DS-8-FinalMLAssignment.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%206/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Classification with Python\n\nEstimated time needed: **25** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Confidently create classification models\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "In this notebook we try to practice all the classification algorithms that we learned in this course.\n\nWe load a dataset using Pandas library, apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n\nLet's first load required libraries:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [],
            "source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nfrom sklearn import preprocessing\n%matplotlib inline"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### About dataset\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "This dataset is about the performance of basketball teams. The **cbb.csv** data set includes performance data about five seasons of 354 basketball teams. It includes the following fields:\n\n| Field      | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |\n| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| TEAM       | The Division I college basketball school                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |\n| CONF       | The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference) |\n| G          | Number of games played                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |\n| W          | Number of games won                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n| ADJOE      | Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n| ADJDE      | Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n| BARTHAG    | Power Rating (Chance of beating an average Division I team)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |\n| EFG_O      | Effective Field Goal Percentage Shot                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n| EFG_D      | Effective Field Goal Percentage Allowed                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| TOR        | Turnover Percentage Allowed (Turnover Rate)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |\n| TORD       | Turnover Percentage Committed (Steal Rate)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |\n| ORB        | Offensive Rebound Percentage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |\n| DRB        | Defensive Rebound Percentage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |\n| FTR        | Free Throw Rate (How often the given team shoots Free Throws)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |\n| FTRD       | Free Throw Rate Allowed                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| 2P_O       | Two-Point Shooting Percentage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |\n| 2P_D       | Two-Point Shooting Percentage Allowed                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |\n| 3P_O       | Three-Point Shooting Percentage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |\n| 3P_D       | Three-Point Shooting Percentage Allowed                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| ADJ_T      | Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |\n| WAB        | Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| POSTSEASON | Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n| SEED       | Seed in the NCAA March Madness Tournament                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |\n| YEAR       | Season                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Load Data From CSV File\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Let's load the dataset \\[NB Need to provide link to csv file]\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TEAM</th>\n      <th>CONF</th>\n      <th>G</th>\n      <th>W</th>\n      <th>ADJOE</th>\n      <th>ADJDE</th>\n      <th>BARTHAG</th>\n      <th>EFG_O</th>\n      <th>EFG_D</th>\n      <th>TOR</th>\n      <th>...</th>\n      <th>FTRD</th>\n      <th>2P_O</th>\n      <th>2P_D</th>\n      <th>3P_O</th>\n      <th>3P_D</th>\n      <th>ADJ_T</th>\n      <th>WAB</th>\n      <th>POSTSEASON</th>\n      <th>SEED</th>\n      <th>YEAR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North Carolina</td>\n      <td>ACC</td>\n      <td>40</td>\n      <td>33</td>\n      <td>123.3</td>\n      <td>94.9</td>\n      <td>0.9531</td>\n      <td>52.6</td>\n      <td>48.1</td>\n      <td>15.4</td>\n      <td>...</td>\n      <td>30.4</td>\n      <td>53.9</td>\n      <td>44.6</td>\n      <td>32.7</td>\n      <td>36.2</td>\n      <td>71.7</td>\n      <td>8.6</td>\n      <td>2ND</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Villanova</td>\n      <td>BE</td>\n      <td>40</td>\n      <td>35</td>\n      <td>123.1</td>\n      <td>90.9</td>\n      <td>0.9703</td>\n      <td>56.1</td>\n      <td>46.7</td>\n      <td>16.3</td>\n      <td>...</td>\n      <td>30.0</td>\n      <td>57.4</td>\n      <td>44.1</td>\n      <td>36.2</td>\n      <td>33.9</td>\n      <td>66.7</td>\n      <td>8.9</td>\n      <td>Champions</td>\n      <td>2.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Notre Dame</td>\n      <td>ACC</td>\n      <td>36</td>\n      <td>24</td>\n      <td>118.3</td>\n      <td>103.3</td>\n      <td>0.8269</td>\n      <td>54.0</td>\n      <td>49.5</td>\n      <td>15.3</td>\n      <td>...</td>\n      <td>26.0</td>\n      <td>52.9</td>\n      <td>46.5</td>\n      <td>37.4</td>\n      <td>36.9</td>\n      <td>65.5</td>\n      <td>2.3</td>\n      <td>E8</td>\n      <td>6.