale the data\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58bJomhs3_uR",
        "colab_type": "text"
      },
      "source": [
        "Our last predictive model was a classifier -- it predicted one of two classes: public or private.  Now we will build a predictive model to predict out-of-state tuition, which is not a class, but a number.  This kind of predictive model is called a *regressor*.  The specific kind of regressor we will create is a k-nearest-neighbor (KNN) regressor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PytxjQLM4aom",
        "colab_type": "code",
        "outputId": "f9529469-214c-414d-a78c-e1f67a70036d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "regr = KNeighborsRegressor()\n",
        "regr.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                    weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lN3QpO9L8cQz",
        "colab_type": "text"
      },
      "source": [
        "Now we will predict tuition values with our model using the test data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcQjIB0B8iVu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = regr.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bE8OjLk58p5g",
        "colab_type": "text"
      },
      "source": [
        "As before, let's compare our predictions with the correct values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSLpTsIS82vv",
        "colab_type": "code",
        "outputId": "fb1394c6-7f3d-4b58-b959-79f974c29f7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(predictions[:10].astype(int))\n",
        "print(y_test[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 9606  7985  6560 12579 11182 10447  9574  9116 10597  6127]\n",
            "[ 6550  4680  8907 10335  6400 10390 11240  3648 12520  6684]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijATlmPX9Jro",
        "colab_type": "text"
      },
      "source": [
        "For the first college in the test set, our predicted tuition is \\$9606 and the actual tuition is \\$6550.  That is not very close.  For the second college we predicted \\$7985 but the actual is \\$4680.  Again, not very close."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ma4_2PM9p9Y",
        "colab_type": "text"
      },
      "source": [
        "One way to measure the overall error in our predictions is to look at the average squared difference between the predicted and actual tuition.  This is called the *mean squared error*, or MSE."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaS3fGjF9oMf",
        "colab_type": "code",
        "outputId": "e2036e16-f4bb-466d-b105-cbe62de0b06f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "mse = ((predictions - y_test)**2).mean()\n",
        "print('MSE: {0:.0f}'.format(mse))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 12178150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ets_z3BAAb8Y",
        "colab_type": "text"
      },
      "source": [
        "We can also compare our predictions to the actual values by plotting the error as a histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CI__BrLAkAg",
        "colab_type": "code",
        "outputId": "fc791b23-c08e-48df-af9e-9e6dad9697d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.distplot(predictions - y_test)\n",
        "plt.title('Histogram of prediction error, regression model 1')\n",
        "plt.xlabel('Absolute difference between prediction and actual value ($)')\n",
        "plt.xlim(-12000, 12000);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GiAKEEAKFX5UjdlgopmclyF1OUAvRKJEUG4HyunYOFZBfYBggChBnKppxobYN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\nCCGEkA6OigFCCCGkg6NigBBCCOng/h/aRQgiQQsZeQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LaPXfdR7-fgh",
        "colab_type": "text"
      },
      "source": [
        "Our tuition predictions were based on only college selectivity and size.  Could we do better by using more variables.  For example, could we make better predictions by also looking at 4-year graduation rate, student/faculty ratio, and precentage of faculty with PhDs?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm-svHG-_o4_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictors = ['perc.accept', 'F.Undergrad', 'Grad.Rate', 'S.F.Ratio', 'PhD']\n",
        "target = 'Outstate'\n",
        "X = df[predictors].values\n",
        "y = df[target].values\n",
        "\n",
        "# split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)\n",
        "\n",
        "# scale the data\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfxIi30n_2NA",
        "colab_type": "code",
        "outputId": "a28b9d06-5ddb-45ec-923f-1b6bbfc1966d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "regr = KNeighborsRegressor()\n",
        "regr.fit(X_train, y_train)\n",
        "predictions = regr.predict(X_test)\n",
        "\n",
        "# print predictions and actual values\n",
        "print(predictions[:10].astype(int))\n",
        "print(y_test[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 6823  6393 10492 14652  7861 10653 13421  5581  8969  4591]\n",
            "[ 6550  4680  8907 10335  6400 10390 11240  3648 12520  6684]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJJCNlfpAENA",
        "colab_type": "text"
      },
      "source": [
        "These predictions look much better.  Is the new mean squared error value lower than before?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4D8Uydv3AQBo",
        "colab_type": "code",
        "outputId": "fe8177cb-c2d1-4c55-96cd-e3acdab5a8a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "mse = ((predictions - y_test)**2).mean()\n",
        "print('MSE: {0:.0f}'.format(mse))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 6146413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d__mcQOcBkNZ",
        "colab_type": "text"
      },
      "source": [
        "Let's plot the error as we did we our previous model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_n67JT8aBngf",
        "colab_type": "code",
        "outputId": "a4d850a2-355a-446f-bc25-8d421e9df160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.distplot(predictions - y_test)\n",
        "plt.title('Histogram of prediction error, regression model 2')\n",
        "plt.xlabel('Absolute difference between prediction and actual value ($)')\n",
        "plt.xlim(-12000, 12000);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tk/HMM8+439ArVqzAnXfeCYfDgeeffx533HEHTCYTli5dCqvViuuvv949lXjllVfi8ccf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\nUSCEEEJIgyhRIIQQQkiD/h9RcQ2ZjSZ8vwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLa7FlJUCCnZ",
        "colab_type": "text"
      },
      "source": [
        "It's easy to see that on average our predictions are better than with our previous predictive model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0s1mkgEZGzmo",
        "colab_type": "text"
      },
      "source": [
        "## Conclusions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHJ897WvF8aJ",
        "colab_type": "text"
      },
      "source": [
        "If you'd like to do more with this data set, here are some other things to try:\n",
        "- produce histograms for some of the other variables in the data set\n",
        "- produce scatterplots for other pairs of variables in the data set\n",
        "- try finding three predictor variables that give the best classification accuracy\n",
        "- see which predictor variables give the lowest MSE "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFozFeYJFOKy",
        "colab_type": "text"
      },
      "source": [
        "We just covered the absolute basics of data exploration and predictive modeling.  To learn more, a couple of good and accessible books are:\n",
        "- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 2 (Aerelion Geron)\n",
        "- Python Data Science Handbook (Jake VanderPlas)"
      ]
    }
  ]
}