KMeans Clustering Project.ipynb

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    "colab": {
      "name": "02-K Means Clustering Project.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/goldenfingaz/Machine_Learning_with_Python/blob/main/KMeans%20Clustering%20Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLdC2n9AT12T"
      },
      "source": [
        "___\n",
        "\n",
        "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
        "___\n",
        "# K Means Clustering Project \n",
        "\n",
        "For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public.\n",
        "\n",
        "___\n",
        "It is **very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.** \n",
        "\n",
        "When using the Kmeans algorithm under normal circumstances, it is because you don't have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but you won't usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don't truly make sense in a real world setting!.\n",
        "___\n",
        "\n",
        "## The Data\n",
        "\n",
        "We will use a data frame with 777 observations on the following 18 variables.\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": "T3JTZOYHT12a"
      },
      "source": [
        "## Import Libraries\n",
        "\n",
        "** Import the libraries you usually use for data analysis.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "xeKzVwf8T12b"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Zn3POcnT12b"
      },
      "source": [
        "## Get the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0NZkM0hT12b"
      },
      "source": [
        "** Read in the College_Data file using read_csv. Figure out how to set the first column as the index.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "_LYA7qkNT12c"
      },
      "source": [
        "college = pd.read_csv(\"https://raw.githubusercontent.com/goldenfingaz/Datasets/master/College_Data\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKuA5L2bUtWL"
      },
      "source": [
        "college = college.set_index(college.columns[0])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "un6sbBO_T12c"
      },
      "source": [
        "**Check the head of the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "aWOYcqQAT12c",
        "outputId": "bc2b0593-c4cb-4621-dfdb-03db8e634fc9"
      },
      "source": [
        "college.head()"
      ],
      "execution_count": 12,
      "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",
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              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
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              "      <th></th>\n",
              "      <th></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",
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            "text/plain": [
              "                             Private  Apps  ...  Expend  Grad.Rate\n",
              "Unnamed: 0                                  ...                   \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]"
            ]
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Di1cJXmST12d"
      },
      "source": [
        "** Check the info() and describe() methods on the data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FfdyGLEqT12d",
        "outputId": "95494480-1df8-46c3-c209-2cece0878100"
      },
      "source": [
        "college.info()"
      ],
      "execution_count": 13,
      "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",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Private      777 non-null    object \n",
            " 1   Apps         777 non-null    int64  \n",
            " 2   Accept       777 non-null    int64  \n",
            " 3   Enroll       777 non-null    int64  \n",
            " 4   Top10perc    777 non-null    int64  \n",
            " 5   Top25perc    777 non-null    int64  \n",
            " 6   F.Undergrad  777 non-null    int64  \n",
            " 7   P.Undergrad  777 non-null    int64  \n",
            " 8   Outstate     777 non-null    int64  \n",
            " 9   Room.Board   777 non-null    int64  \n",
            " 10  Books        777 non-null    int64  \n",
            " 11  Personal     777 non-null    int64  \n",
            " 12  PhD          777 non-null    int64  \n",
            " 13  Terminal     777 non-null    int64  \n",
            " 14  S.F.Ratio    777 non-null    float64\n",
            " 15  perc.alumni  777 non-null    int64  \n",
            " 16  Expend       777 non-null    int64  \n",
            " 17  Grad.Rate    777 non-null    int64  \n",
            "dtypes: float64(1), int64(16), object(1)\n",
            "memory usage: 135.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "5SF8ChcBT12e",
        "outputId": "abc57e13-3dd5-4e0f-90d0-4df7335a2628"
      },
      "source": [
        "college.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>count</th>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3001.638353</td>\n",
              "      <td>2018.804376</td>\n",
              "      <td>779.972973</td>\n",
              "      <td>27.558559</td>\n",
              "      <td>55.796654</td>\n",
              "      <td>3699.907336</td>\n",
              "      <td>855.298584</td>\n",
              "      <td>10440.669241</td>\n",
              "      <td>4357.526384</td>\n",
              "      <td>549.380952</td>\n",
              "      <td>1340.642214</td>\n",
              "      <td>72.660232</td>\n",
              "      <td>79.702703</td>\n",
              "      <td>14.089704</td>\n",
              "      <td>22.743887</td>\n",
              "      <td>9660.171171</td>\n",
              "      <td>65.46332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3870.201484</td>\n",
              "      <td>2451.113971</td>\n",
              "      <td>929.176190</td>\n",
              "      <td>17.640364</td>\n",
              "      <td>19.804778</td>\n",
              "      <td>4850.420531</td>\n",
              "      <td>1522.431887</td>\n",
              "      <td>4023.016484</td>\n",
              "      <td>1096.696416</td>\n",
              "      <td>165.105360</td>\n",
              "      <td>677.071454</td>\n",
              "      <td>16.328155</td>\n",
              "      <td>14.722359</td>\n",
              "      <td>3.958349</td>\n",