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Virginia</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>29</td>\n      <td>119.9</td>\n      <td>91.0</td>\n      <td>0.9600</td>\n      <td>54.8</td>\n      <td>48.4</td>\n      <td>15.1</td>\n      <td>...</td>\n      <td>33.4</td>\n      <td>52.6</td>\n      <td>46.3</td>\n      <td>40.3</td>\n      <td>34.7</td>\n      <td>61.9</td>\n      <td>8.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kansas</td>\n      <td>B12</td>\n      <td>37</td>\n      <td>32</td>\n      <td>120.9</td>\n      <td>90.4</td>\n      <td>0.9662</td>\n      <td>55.7</td>\n      <td>45.1</td>\n      <td>17.8</td>\n      <td>...</td>\n      <td>37.3</td>\n      <td>52.7</td>\n      <td>43.4</td>\n      <td>41.3</td>\n      <td>32.5</td>\n      <td>70.1</td>\n      <td>11.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 24 columns</p>\n</div>",
                        "text/plain": "             TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  \\\n0  North Carolina  ACC  40  33  123.3   94.9   0.9531   52.6   48.1  15.4   \n1       Villanova   BE  40  35  123.1   90.9   0.9703   56.1   46.7  16.3   \n2      Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3   \n3        Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1   \n4          Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8   \n\n   ...  FTRD  2P_O  2P_D  3P_O  3P_D  ADJ_T   WAB  POSTSEASON  SEED  YEAR  \n0  ...  30.4  53.9  44.6  32.7  36.2   71.7   8.6         2ND   1.0  2016  \n1  ...  30.0  57.4  44.1  36.2  33.9   66.7   8.9   Champions   2.0  2016  \n2  ...  26.0  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016  \n3  ...  33.4  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016  \n4  ...  37.3  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016  \n\n[5 rows x 24 columns]"
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%206/cbb.csv')\ndf.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(1406, 24)"
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.shape"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Add Column\n\nNext we'll add a column that will contain \"true\" if the wins above bubble are over 7 and \"false\" if not. We'll call this column Win Index or \"windex\" for short.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": "df['windex'] = np.where(df.WAB > 7, 'True', 'False')"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Data visualization and pre-processing\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Next we'll filter the data set to the teams that made the Sweet Sixteen, the Elite Eight, and the Final Four in the post season. We'll also create a new dataframe that will hold the values with the new column.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TEAM</th>\n      <th>CONF</th>\n      <th>G</th>\n      <th>W</th>\n      <th>ADJOE</th>\n      <th>ADJDE</th>\n      <th>BARTHAG</th>\n      <th>EFG_O</th>\n      <th>EFG_D</th>\n      <th>TOR</th>\n      <th>...</th>\n      <th>2P_O</th>\n      <th>2P_D</th>\n      <th>3P_O</th>\n      <th>3P_D</th>\n      <th>ADJ_T</th>\n      <th>WAB</th>\n      <th>POSTSEASON</th>\n      <th>SEED</th>\n      <th>YEAR</th>\n      <th>windex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Notre Dame</td>\n      <td>ACC</td>\n      <td>36</td>\n      <td>24</td>\n      <td>118.3</td>\n      <td>103.3</td>\n      <td>0.8269</td>\n      <td>54.0</td>\n      <td>49.5</td>\n      <td>15.3</td>\n      <td>...</td>\n      <td>52.9</td>\n      <td>46.5</td>\n      <td>37.4</td>\n      <td>36.9</td>\n      <td>65.5</td>\n      <td>2.3</td>\n      <td>E8</td>\n      <td>6.0</td>\n      <td>2016</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Virginia</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>29</td>\n      <td>119.9</td>\n      <td>91.0</td>\n      <td>0.9600</td>\n      <td>54.8</td>\n      <td>48.4</td>\n      <td>15.1</td>\n      <td>...</td>\n      <td>52.6</td>\n      <td>46.3</td>\n      <td>40.3</td>\n      <td>34.7</td>\n      <td>61.9</td>\n      <td>8.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kansas</td>\n      <td>B12</td>\n      <td>37</td>\n      <td>32</td>\n      <td>120.9</td>\n      <td>90.4</td>\n      <td>0.9662</td>\n      <td>55.7</td>\n      <td>45.1</td>\n      <td>17.8</td>\n      <td>...</td>\n      <td>52.7</td>\n      <td>43.4</td>\n      <td>41.3</td>\n      <td>32.5</td>\n      <td>70.1</td>\n      <td>11.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Oregon</td>\n      <td>P12</td>\n      <td>37</td>\n      <td>30</td>\n      <td>118.4</td>\n      <td>96.2</td>\n      <td>0.9163</td>\n      <td>52.3</td>\n      <td>48.9</td>\n      <td>16.1</td>\n      <td>...</td>\n      <td>52.6</td>\n      <td>46.1</td>\n      <td>34.4</td>\n      <td>36.2</td>\n      <td>69.0</td>\n      <td>6.7</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Syracuse</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>23</td>\n      <td>111.9</td>\n      <td>93.6</td>\n      <td>0.8857</td>\n      <td>50.0</td>\n      <td>47.3</td>\n      <td>18.1</td>\n      <td>...</td>\n      <td>47.2</td>\n      <td>48.1</td>\n      <td>36.0</td>\n      <td>30.7</td>\n      <td>65.5</td>\n      <td>-0.3</td>\n      <td>F4</td>\n      <td>10.0</td>\n      <td>2016</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 25 columns</p>\n</div>",
