DS-8-Assignment.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n","    </a>\n","</p>\n","\n","# Classification with Python\n","\n","Estimated time needed: **25** minutes\n","\n","## Objectives\n","\n","After 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","\n","We load a dataset using Pandas library, apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n","\n","Let's first load required libraries:\n"]},{"cell_type":"code","execution_count":211,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[],"source":["import itertools\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.ticker import NullFormatter\n","import pandas as pd\n","import numpy as np\n","import matplotlib.ticker as ticker\n","from 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":212,"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 × 24 columns</p>\n","</div>"],"text/plain":["             TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  \\\n","0  North Carolina  ACC  40  33  123.3   94.9   0.9531   52.6   48.1  15.4   \n","1       Villanova   BE  40  35  123.1   90.9   0.9703   56.1   46.7  16.3   \n","2      Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3   \n","3        Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1   \n","4          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  \n","0  ...  30.4  53.9  44.6  32.7  36.2   71.7   8.6         2ND   1.0  2016  \n","1  ...  30.0  57.4  44.1  36.2  33.9   66.7   8.9   Champions   2.0  2016  \n","2  ...  26.0  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016  \n","3  ...  33.4  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016  \n","4  ...  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":212,"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')\n","df.head()"]},{"cell_type":"code","execution_count":213,"metadata":{},"outputs":[{"data":{"text/plain":["(1406, 24)"]},"execution_count":213,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{},"source":["## Add Column\n","\n","Next 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":214,"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":215,"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 × 25 columns</p>\n","</div>"],"text/plain":["         TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  ...  \\\n","2  Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  ...   \n","3    Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  ...   \n","4      Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  ...   \n","5      Oregon  P12  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  ...   \n","6    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  \n","2  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016   False  \n","3  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016    True  \n","4  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016    True  \n","5  52.6  46.1  34.4  36.2   69.0   6.7          E8   1.0  2016   False  \n","6  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":215,"metadata":{},"output_type":"execute_result"}],"source":["df1 = df.loc[df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\n","df1.head()"]},{"cell_type":"code","execution_count":216,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["S16    32\n","E8     16\n","F4      8\n","Name: POSTSEASON, dtype: int64"]},"execution_count":216,"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":217,"metadata":{},"outputs":[],"source":["# notice: installing seaborn might takes a few minutes\n","#!conda install -c anaconda seaborn -y"]},{"cell_type":"code","execution_count":218,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1800x300 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import seaborn as sns\n","\n","bins = np.linspace(df1.BARTHAG.min(), df1.BARTHAG.max(), 10)\n","g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=6)\n","g.map(plt.hist, 'BARTHAG', bins=bins, ec=\"k\")\n","\n","g.axes[-1].legend()\n","plt.show()"]},{"cell_type":"code","execution_count":219,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x300 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["bins = np.linspace(df1.ADJOE.min(), df1.ADJOE.max(), 10)\n","g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\n","g.map(plt.hist, 'ADJOE', bins=bins, ec=\"k\")\n","\n","g.axes[-1].legend()\n","plt.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":220,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x300 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["bins = np.linspace(df1.ADJDE.min(), df1.ADJDE.max(), 10)\n","g = sns.FacetGrid(df1, col=\"windex\", hue=\"POSTSEASON\", palette=\"Set1\", col_wrap=2)\n","g.map(plt.hist, 'ADJDE', bins=bins, ec=\"k\")\n","g.axes[-1].legend()\n","plt.