              "      <td>12.391801</td>\n",
              "      <td>5221.768440</td>\n",
              "      <td>17.17771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>81.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2340.000000</td>\n",
              "      <td>1780.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3186.000000</td>\n",
              "      <td>10.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>776.000000</td>\n",
              "      <td>604.000000</td>\n",
              "      <td>242.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>992.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>7320.000000</td>\n",
              "      <td>3597.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6751.000000</td>\n",
              "      <td>53.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1558.000000</td>\n",
              "      <td>1110.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1707.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>9990.000000</td>\n",
              "      <td>4200.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>13.600000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>8377.000000</td>\n",
              "      <td>65.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3624.000000</td>\n",
              "      <td>2424.000000</td>\n",
              "      <td>902.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>4005.000000</td>\n",
              "      <td>967.000000</td>\n",
              "      <td>12925.000000</td>\n",
              "      <td>5050.000000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>1700.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>10830.000000</td>\n",
              "      <td>78.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>48094.000000</td>\n",
              "      <td>26330.000000</td>\n",
              "      <td>6392.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>31643.000000</td>\n",
              "      <td>21836.000000</td>\n",
              "      <td>21700.000000</td>\n",
              "      <td>8124.000000</td>\n",
              "      <td>2340.000000</td>\n",
              "      <td>6800.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>39.800000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>56233.000000</td>\n",
              "      <td>118.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Apps        Accept  ...        Expend  Grad.Rate\n",
              "count    777.000000    777.000000  ...    777.000000  777.00000\n",
              "mean    3001.638353   2018.804376  ...   9660.171171   65.46332\n",
              "std     3870.201484   2451.113971  ...   5221.768440   17.17771\n",
              "min       81.000000     72.000000  ...   3186.000000   10.00000\n",
              "25%      776.000000    604.000000  ...   6751.000000   53.00000\n",
              "50%     1558.000000   1110.000000  ...   8377.000000   65.00000\n",
              "75%     3624.000000   2424.000000  ...  10830.000000   78.00000\n",
              "max    48094.000000  26330.000000  ...  56233.000000  118.00000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TB9-ZbVOT12e"
      },
      "source": [
        "## EDA\n",
        "\n",
        "It's time to create some data visualizations!\n",
        "\n",
        "** Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column. **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "zL0FfQ9YT12e",
        "outputId": "fe171750-4b47-4656-ad3d-f9cc4383c3b0"
      },
      "source": [
        "sns.scatterplot(data=college,x='Room.Board',y='Grad.Rate',hue='Private')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f30973de990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjcczM_DT12e"
      },
      "source": [
        "**Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "7I_qEZodT12f",
        "outputId": "f0b43d99-1db9-4a52-e193-21a95f833427"
      },
      "source": [
        "plt.figure(figsize=[4,6])\n",
        "sns.scatterplot(data=college,x='Outstate',y='F.Undergrad',hue='Private')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3093bbb990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIaIXKpGT12f"
      },
      "source": [
        "** Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html). If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist'). **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "aLKTTCnbT12f",
        "outputId": "c142dac0-8246-4cbb-89b0-71a0496f4af0"
      },
      "source": [
        "college[college['Private']=='Yes']['Outstate'].plot(kind='hist',bins=30,alpha=0.6)\n",
        "college[college['Private']=='No']['Outstate'].plot(kind='hist',bins=30,alpha=0.6)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f30934de350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-xG8M3yT12f"
      },
      "source": [
        "**Create a similar histogram for the Grad.Rate column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jxwzN2KFT12f",
        "outputId": "62749c02-c612-4e74-bede-b4ccd5941fce"
      },
      "source": [
        "college[college['Private']=='Yes']['Grad.Rate'].plot(kind='hist',bins=30,alpha=0.6)\n",
        "college[college['Private']=='No']['Grad.Rate'].plot(kind='hist',bins=30,alpha=0.6)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3093413990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4bBWOxRT12g"
      },
      "source": [
        "** Notice how there seems to be a private school with a graduation rate of higher than 100%.What is the name of that school?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "r1qReE9qT12g",
        "outputId": "e45595ae-b09e-41be-e3d8-92031b86cd47"
      },
      "source": [
        "college[college['Grad.Rate'] > 100]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cazenovia College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3847</td>\n",
              "      <td>3433</td>\n",
              "      <td>527</td>\n",
              "      <td>9</td>\n",
              "      <td>35</td>\n",
              "      <td>1010</td>\n",
              "      <td>12</td>\n",
              "      <td>9384</td>\n",
              "      <td>4840</td>\n",
              "      <td>600</td>\n",
              "      <td>500</td>\n",
              "      <td>22</td>\n",
              "      <td>47</td>\n",
              "      <td>14.3</td>\n",
              "      <td>20</td>\n",