                        "text/plain": "         TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  ...  \\\n2  Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  ...   \n3    Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  ...   \n4      Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  ...   \n5      Oregon  P12  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  ...   \n6    Syracuse  ACC  37  23  111.9   93.6   0.8857   50.0   47.3  18.1  ...   \n\n   2P_O  2P_D  3P_O  3P_D  ADJ_T   WAB  POSTSEASON  SEED  YEAR  windex  \n2  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016   False  \n3  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016    True  \n4  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016    True  \n5  52.6  46.1  34.4  36.2   69.0   6.7          E8   1.0  2016   False  \n6  47.2  48.1  36.0  30.7   65.5  -0.3          F4  10.0  2016   False  \n\n[5 rows x 25 columns]"
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df1 = df.loc[df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\ndf1.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "S16    32\nE8     16\nF4      8\nName: POSTSEASON, dtype: int64"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df1['POSTSEASON'].value_counts()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "32 teams made it into the Sweet Sixteen, 16 into the Elite Eight, and 8 made it into the Final Four over 5 seasons.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Lets plot some columns to underestand the data better:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Collecting package metadata (current_repodata.json): done\nSolving environment: done\n\n# All requested packages already installed.\n\n"
                }
            ],
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 1296x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "import seaborn as sns\n\nbins = np.linspace(df1.BARTHAG.min(), df1.BARTHAG.max(), 10)\ng = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=6)\ng.map(plt.hist, 'BARTHAG', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "bins = np.linspace(df1.ADJOE.min(), df1.ADJOE.max(), 10)\ng = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'ADJOE', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Pre-processing:  Feature selection/extraction\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Lets look at how Adjusted Defense Efficiency plots\n"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "bins = np.linspace(df1.ADJDE.min(), df1.ADJDE.max(), 10)\ng = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'ADJDE', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "We see that this data point doesn't impact the ability of a team to get into the Final Four.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## Convert Categorical features to numerical values\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets look at the postseason:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "windex  POSTSEASON\nFalse   S16           0.605263\n        E8            0.263158\n        F4            0.131579\nTrue    S16           0.500000\n        E8            0.333333\n        F4            0.166667\nName: POSTSEASON, dtype: float64"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df1.groupby(['windex'])['POSTSEASON'].value_counts(normalize=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "13% of teams with 6 or less wins above bubble make it into the final four while 17% of teams with 7 or more do.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets convert wins above bubble (winindex) under 7 to 0 and over 7 to 1:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/core/generic.py:6619: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  return self._update_inplace(result)\n"
                },
                {
                    "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>TEAM</th>\n      <th>CONF</th>\n      <th>G</th>\n      <th>W</th>\n      <th>ADJOE</th>\n      <th>ADJDE</th>\n      <th>BARTHAG</th>\n      <th>EFG_O</th>\n      <th>EFG_D</th>\n      <th>TOR</th>\n      <th>...</th>\n      <th>2P_O</th>\n      <th>2P_D</th>\n      <th>3P_O</th>\n      <th>3P_D</th>\n      <th>ADJ_T</th>\n      <th>WAB</th>\n      <th>POSTSEASON</th>\n      <th>SEED</th>\n      <th>YEAR</th>\n      <th>windex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Notre Dame</td>\n      <td>ACC</td>\n      <td>36</td>\n      <td>24</td>\n      <td>118.3</td>\n      <td>103.3</td>\n      <td>0.8269</td>\n      <td>54.0</td>\n      <td>49.5</td>\n      <td>15.3</td>\n      <td>...</td>\n      <td>52.9</td>\n      <td>46.5</td>\n      <td>37.4</td>\n      <td>36.9</td>\n      <td>65.5</td>\n      <td>2.3</td>\n      <td>E8</td>\n      <td>6.0</td>\n      <td>2016</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Virginia</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>29</td>\n      <td>119.9</td>\n      <td>91.0</td>\n      <td>0.9600</td>\n      <td>54.8</td>\n      <td>48.4</td>\n      <td>15.1</td>\n      <td>...</td>\n      <td>52.6</td>\n      <td>46.3</td>\n      <td>40.3</td>\n      <td>34.7</td>\n      <td>61.9</td>\n      <td>8.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kansas</td>\n      <td>B12</td>\n      <td>37</td>\n      <td>32</td>\n      <td>120.9</td>\n      <td>90.4</td>\n      <td>0.9662</td>\n      <td>55.7</td>\n      <td>45.1</td>\n      <td>17.8</td>\n      <td>...</td>\n      <td>52.7</td>\n      <td>43.4</td>\n      <td>41.3</td>\n      <td>32.5</td>\n      <td>70.1</td>\n      <td>11.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Oregon</td>\n      <td>P12</td>\n      <td>37</td>\n      <td>30</td>\n      <td>118.4</td>\n      <td>96.2</td>\n      <td>0.9163</td>\n      <td>52.3</td>\n      <td>48.9</td>\n      <td>16.1</td>\n      <td>...</td>\n      <td>52.6</td>\n      <td>46.1</td>\n      <td>34.4</td>\n      <td>36.2</td>\n      <td>69.0</td>\n      <td>6.7</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Syracuse</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>23</td>\n      <td>111.9</td>\n      <td>93.6</td>\n      <td>0.8857</td>\n      <td>50.0</td>\n      <td>47.3</td>\n      <td>18.1</td>\n      <td>...</td>\n      <td>47.2</td>\n      <td>48.1</td>\n      <td>36.0</td>\n      <td>30.7</td>\n      <td>65.5</td>\n      <td>-0.3</td>\n      <td>F4</td>\n      <td>10.0</td>\n      <td>2016</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 25 columns</p>\n</div>",