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":221,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["windex  POSTSEASON\n","False   S16           0.605263\n","        E8            0.263158\n","        F4            0.131579\n","True    S16           0.500000\n","        E8            0.333333\n","        F4            0.166667\n","Name: POSTSEASON, dtype: float64"]},"execution_count":221,"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":222,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\SummerRay\\AppData\\Local\\Temp\\ipykernel_15748\\1994287662.py:1: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  df1['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\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 × 25 columns</p>\n","</div>"],"text/plain":["         TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  ...  \\\n","2  Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  ...   \n","3    Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  ...   \n","4      Kansas  B12  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  ...   \n","5      Oregon  P12  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  ...   \n","6    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  \n","2  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016       0  \n","3  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016       1  \n","4  52.7  43.4  41.3  32.5   70.1  11.6          E8   1.0  2016       1  \n","5  52.6  46.1  34.4  36.2   69.0   6.7          E8   1.0  2016       0  \n","6  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":222,"metadata":{},"output_type":"execute_result"}],"source":["df1['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\n","df1.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":223,"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 × 21 columns</p>\n","</div>"],"text/plain":["    G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  TORD   ORB  ...   FTR  \\\n","2  36  24  118.3  103.3   0.8269   54.0   49.5  15.3  14.8  32.7  ...  32.9   \n","3  37  29  119.9   91.0   0.9600   54.8   48.4  15.1  18.8  29.9  ...  32.1   \n","4  37  32  120.9   90.4   0.9662   55.7   45.1  17.8  18.5  32.2  ...  38.6   \n","5  37  30  118.4   96.2   0.9163   52.3   48.9  16.1  20.2  34.1  ...  40.3   \n","6  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  \n","2  26.0  52.9  46.5  37.4  36.9   65.5   2.3   6.0       0  \n","3  33.4  52.6  46.3  40.3  34.7   61.9   8.6   1.0       1  \n","4  37.3  52.7  43.4  41.3  32.5   70.1  11.6   1.0       1  \n","5  32.0  52.6  46.1  34.4  36.2   69.0   6.7   1.0       0  \n","6  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":223,"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']]\n","X[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":224,"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":224,"metadata":{},"output_type":"execute_result"}],"source":["y = df1['POSTSEASON'].values\n","y[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":225,"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":225,"metadata":{},"output_type":"execute_result"}],"source":["X= preprocessing.StandardScaler().fit(X).transform(X)\n","X[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":226,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["Train set: (44, 21) (44,)\n","Validation set: (12, 21) (12,)\n"]}],"source":["# We split the X into train and test to find the best k\n","from sklearn.model_selection import train_test_split\n","X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=4)\n","print ('Train set:', X_train.shape,  y_train.shape)\n","print ('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\n","You 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":227,"metadata":{},"outputs":[],"source":["from sklearn.metrics import accuracy_score\n","from sklearn.neighbors import KNeighborsClassifier"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["neighbor_model = KNeighborsClassifier(n_neighbors=5)\n","neighbor_model.fit(X_train, y_train)\n","\n","y_predicted = neighbor_model.predict(X_val)\n","\n","acc_score_neighbor = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","print(acc_score_neighbor)"]},{"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":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy score for k = 1 is: 0.3333333333333333\n","Accuracy score for k = 2 is: 0.3333333333333333\n","Accuracy score for k = 3 is: 0.5\n","Accuracy score for k = 4 is: 0.5833333333333334\n","Accuracy score for k = 5 is: 0.6666666666666666\n","Accuracy score for k = 6 is: 0.5833333333333334\n","Accuracy score for k = 7 is: 0.5833333333333334\n","Accuracy score for k = 8 is: 0.6666666666666666\n","Accuracy score for k = 9 is: 0.5833333333333334\n","Accuracy score for k = 10 is: 0.5833333333333334\n","Accuracy score for k = 11 is: 0.5833333333333334\n","Accuracy score for k = 12 