              "      <td>7697</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Private  Apps  Accept  ...  perc.alumni  Expend  Grad.Rate\n",
              "Unnamed: 0                               ...                                \n",
              "Cazenovia College     Yes  3847    3433  ...           20    7697        118\n",
              "\n",
              "[1 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNYQTO7bT12g"
      },
      "source": [
        "** Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRA_nhvCT12g"
      },
      "source": [
        "college['Grad.Rate'] = college['Grad.Rate'].replace(118,100)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "b-bMTRHaT12h",
        "outputId": "81f1d027-b054-4d74-f40c-12a5ae0cfec6"
      },
      "source": [
        "college[college['Private']=='Yes']['Grad.Rate'].plot(kind='hist',bins=30,alpha=0.6)\n",
        "college[college['Private']=='No']['Grad.Rate'].plot(kind='hist',bins=30,alpha=0.6)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f309318c5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjXydc14T12h",
        "outputId": "d5610e68-8fe1-4883-cdd5-35dd829179d8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RLBYjynOGOwbAmHbk+8C+4Y5xQo2Nbxr0vgoWAMOiWieED8buJ16IWXOmD3cMgDGtWNwX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\nvkJfrrscWfbKWq7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            "text/plain": [
              "<matplotlib.figure.Figure at 0x13687e588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76Y05kZPT12h"
      },
      "source": [
        "## K Means Cluster Creation\n",
        "\n",
        "Now it is time to create the Cluster labels!\n",
        "\n",
        "** Import KMeans from SciKit Learn.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "iNtZ3cOvT12h"
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94wi74cuT12h"
      },
      "source": [
        "** Create an instance of a K Means model with 2 clusters.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "pBcsetslT12h"
      },
      "source": [
        "kmeans = KMeans(n_clusters=2)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lUZ5FHUT12i"
      },
      "source": [
        "**Fit the model to all the data except for the Private label.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOVSiduOT12i",
        "outputId": "17c239a8-93e4-48e0-b4f8-730f2414dd99"
      },
      "source": [
        "kmeans.fit(college.drop('Private',axis=1))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGFevCrAT12i"
      },
      "source": [
        "** What are the cluster center vectors?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBpzF1tPT12i",
        "outputId": "0763e886-ea2e-4fea-81b7-05d994e9d678"
      },
      "source": [
        "kmeans.cluster_centers_"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.81323468e+03, 1.28716592e+03, 4.91044843e+02, 2.53094170e+01,\n",
              "        5.34708520e+01, 2.18854858e+03, 5.95458894e+02, 1.03957085e+04,\n",
              "        4.31136472e+03, 5.41982063e+02, 1.28033632e+03, 7.04424514e+01,\n",
              "        7.78251121e+01, 1.40997010e+01, 2.31748879e+01, 8.93204634e+03,\n",
              "        6.50926756e+01],\n",
              "       [1.03631389e+04, 6.55089815e+03, 2.56972222e+03, 4.14907407e+01,\n",
              "        7.02037037e+01, 1.30619352e+04, 2.46486111e+03, 1.07191759e+04,\n",
              "        4.64347222e+03, 5.95212963e+02, 1.71420370e+03, 8.63981481e+01,\n",
              "        9.13333333e+01, 1.40277778e+01, 2.00740741e+01, 1.41705000e+04,\n",
              "        6.75925926e+01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HVGtVBmT12i"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "There is no perfect way to evaluate clustering if you don't have the labels, however since this is just an exercise, we do have the labels, so we take advantage of this to evaluate our clusters, keep in mind, you usually won't have this luxury in the real world.\n",
        "\n",
        "** Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sN3qm3VT12j"
      },
      "source": [
        "dic = {'Yes':1,'No':0}\n",
        "college['Cluster'] = college['Private'].map(dic)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "atCRJZStT12j",
        "outputId": "cf19344c-c204-4bff-a56d-5e57f354af4a"
      },
      "source": [
        "college.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Private  Apps  Accept  ...  Expend  Grad.Rate  Cluster\n",
              "Unnamed: 0                                          ...                            \n",
              "Abilene Christian University     Yes  1660    1232  ...    7041         60        1\n",
              "Adelphi University               Yes  2186    1924  ...   10527         56        1\n",
              "Adrian College                   Yes  1428    1097  ...    8735         54        1\n",
              "Agnes Scott College              Yes   417     349  ...   19016         59        1\n",
              "Alaska Pacific University        Yes   193     146  ...   10922         15        1\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaOM2SirT12j"
      },
      "source": [
        "** Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eckGDuG4T12j"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn1TB_5Wb2Sn",
        "outputId": "3462e8f3-1256-4f3f-b27c-4e9bee87686d"
      },
      "source": [
        "print(confusion_matrix(college['Cluster'],kmeans.labels_))\n",
        "print(classification_report(college['Cluster'],kmeans.labels_))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[138  74]\n",
            " [531  34]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.21      0.65      0.31       212\n",
            "           1       0.31      0.06      0.10       565\n",
            "\n",
            "    accuracy                           0.22       777\n",
            "   macro avg       0.26      0.36      0.21       777\n",
            "weighted avg       0.29      0.22      0.16       777\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zXnechNT12j"
      },
      "source": [
        "Not so bad considering the algorithm is purely using the features to cluster the universities into 2 distinct groups! Hopefully you can begin to see how K Means is useful for clustering un-labeled data!\n",
        "\n",
        "## Great Job!"
      ]
    }
  ]
}