                        "text/plain": "         TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  ...  \\\n2  Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  ...   \n3    Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  ...   \n4      Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  ...   \n5      Oregon  P12  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  ...   \n6    Syracuse  ACC  37  23  111.9   93.6   0.8857   50.0   47.3  18.1  ...   \n\n   2P_O  2P_D  3P_O  3P_D  ADJ_T   WAB  POSTSEASON  SEED  YEAR  windex  \n2  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016       0  \n3  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016       1  \n4  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016       1  \n5  52.6  46.1  34.4  36.2   69.0   6.7          E8   1.0  2016       0  \n6  47.2  48.1  36.0  30.7   65.5  -0.3          F4  10.0  2016       0  \n\n[5 rows x 25 columns]"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df1['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\ndf1.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Feature selection\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Let's define feature sets, X:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>G</th>\n      <th>W</th>\n      <th>ADJOE</th>\n      <th>ADJDE</th>\n      <th>BARTHAG</th>\n      <th>EFG_O</th>\n      <th>EFG_D</th>\n      <th>TOR</th>\n      <th>TORD</th>\n      <th>ORB</th>\n      <th>...</th>\n      <th>FTR</th>\n      <th>FTRD</th>\n      <th>2P_O</th>\n      <th>2P_D</th>\n      <th>3P_O</th>\n      <th>3P_D</th>\n      <th>ADJ_T</th>\n      <th>WAB</th>\n      <th>SEED</th>\n      <th>windex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>36</td>\n      <td>24</td>\n      <td>118.3</td>\n      <td>103.3</td>\n      <td>0.8269</td>\n      <td>54.0</td>\n      <td>49.5</td>\n      <td>15.3</td>\n      <td>14.8</td>\n      <td>32.7</td>\n      <td>...</td>\n      <td>32.9</td>\n      <td>26.0</td>\n      <td>52.9</td>\n      <td>46.5</td>\n      <td>37.4</td>\n      <td>36.9</td>\n      <td>65.5</td>\n      <td>2.3</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>37</td>\n      <td>29</td>\n      <td>119.9</td>\n      <td>91.0</td>\n      <td>0.9600</td>\n      <td>54.8</td>\n      <td>48.4</td>\n      <td>15.1</td>\n      <td>18.8</td>\n      <td>29.9</td>\n      <td>...</td>\n      <td>32.1</td>\n      <td>33.4</td>\n      <td>52.6</td>\n      <td>46.3</td>\n      <td>40.3</td>\n      <td>34.7</td>\n      <td>61.9</td>\n      <td>8.6</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>37</td>\n      <td>32</td>\n      <td>120.9</td>\n      <td>90.4</td>\n      <td>0.9662</td>\n      <td>55.7</td>\n      <td>45.1</td>\n      <td>17.8</td>\n      <td>18.5</td>\n      <td>32.2</td>\n      <td>...</td>\n      <td>38.6</td>\n      <td>37.3</td>\n      <td>52.7</td>\n      <td>43.4</td>\n      <td>41.3</td>\n      <td>32.5</td>\n      <td>70.1</td>\n      <td>11.6</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>37</td>\n      <td>30</td>\n      <td>118.4</td>\n      <td>96.2</td>\n      <td>0.9163</td>\n      <td>52.3</td>\n      <td>48.9</td>\n      <td>16.1</td>\n      <td>20.2</td>\n      <td>34.1</td>\n      <td>...</td>\n      <td>40.3</td>\n      <td>32.0</td>\n      <td>52.6</td>\n      <td>46.1</td>\n      <td>34.4</td>\n      <td>36.2</td>\n      <td>69.0</td>\n      <td>6.7</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>37</td>\n      <td>23</td>\n      <td>111.9</td>\n      <td>93.6</td>\n      <td>0.8857</td>\n      <td>50.0</td>\n      <td>47.3</td>\n      <td>18.1</td>\n      <td>20.4</td>\n      <td>33.5</td>\n      <td>...</td>\n      <td>35.4</td>\n      <td>28.0</td>\n      <td>47.2</td>\n      <td>48.1</td>\n      <td>36.0</td>\n      <td>30.7</td>\n      <td>65.5</td>\n      <td>-0.3</td>\n      <td>10.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 21 columns</p>\n</div>",
                        "text/plain": "    G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  TORD   ORB  ...   FTR  \\\n2  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  14.8  32.7  ...  32.9   \n3  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  18.8  29.9  ...  32.1   \n4  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  18.5  32.2  ...  38.6   \n5  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  20.2  34.1  ...  40.3   \n6  37  23  111.9   93.6   0.8857   50.0   47.3  18.1  20.4  33.5  ...  35.4   \n\n   FTRD  2P_O  2P_D  3P_O  3P_D  ADJ_T   WAB  SEED  windex  \n2  26.0  52.9  46.5  37.4  36.9   65.5   2.3   6.0       0  \n3  33.4  52.6  46.3  40.3  34.7   61.9   8.6   1.0       1  \n4  37.3  52.7  43.4  41.3  32.5   70.1  11.6   1.0       1  \n5  32.0  52.6  46.1  34.4  36.2   69.0   6.7   1.0       0  \n6  28.0  47.2  48.1  36.0  30.7   65.5  -0.3  10.0       0  \n\n[5 rows x 21 columns]"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = df1[['G', 'W', 'ADJOE', 'ADJDE', 'BARTHAG', 'EFG_O', 'EFG_D',\n       'TOR', 'TORD', 'ORB', 'DRB', 'FTR', 'FTRD', '2P_O', '2P_D', '3P_O',\n       '3P_D', 'ADJ_T', 'WAB', 'SEED', 'windex']]\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "What are our lables? Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)|\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['E8', 'E8', 'E8', 'E8', 'F4'], dtype=object)"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "y = df1['POSTSEASON'].values\ny[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## Normalize Data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Data Standardization gives data zero mean and unit variance (technically should be done after train test split )\n"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[-0.43331874, -1.26140173,  0.28034482,  2.74329908, -2.45717765,\n         0.10027963,  0.94171924, -1.16188145, -1.71391372,  0.12750511,\n         1.33368704, -0.4942211 , -0.87998988,  0.02784185,  0.00307239,\n         0.22576157,  1.59744386, -1.12106011, -1.0448016 ,  0.49716104,\n        -0.6882472 ],\n       [ 0.40343468,  0.35874728,  0.64758014, -0.90102957,  1.127076  ,\n         0.39390887,  0.38123706, -1.29466791, -0.03522254, -0.62979797,\n        -1.31585883, -0.68542235,  0.55458056, -0.07167795, -0.0829545 ,\n         1.32677295,  0.65081046, -2.369021  ,  0.98050611, -1.14054592,\n         1.45296631],\n       [ 0.40343468,  1.33083669,  0.87710222, -1.0788017 ,  1.29403598,\n         0.72424177, -1.30020946,  0.49794919, -0.16112438, -0.00772758,\n        -0.27908001,  0.86808783,  1.31063795, -0.03850468, -1.33034432,\n         1.70643205, -0.29582294,  0.47355659,  1.94493836, -1.14054592,\n         1.45296631],\n       [ 0.40343468,  0.68277708,  0.30329703,  0.63966222, -0.04972253,\n        -0.52368251,  0.63600169, -0.63073565,  0.55231938,  0.50615665,\n         0.71929959,  1.2743905 ,  0.28317534, -0.07167795, -0.16898138,\n        -0.91321572,  1.29624232,  0.0922352 ,  0.36969903, -1.14054592,\n        -0.6882472 ],\n       [ 0.40343468, -1.58543153, -1.18859646, -0.13068368, -0.87375079,\n        -1.36786658, -0.17924511,  0.69712887,  0.63625394,  0.34387742,\n         2.56246194,  0.10328282, -0.49226814, -1.8630343 ,  0.69128747,\n        -0.30576117, -1.07034117, -1.12106011, -1.88064288,  1.80732661,\n        -0.6882472 ]])"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## Training and Validation\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Split the data into Training and Validation data.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (44, 21) (44,)\nValidation set: (12, 21) (12,)\n"
                }
            ],
            "source": "# We split the X into train and test to find the best k\nfrom sklearn.model_selection import train_test_split\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Validation set:', X_val.shape,  y_val.shape)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Classification\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Now, it is your turn, use the training set to build an accurate model. Then use the validation set  to report the accuracy of the model\nYou should use the following algorithm:\n\n*   K Nearest Neighbor(KNN)\n*   Decision Tree\n*   Support Vector Machine\n*   Logistic Regression\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# K Nearest Neighbor(KNN)\n\n<b>Question  1 </b> Build a KNN model using a value of k equals five, find the accuracy on the validation data (X_val and y_val)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You can use <code> accuracy_score</cdoe>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.6666666666666666"
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "knn = KNeighborsClassifier(n_neighbors=5)\nknn.fit(X_train,y_train)\nyhatknn = knn.predict(X_val)\naccuracy_score(y_val,yhatknn)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<b>Question  2</b> Determine and print the accuracy for the first 15 values of k on the validation data:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "For k=1 the accuracy is 0.3333333333333333\nFor k=2 the accuracy is 0.3333333333333333\nFor k=3 the accuracy is 0.5\nFor k=4 the accuracy is 0.5833333333333334\nFor k=5 the accuracy is 0.6666666666666666\nFor k=6 the accuracy is 0.5833333333333334\nFor k=7 the accuracy is 0.5833333333333334\nFor k=8 the accuracy is 0.6666666666666666\nFor k=9 the accuracy is 0.5833333333333334\nFor k=10 the accuracy is 0.5833333333333334\nFor k=11 the accuracy is 0.5833333333333334\nFor k=12 the accuracy is 0.5\nFor k=13 the accuracy is 0.5833333333333334\nFor k=14 the accuracy is 0.5833333333333334\nFor k=15 the accuracy is 0.5833333333333334\n"
                }
            ],
            "source": "k_arr = np.arange(1,16,1)\naccuracy_list = []\nfor i in k_arr:\n    knn = KNeighborsClassifier(n_neighbors=i)\n    knn.fit(X_train,y_train)\n    yhatknn = knn.predict(X_val)\n    acc=accuracy_score(y_val,yhatknn)\n    accuracy_list.append(acc)\n\nfor k,acc in zip(k_arr,accuracy_list):\n    print(f\"For k={k} the accuracy is {acc}\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Decision Tree\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The following lines of code fit a <code>DecisionTreeClassifier</code>:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.tree import DecisionTreeClassifier"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<b>Question  3</b> Determine the minumum   value for the parameter <code>max_depth</code> that improves results\n"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Prediction Values:  ['S16' 'S16' 'S16' 'S16' 'S16']\nValidation Values:  ['F4' 'S16' 'S16' 'S16' 'S16']\nDecision Tree Accuraccy Score: 0.6666666666666666\nThe minimun value of \"max_depth\" that improves results is:  1\n"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 864x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "#Create the the instance:\nncaa_tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 1)\n\n#Fit the data:\nncaa_tree.fit(X_train,y_train)\n\n#Prediction:\npred_tree = ncaa_tree.predict(X_val)\nprint(\"Prediction Values: \", pred_tree[0:5])\nprint(\"Validation Values: \", y_val[0:5])\nprint(\"Decision Tree Accuraccy Score:\", accuracy_score(y_val,pred_tree))\n\n#Create a for loop to determine the min value for max_depth iterating through 100 values of max_depth\nmx_depth = 100\naccuracy = np.zeros((mx_depth-1))\n\nd2 = []\nfor n in range(1,mx_depth):\n    #Create the the instance:\n    ncaa_tree2 = DecisionTreeClassifier(criterion=\"entropy\", max_depth = n)\n    #Fit the data:\n    ncaa_tree2.fit(X_train,y_train)\n    pred_tree2 = ncaa_tree2.predict(X_val)\n    accuracy[n-1] = round(accuracy_score(y_val,pred_tree2),2)\n    d2.append({\"Max_depth\":n,\"accuracy\":accuracy[n-1]})\n   \n#Add Max_depth and accuracy values to a dataframe\ndf_acc2 = pd.DataFrame(d2).set_index('Max_depth')\n#Print the minumum value for the parameter max_depth that improves results, that means when we obtain the maximun acuraccy.\nprint('The minimun value of \"max_depth\" that improves results is: ', df_acc2['accuracy'].idxmax())\n\nplt.figure(figsize = (12,8))\nplt.plot(range(1,mx_depth),accuracy,'g')\nplt.legend(('Accuracy ',\"\"))\nplt.ylabel('Accuracy ')\nplt.xlabel('Max_depth values')\nplt.tight_layout()\nplt.ticklabel_format()\nplt.xticks(np.arange(0,104,2))\nplt.yticks(np.arange(0.2,1,0.02))\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Support Vector Machine\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<b>Question  4</b> Train the support  vector machine model and determine the accuracy on the validation data for each kernel. Find the kernel (linear, poly, rbf, sigmoid) that provides the best score on the validation data and train a SVM using it.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn import svm\nfrom sklearn.metrics import classification_report"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "\nKernel Type:  linear\nTrain set Accuracy:  0.9318181818181818\nValidation set Accuracy for kernel:  0.25\n\nKernel Type:  poly\nTrain set Accuracy:  0.75\nValidation set Accuracy for kernel:  0.6666666666666666\n\nKernel Type:  rbf\nTrain set Accuracy:  0.8409090909090909\nValidation set Accuracy for kernel:  0.5833333333333334\n\nKernel Type:  sigmoid\nTrain set Accuracy:  0.6363636363636364\nValidation set Accuracy for kernel:  0.5833333333333334\n"
                }
            ],
            "source": "kernel_list=[\"linear\", \"poly\", \"rbf\", \"sigmoid\"];\naccsvm=[]\nfor i in kernel_list:\n    svmc = svm.SVC(kernel=i)\n    svmc.fit(X_train,y_train)\n    yhatsvm = svmc.predict(X_val)\n    accuracy = accuracy_score(y_val, yhatsvm)\n    accsvm.append(accuracy)\n\n    #Prediction\n    yhat3 = svmc.predict(X_val)\n    \n    #Print Accuracy\n    print(\"\\nKernel Type: \", i)\n    print(\"Train set Accuracy: \", accuracy_score(y_train, svmc.predict(X_train)))\n    print(\"Validation set Accuracy for kernel: \", accuracy_score(y_val, yhat3))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Logistic Regression\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<b>Question 5</b> Train a logistic regression model and determine the accuracy of the validation data (set C=0.01)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.linear_model import LogisticRegression"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "accuracy =  0.5833333333333334\n"
                }
            ],
            "source": "LR_model = LogisticRegression(C=0.01).fit(X_train,y_train)\nyhat = LR_model.predict(X_val)\nprint('accuracy = ', accuracy_score(yhat,y_val))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Model Evaluation using Test set\n"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.metrics import f1_score\n# for f1_score please set the average parameter to 'micro'\nfrom sklearn.metrics import log_loss"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [],
            "source": "def jaccard_index(predictions, true):\n    if (len(predictions) == len(true)):\n        intersect = 0;\n        for x,y in zip(predictions, true):\n            if (x == y):\n                intersect += 1\n        return intersect / (len(predictions) + len(true) - intersect)\n    else:\n        return -1"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<b>Question  5</b> Calculate the  F1 score and Jaccard score for each model from above. Use the Hyperparameter that performed best on the validation data. **For f1\\_score please set the average parameter to 'micro'.**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Load Test set for evaluation\n"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n\n\n  exec(code_obj, self.user_global_ns, self.user_ns)\n"
                },
                {