is: 0.5\n","Accuracy score for k = 13 is: 0.5833333333333334\n","Accuracy score for k = 14 is: 0.5833333333333334\n","Accuracy score for k = 15 is: 0.5833333333333334\n"]}],"source":["for k in range(1,16):\n","    neighbor_model = KNeighborsClassifier(n_neighbors=k)\n","    \n","    neighbor_model.fit(X_train, y_train)\n","\n","    y_predicted = neighbor_model.predict(X_val)\n","\n","    acc_score = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","    print(f'Accuracy score for k = {k} is: {acc_score}')"]},{"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":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.4166666666666667\n"]}],"source":["from sklearn.tree import DecisionTreeClassifier\n","from sklearn import tree"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["tree_model = DecisionTreeClassifier()\n","tree_model.fit(X_train, y_train)\n","\n","y_predicted = tree_model.predict(X_val)\n","\n","acc_score_tree = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","print(acc_score_tree)"]},{"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":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy score for depth = 1 is: 0.6666666666666666\n","Accuracy score for depth = 2 is: 0.3333333333333333\n","Accuracy score for depth = 3 is: 0.4166666666666667\n","Accuracy score for depth = 4 is: 0.4166666666666667\n","Accuracy score for depth = 5 is: 0.4166666666666667\n","Accuracy score for depth = 6 is: 0.4166666666666667\n","Accuracy score for depth = 7 is: 0.4166666666666667\n","Accuracy score for depth = 8 is: 0.5\n","Accuracy score for depth = 9 is: 0.4166666666666667\n"]}],"source":["for depth in range(1,10):\n","    tree_model = DecisionTreeClassifier(max_depth=depth)\n","    \n","    tree_model.fit(X_train, y_train)\n","\n","    y_predicted = tree_model.predict(X_val)\n","\n","    acc_score = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","    print(f'Accuracy score for depth = {depth} is: {acc_score}')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6666666666666666\n"]}],"source":["tree_model = DecisionTreeClassifier(max_depth=1)\n","tree_model.fit(X_train, y_train)\n","\n","y_predicted = tree_model.predict(X_val)\n","\n","acc_score_tree = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","print(acc_score_tree)"]},{"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":null,"metadata":{},"outputs":[],"source":["from sklearn import svm"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;poly&#x27;, random_state=18)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;poly&#x27;, random_state=18)</pre></div></div></div></div></div>"],"text/plain":["SVC(kernel='poly', random_state=18)"]},"execution_count":183,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for kern in ['linear', 'poly', 'rbf', 'sigmoid']:\n","    svm_model = svm.SVC(kernel=kern)\n","    \n","    svm_model.fit(X_train, y_train)\n","    \n","    y_predicted = svm_model.predict(X_val)\n","    \n","    acc_score = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","    print(f'Accuracy score for kernel = {kern} is: {acc_score}')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6666666666666666\n"]}],"source":["svm_model = svm.SVC(kernel='poly')\n","    \n","svm_model.fit(X_train, y_train)\n","    \n","y_predicted = svm_model.predict(X_val)\n","    \n","acc_score_svm = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","print(acc_score_svm)"]},{"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":null,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import LogisticRegression"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.5833333333333334\n"]}],"source":["lr = LogisticRegression(C=0.01)\n","lr.fit(X_train, y_train)\n","\n","y_predicted =lr.predict(X_val)\n","\n","acc_score_log_reg = accuracy_score(y_true=y_val, y_pred=y_predicted)\n","print(acc_score_log_reg)"]},{"cell_type":"markdown","metadata":{},"source":["# Model Evaluation using Test set\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.metrics import f1_score\n","# for f1_score please set the average parameter to 'micro'\n","from sklearn.metrics import log_loss"]},{"cell_type":"code","execution_count":null,"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":null,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\SummerRay\\AppData\\Local\\Temp\\ipykernel_15748\\4078779282.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n","\n","\n","  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)\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 × 24 columns</p>\n","</div>"],"text/plain":["             TEAM CONF   G   W  ADJOE  ADJDE  BARTHAG  EFG_O  EFG_D   TOR  \\\n","0  North Carolina  ACC  40  33  123.3   94.9   0.9531   52.6   48.1  15.4   \n","1       Villanova   BE  40  35  123.1   90.9   0.9703   56.1   46.7  16.3   \n","2      Notre Dame  ACC  36  24  118.3  103.3   0.8269   54.0   49.5  15.3   \n","3        Virginia  ACC  37  29  119.9   91.0   0.9600   54.8   48.4  15.1   \n","4          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  \n","0  ...  