                    "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>TEAM</th>\n      <th>CONF</th>\n      <th>G</th>\n      <th>W</th>\n      <th>ADJOE</th>\n      <th>ADJDE</th>\n      <th>BARTHAG</th>\n      <th>EFG_O</th>\n      <th>EFG_D</th>\n      <th>TOR</th>\n      <th>...</th>\n      <th>FTRD</th>\n      <th>2P_O</th>\n      <th>2P_D</th>\n      <th>3P_O</th>\n      <th>3P_D</th>\n      <th>ADJ_T</th>\n      <th>WAB</th>\n      <th>POSTSEASON</th>\n      <th>SEED</th>\n      <th>YEAR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>North Carolina</td>\n      <td>ACC</td>\n      <td>40</td>\n      <td>33</td>\n      <td>123.3</td>\n      <td>94.9</td>\n      <td>0.9531</td>\n      <td>52.6</td>\n      <td>48.1</td>\n      <td>15.4</td>\n      <td>...</td>\n      <td>30.4</td>\n      <td>53.9</td>\n      <td>44.6</td>\n      <td>32.7</td>\n      <td>36.2</td>\n      <td>71.7</td>\n      <td>8.6</td>\n      <td>2ND</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Villanova</td>\n      <td>BE</td>\n      <td>40</td>\n      <td>35</td>\n      <td>123.1</td>\n      <td>90.9</td>\n      <td>0.9703</td>\n      <td>56.1</td>\n      <td>46.7</td>\n      <td>16.3</td>\n      <td>...</td>\n      <td>30.0</td>\n      <td>57.4</td>\n      <td>44.1</td>\n      <td>36.2</td>\n      <td>33.9</td>\n      <td>66.7</td>\n      <td>8.9</td>\n      <td>Champions</td>\n      <td>2.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Notre Dame</td>\n      <td>ACC</td>\n      <td>36</td>\n      <td>24</td>\n      <td>118.3</td>\n      <td>103.3</td>\n      <td>0.8269</td>\n      <td>54.0</td>\n      <td>49.5</td>\n      <td>15.3</td>\n      <td>...</td>\n      <td>26.0</td>\n      <td>52.9</td>\n      <td>46.5</td>\n      <td>37.4</td>\n      <td>36.9</td>\n      <td>65.5</td>\n      <td>2.3</td>\n      <td>E8</td>\n      <td>6.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Virginia</td>\n      <td>ACC</td>\n      <td>37</td>\n      <td>29</td>\n      <td>119.9</td>\n      <td>91.0</td>\n      <td>0.9600</td>\n      <td>54.8</td>\n      <td>48.4</td>\n      <td>15.1</td>\n      <td>...</td>\n      <td>33.4</td>\n      <td>52.6</td>\n      <td>46.3</td>\n      <td>40.3</td>\n      <td>34.7</td>\n      <td>61.9</td>\n      <td>8.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Kansas</td>\n      <td>B12</td>\n      <td>37</td>\n      <td>32</td>\n      <td>120.9</td>\n      <td>90.4</td>\n      <td>0.9662</td>\n      <td>55.7</td>\n      <td>45.1</td>\n      <td>17.8</td>\n      <td>...</td>\n      <td>37.3</td>\n      <td>52.7</td>\n      <td>43.4</td>\n      <td>41.3</td>\n      <td>32.5</td>\n      <td>70.1</td>\n      <td>11.6</td>\n      <td>E8</td>\n      <td>1.0</td>\n      <td>2016</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 24 columns</p>\n</div>",
                        "text/plain": "             TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  \\\n0  North Carolina  ACC  40  33  123.3   94.9   0.9531   52.6   48.1  15.4   \n1       Villanova   BE  40  35  123.1   90.9   0.9703   56.1   46.7  16.3   \n2      Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3   \n3        Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1   \n4          Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8   \n\n   ...  FTRD  2P_O  2P_D  3P_O  3P_D  ADJ_T   WAB  POSTSEASON  SEED  YEAR  \n0  ...  30.4  53.9  44.6  32.7  36.2   71.7   8.6         2ND   1.0  2016  \n1  ...  30.0  57.4  44.1  36.2  33.9   66.7   8.9   Champions   2.0  2016  \n2  ...  26.0  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016  \n3  ...  33.4  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016  \n4  ...  37.3  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016  \n\n[5 rows x 24 columns]"
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0120ENv3/Dataset/ML0101EN_EDX_skill_up/basketball_train.csv',error_bad_lines=False)\ntest_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/core/generic.py:6619: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  return self._update_inplace(result)\n"
                },
                {
                    "data": {
                        "text/plain": "array([[-4.08074446e-01, -1.10135297e+00,  3.37365934e-01,\n         2.66479976e+00, -2.46831661e+00,  2.13703245e-01,\n         9.44090550e-01, -1.19216365e+00, -1.64348924e+00,\n         1.45405982e-02,  1.29523097e+00, -6.23533182e-01,\n        -9.31788560e-01,  1.42784371e-01,  1.68876201e-01,\n         2.84500844e-01,  1.62625961e+00, -8.36649260e-01,\n        -9.98500539e-01,  4.84319174e-01, -6.77003200e-01],\n       [ 3.63958290e-01,  3.26326807e-01,  7.03145068e-01,\n        -7.13778644e-01,  1.07370841e+00,  4.82633172e-01,\n         4.77498943e-01, -1.32975879e+00, -6.86193316e-02,\n        -7.35448152e-01, -1.35447914e+00, -8.06829025e-01,\n         3.41737757e-01,  4.96641291e-02,  9.40576311e-02,\n         1.37214061e+00,  6.93854620e-01, -2.00860931e+00,\n         9.80549967e-01, -1.19401460e+00,  1.47709789e+00],\n       [ 3.63958290e-01,  1.18293467e+00,  9.31757027e-01,\n        -8.78587347e-01,  1.23870131e+00,  7.85179340e-01,\n        -9.22275877e-01,  5.27775662e-01, -1.86734575e-01,\n        -1.19385964e-01, -3.17636057e-01,  6.82449703e-01,\n         1.01292055e+00,  8.07042098e-02, -9.90811637e-01,\n         1.74718880e+00, -2.38550367e-01,  6.60855252e-01,\n         1.92295497e+00, -1.19401460e+00,  1.47709789e+00],\n       [ 3.63958290e-01,  6.11862762e-01,  3.60227129e-01,\n         7.14563447e-01, -8.92254236e-02, -3.57772849e-01,\n         6.89586037e-01, -6.41783067e-01,  4.82585136e-01,\n         3.89534973e-01,  6.80805434e-01,  1.07195337e+00,\n         1.00800346e-01,  4.96641291e-02,  1.92390609e-02,\n        -8.40643737e-01,  1.32958529e+00,  3.02756347e-01,\n         3.83693465e-01, -1.19401460e+00, -6.77003200e-01],\n       [ 3.63958290e-01, -1.38688893e+00, -1.12575060e+00,\n         3.92401673e-04, -9.03545224e-01, -1.13094639e+00,\n         1.09073363e-02,  7.34168378e-01,  5.61328631e-01,\n         2.28823098e-01,  2.52408203e+00, -5.07336709e-02,\n        -5.87592258e-01, -1.62650023e+00,  7.67424763e-01,\n        -2.40566627e-01, -1.00142717e+00, -8.36649260e-01,\n        -1.81525154e+00,  1.82698619e+00, -6.77003200e-01]])"
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df['windex'] = np.where(test_df.WAB > 7, 'True', 'False')\ntest_df1 = test_df[test_df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\ntest_Feature = test_df1[['G', 'W', 'ADJOE', 'ADJDE', 'BARTHAG', 'EFG_O', 'EFG_D',\n       'TOR', 'TORD', 'ORB', 'DRB', 'FTR', 'FTRD', '2P_O', '2P_D', '3P_O',\n       '3P_D', 'ADJ_T', 'WAB', 'SEED', 'windex']]\ntest_Feature['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\ntest_X=test_Feature\ntest_X= preprocessing.StandardScaler().fit(test_X).transform(test_X)\ntest_X[0:5]"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['E8', 'E8', 'E8', 'E8', 'F4'], dtype=object)"
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_y = test_df1['POSTSEASON'].values\ntest_y[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "KNN\n"
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Test set Accuracy:  0.6285714285714286\nTest set Jaccard_score:  0.4583333333333333\nTest set F1_score:  0.6285714285714286\n"
                }
            ],
            "source": "k = 5\n#Train Model and Predict  \nkneigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n\n#Predicting\nyhat_eval = kneigh.predict(test_X)\n\n#Accuracy evaluation\nprint(\"Test set Accuracy: \", accuracy_score(test_y, yhat_eval))\nprint(\"Test set Jaccard_score: \", jaccard_index(test_y, yhat_eval))\nprint(\"Test set F1_score: \", f1_score(test_y, yhat_eval, average = 'micro'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Decision Tree\n"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Test set Accuracy:  0.6428571428571429\nTest set Jaccard_score:  0.47368421052631576\nTest set F1_score:  0.6428571428571429\n"
                }
            ],
            "source": "#Create the the instance:\nncaa_tree3 = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 1)\n\n#Fit the data:\nncaa_tree3.fit(X_train,y_train)\n\n#Prediction:\npred_tree3 = ncaa_tree3.predict(test_X)\n#Accuracy evaluation\nprint(\"Test set Accuracy: \", accuracy_score(test_y, pred_tree3))\nprint(\"Test set Jaccard_score: \", jaccard_index(test_y, pred_tree3))\nprint(\"Test set F1_score: \", f1_score(test_y, pred_tree3, average = 'micro'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "SVM\n"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Test set Accuracy:  0.6857142857142857\nTest set Jaccard_score:  0.5217391304347826\nTest set F1_score:  0.6857142857142857\n"
                }
            ],
            "source": "#Creating Instance\nclf3 = svm.SVC(kernel='poly')\n\n#Fitting the training data\nclf3.fit(X_train, y_train)\n\n#Prediction\nyhat_eval2 = clf3.predict(test_X)\n#Print Accuracy \nprint(\"Test set Accuracy: \", accuracy_score(test_y, yhat_eval2))\nprint(\"Test set Jaccard_score: \", jaccard_index(test_y, yhat_eval2))\nprint(\"Test set F1_score: \", f1_score(test_y, yhat_eval2, average = 'micro'))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Logistic Regression\n"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Test set Accuracy:  0.6857142857142857\nTest set Jaccard_score:  0.5217391304347826\nTest set F1_score:  0.6857142857142857\nTest set LogLoss:  1.03718699059278\n"
                }
            ],
            "source": "#Training the algorithm\nLR2 = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n\n#Predicting\nyhat_eval3 = LR2.predict(test_X)\nyhat_eval4 = LR2.predict_proba(test_X)\n\n#Print Accuracy \nprint(\"Test set Accuracy: \", accuracy_score(test_y, yhat_eval3))\nprint(\"Test set Jaccard_score: \", jaccard_index(test_y, yhat_eval3))\nprint(\"Test set F1_score: \", f1_score(test_y, yhat_eval3, average = 'micro'))\nprint(\"Test set LogLoss: \", log_loss(test_y, yhat_eval4))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Report\n\nYou should be able to report the accuracy of the built model using different evaluation metrics:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Algorithm           | Accuracy | Jaccard | F1-score | LogLoss |\n| ------------------- | -------- | ------- | -------- | ------- |\n| KNN                 | 0.629    | 0.458   | 0.629    | NA      |\n| Decision Tree       | 0.643    | 0.474   | 0.643    | NA      |\n| SVM                 | 0.686    | 0.522   | 0.686    | NA      |\n| Logistic Regression | 0.686    | 0.522   | 0.686    | 1.037   |"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Something to keep in mind when creating models to predict the results of basketball tournaments or sports in general is that is quite hard due to so many factors influencing the game. Even in sports betting an accuracy of 55% and over is considered good as it indicates profits.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Thank you for completing this lab!\n\n## Author\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n| ----------------- | ------- | ------------- | ---------------------------------- |\n| 2021-04-03        | 2.1     | Malika Singla | Updated the Report accuracy        |\n| 2020-08-27        | 2.0     | Lavanya       | Moved lab to course repo in GitLab |\n|                   |         |               |                                    |\n|                   |         |               |                                    |\n\n## <h3 align=\"center\"> \u00a9 IBM Corporation 2020. All rights reserved. <h3/>\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.9",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.9.7"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}