30.4  53.9  44.6  32.7  36.2   71.7   8.6         2ND   1.0  2016  \n","1  ...  30.0  57.4  44.1  36.2  33.9   66.7   8.9   Champions   2.0  2016  \n","2  ...  26.0  52.9  46.5  37.4  36.9   65.5   2.3          E8   6.0  2016  \n","3  ...  33.4  52.6  46.3  40.3  34.7   61.9   8.6          E8   1.0  2016  \n","4  ...  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":188,"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)\n","test_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\SummerRay\\AppData\\Local\\Temp\\ipykernel_15748\\3609998696.py:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  test_Feature['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\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":189,"metadata":{},"output_type":"execute_result"}],"source":["test_df['windex'] = np.where(test_df.WAB > 7, 'True', 'False')\n","test_df1 = test_df[test_df['POSTSEASON'].str.contains('F4|S16|E8', na=False)]\n","test_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']]\n","test_Feature['windex'].replace(to_replace=['False','True'], value=[0,1],inplace=True)\n","test_X=test_Feature\n","test_X= preprocessing.StandardScaler().fit(test_X).transform(test_X)\n","test_X[0:5]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["array(['E8', 'E8', 'E8', 'E8', 'F4'], dtype=object)"]},"execution_count":190,"metadata":{},"output_type":"execute_result"}],"source":["test_y = test_df1['POSTSEASON'].values\n","test_y[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["KNN\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6285714285714286\n","0.4583333333333333\n"]}],"source":["knn_model = KNeighborsClassifier(n_neighbors=5)\n","knn_model.fit(X_train, y_train)\n","knn_yhat2 = knn_model.predict(test_X)\n","knn_jacc = jaccard_index(knn_yhat2, test_y)\n","knn_f1 = f1_score(test_y, knn_yhat2, average='micro')\n","knn_acc = accuracy_score(test_y, knn_yhat2)\n","print(knn_f1)\n","print(knn_jacc)\n","#print(knn_acc)\n","\n","knn_list = ['knn', knn_jacc, knn_f1, knn_acc]"]},{"cell_type":"markdown","metadata":{},"source":["Decision Tree\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6428571428571429\n","0.47368421052631576\n"]}],"source":["tree_model = DecisionTreeClassifier(max_depth=1)\n","tree_model.fit(X_train, y_train)\n","y_predicted = tree_model.predict(test_X)\n","f1_score_value_tree = f1_score(y_true=test_y, y_pred=y_predicted, average='micro')\n","print(f1_score_value_tree)\n","jaccard_index_value_tree = jaccard_index(predictions=y_predicted, true=test_y)\n","print(jaccard_index_value_tree)"]},{"cell_type":"markdown","metadata":{},"source":["SVM\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6857142857142857\n","0.5217391304347826\n"]}],"source":["svm_model = svm.SVC(kernel='poly')\n","svm_model.fit(X_train, y_train)\n","y_predicted = svm_model.predict(test_X)\n","f1_score_value_svm = f1_score(y_true=test_y, y_pred=y_predicted, average='micro')\n","print(f1_score_value_svm)\n","jaccard_index_value_svm = jaccard_index(predictions=y_predicted, true=test_y)\n","print(jaccard_index_value_svm)"]},{"cell_type":"markdown","metadata":{},"source":["Logistic Regression\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.5714285714285714\n","0.4\n"]}],"source":["log_reg_model = LogisticRegression(C=0.01)\n","log_reg_model.fit(X_train, y_train)\n","y_predicted = log_reg_model.predict(test_X)\n","f1_score_value_log_reg = f1_score(y_true=test_y, y_pred=y_predicted, average='micro')\n","print(f1_score_value_log_reg)\n","jaccard_index_value_log_reg = jaccard_index(predictions=y_predicted, true=test_y)\n","print(jaccard_index_value_log_reg)"]},{"cell_type":"markdown","metadata":{},"source":["# Report\n","\n","You 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                | ?        | ?       | ?        | NA      |\n","| Decision Tree      | ?        | ?       | ?        | NA      |\n","| SVM                | ?        | ?       | ?        | NA      |\n","| LogisticRegression | ?        | ?       | ?        | ?       |\n"]},{"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","\n","IBM 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 – 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","\n","Also, 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","\n","Saeed 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\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3.10.7 64-bit","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":"3.10.7"},"vscode":{"interpreter":{"hash":"ba40e4634ddf875ceb6a9d7971afc7319e26bc317ef08ce7a853d2c77b90af89"}}},"nbformat":4,"nbformat_minor":4}