Predicting outcome of IPL match.ipynb

{"cells":[{"metadata":{"_cell_guid":"994e6f34-f52d-4301-be75-24bcdb62773d","_uuid":"e57ea06f08c82eb7e3722da4a3fa8340aea76fcd","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[{"output_type":"stream","text":"ipl\n\n","name":"stdout"}]},{"metadata":{"_cell_guid":"bcb2c4e3-9b50-401a-820d-86516ab627e0","_uuid":"443ae25c196175a0294a9aa4fd33a694beb83b9d","trusted":true},"cell_type":"code","source":"#here is our dataset\nmatches=pd.read_csv('../input/ipl/matches.csv')\nmatches.info()","execution_count":2,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 636 entries, 0 to 635\nData columns (total 18 columns):\n #   Column           Non-Null Count  Dtype  \n---  ------           --------------  -----  \n 0   id               636 non-null    int64  \n 1   season           636 non-null    int64  \n 2   city             629 non-null    object \n 3   date             636 non-null    object \n 4   team1            636 non-null    object \n 5   team2            636 non-null    object \n 6   toss_winner      636 non-null    object \n 7   toss_decision    636 non-null    object \n 8   result           636 non-null    object \n 9   dl_applied       636 non-null    int64  \n 10  winner           633 non-null    object \n 11  win_by_runs      636 non-null    int64  \n 12  win_by_wickets   636 non-null    int64  \n 13  player_of_match  633 non-null    object \n 14  venue            636 non-null    object \n 15  umpire1          635 non-null    object \n 16  umpire2          635 non-null    object \n 17  umpire3          0 non-null      float64\ndtypes: float64(1), int64(5), object(12)\nmemory usage: 89.6+ KB\n","name":"stdout"}]},{"metadata":{"_cell_guid":"4ce2fb0f-a10a-4c82-a4b8-fddcdbcaf25f","_uuid":"7fb66312d6307f21e2e8e27294e0b3b25679d4dd","trusted":true},"cell_type":"code","source":"matches[pd.isnull(matches['winner'])]","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"      id  season       city        date                        team1  \\\n300  301    2011      Delhi  2011-05-21             Delhi Daredevils   \n545  546    2015  Bangalore  2015-04-29  Royal Challengers Bangalore   \n570  571    2015  Bangalore  2015-05-17             Delhi Daredevils   \n\n                           team2                  toss_winner toss_decision  \\\n300                Pune Warriors             Delhi Daredevils           bat   \n545             Rajasthan Royals             Rajasthan Royals         field   \n570  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n\n        result  dl_applied winner  win_by_runs  win_by_wickets  \\\n300  no result           0    NaN            0               0   \n545  no result           0    NaN            0               0   \n570  no result           0    NaN            0               0   \n\n    player_of_match                  venue          umpire1       umpire2  \\\n300             NaN       Feroz Shah Kotla        SS Hazare     RJ Tucker   \n545             NaN  M Chinnaswamy Stadium        JD Cloete     PG Pathak   \n570             NaN  M Chinnaswamy Stadium  HDPK Dharmasena  K Srinivasan   \n\n     umpire3  \n300      NaN  \n545      NaN  \n570      NaN  ","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>id</th>\n      <th>season</th>\n      <th>city</th>\n      <th>date</th>\n      <th>team1</th>\n      <th>team2</th>\n      <th>toss_winner</th>\n      <th>toss_decision</th>\n      <th>result</th>\n      <th>dl_applied</th>\n      <th>winner</th>\n      <th>win_by_runs</th>\n      <th>win_by_wickets</th>\n      <th>player_of_match</th>\n      <th>venue</th>\n      <th>umpire1</th>\n      <th>umpire2</th>\n      <th>umpire3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>300</th>\n      <td>301</td>\n      <td>2011</td>\n      <td>Delhi</td>\n      <td>2011-05-21</td>\n      <td>Delhi Daredevils</td>\n      <td>Pune Warriors</td>\n      <td>Delhi Daredevils</td>\n      <td>bat</td>\n      <td>no result</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>Feroz Shah Kotla</td>\n      <td>SS Hazare</td>\n      <td>RJ Tucker</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>545</th>\n      <td>546</td>\n      <td>2015</td>\n      <td>Bangalore</td>\n      <td>2015-04-29</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>Rajasthan Royals</td>\n      <td>Rajasthan Royals</td>\n      <td>field</td>\n      <td>no result</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>JD Cloete</td>\n      <td>PG Pathak</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>570</th>\n      <td>571</td>\n      <td>2015</td>\n      <td>Bangalore</td>\n      <td>2015-05-17</td>\n      <td>Delhi Daredevils</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>Royal Challengers Bangalore</td>\n      <td>field</td>\n      <td>no result</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>HDPK Dharmasena</td>\n      <td>K Srinivasan</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"e0f4b1e8-c456-4326-916e-d2c31a5ed1b1","_uuid":"554b5faf595dadc27311566b5c4d2a3c2e38d301","trusted":true},"cell_type":"code","source":"matches['winner'].fillna('Draw', inplace=True)","execution_count":4,"outputs":[]},{"metadata":{"_cell_guid":"3379288c-b75b-49bf-ba1f-cb587711fb2d","_uuid":"50eadf5ec6d3067761775e5287f5c65afdbc2ec7","scrolled":true,"trusted":true},"cell_type":"code","source":"#here we are replacing the name with shortnames...\nmatches.replace(['Mumbai Indians','Kolkata Knight Riders','Royal Challengers Bangalore','Deccan Chargers','Chennai Super Kings',\n                 'Rajasthan Royals','Delhi Daredevils','Gujarat Lions','Kings XI Punjab',\n                 'Sunrisers Hyderabad','Rising Pune Supergiants','Rising Pune Supergiant','Kochi Tuskers Kerala','Pune Warriors']\n                ,['MI','KKR','RCB','DC','CSK','RR','DD','GL','KXIP','SRH','RPS','RPS','KTK','PW'],inplace=True)\n\n#here we are encoding that short name to numbers...\nencode = {'team1': {'MI':1,'KKR':2,'RCB':3,'DC':4,'CSK':5,'RR':6,'DD':7,'GL':8,'KXIP':9,'SRH':10,'RPS':11,'KTK':12,'PW':13},\n          'team2': {'MI':1,'KKR':2,'RCB':3,'DC':4,'CSK':5,'RR':6,'DD':7,'GL':8,'KXIP':9,'SRH':10,'RPS':11,'KTK':12,'PW':13},\n          'toss_winner': {'MI':1,'KKR':2,'RCB':3,'DC':4,'CSK':5,'RR':6,'DD':7,'GL':8,'KXIP':9,'SRH':10,'RPS':11,'KTK':12,'PW':13},\n          'winner': {'MI':1,'KKR':2,'RCB':3,'DC':4,'CSK':5,'RR':6,'DD':7,'GL':8,'KXIP':9,'SRH':10,'RPS':11,'KTK':12,'PW':13,'Draw':14}}\nmatches.replace(encode, inplace=True)\nmatches.head(2)","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"   id  season       city        date  team1  team2  toss_winner toss_decision  \\\n0   1    2017  Hyderabad  2017-04-05     10      3            3         field   \n1   2    2017       Pune  2017-04-06      1     11           11         field   \n\n   result  dl_applied  winner  win_by_runs  win_by_wickets player_of_match  \\\n0  normal           0      10           35               0    Yuvraj Singh   \n1  normal           0      11            0               7       SPD Smith   \n\n                                       venue         umpire1   umpire2  \\\n0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar  NJ Llong   \n1    Maharashtra Cricket Association Stadium  A Nand Kishore    S Ravi   \n\n   umpire3  \n0      NaN  \n1      NaN  ","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>id</th>\n      <th>season</th>\n      <th>city</th>\n      <th>date</th>\n      <th>team1</th>\n      <th>team2</th>\n      <th>toss_winner</th>\n      <th>toss_decision</th>\n      <th>result</th>\n      <th>dl_applied</th>\n      <th>winner</th>\n      <th>win_by_runs</th>\n      <th>win_by_wickets</th>\n      <th>player_of_match</th>\n      <th>venue</th>\n      <th>umpire1</th>\n      <th>umpire2</th>\n      <th>umpire3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2017</td>\n      <td>Hyderabad</td>\n      <td>2017-04-05</td>\n      <td>10</td>\n      <td>3</td>\n      <td>3</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>10</td>\n      <td>35</td>\n      <td>0</td>\n      <td>Yuvraj Singh</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>AY Dandekar</td>\n      <td>NJ Llong</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2017</td>\n      <td>Pune</td>\n      <td>2017-04-06</td>\n      <td>1</td>\n      <td>11</td>\n      <td>11</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0</td>\n      <td>7</td>\n      <td>SPD Smith</td>\n      <td>Maharashtra Cricket Association Stadium</td>\n      <td>A Nand Kishore</td>\n      <td>S Ravi</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"5a5effe0-0045-4c07-a6b5-c1865ba8ae27","_uuid":"09ccf7d5f02e7f7d56029ea7f86968de5f6222dc","trusted":true},"cell_type":"code","source":"#Find cities which are null\nmatches[pd.isnull(matches['city'])]","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"      id  season city        date  team1  team2  toss_winner toss_decision  \\\n461  462    2014  NaN  2014-04-19      1      3            3         field   \n462  463    2014  NaN  2014-04-19      2      7            2           bat   \n466  467    2014  NaN  2014-04-23      5      6            6         field   \n468  469    2014  NaN  2014-04-25     10      7           10           bat   \n469  470    2014  NaN  2014-04-25      1      5            1           bat   \n474  475    2014  NaN  2014-04-28      3      9            9         field   \n476  477    2014  NaN  2014-04-30     10      1            1         field   \n\n     result  dl_applied  winner  win_by_runs  win_by_wickets player_of_match  \\\n461  normal           0       3            0               7        PA Patel   \n462  normal           0       7            0               4       JP Duminy   \n466  normal           0       5            7               0       RA Jadeja   \n468  normal           0      10            4               0        AJ Finch   \n469  normal           0       5            0               7       MM Sharma   \n474  normal           0       9            0               5  Sandeep Sharma   \n476  normal           0      10           15               0         B Kumar   \n\n                                   venue          umpire1         umpire2  \\\n461  Dubai International Cricket Stadium        Aleem Dar    AK Chaudhary   \n462  Dubai International Cricket Stadium        Aleem Dar     VA Kulkarni   \n466  Dubai International Cricket Stadium  HDPK Dharmasena  RK Illingworth   \n468  Dubai International Cricket Stadium        M Erasmus          S Ravi   \n469  Dubai International Cricket Stadium        BF Bowden       M Erasmus   \n474  Dubai International Cricket Stadium        BF Bowden          S Ravi   \n476  Dubai International Cricket Stadium  HDPK Dharmasena       M Erasmus   \n\n     umpire3  \n461      NaN  \n462      NaN  \n466      NaN  \n468      NaN  \n469      NaN  \n474      NaN  \n476      NaN  ","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>id</th>\n      <th>season</th>\n      <th>city</th>\n      <th>date</th>\n      <th>team1</th>\n      <th>team2</th>\n      <th>toss_winner</th>\n      <th>toss_decision</th>\n      <th>result</th>\n      <th>dl_applied</th>\n      <th>winner</th>\n      <th>win_by_runs</th>\n      <th>win_by_wickets</th>\n      <th>player_of_match</th>\n      <th>venue</th>\n      <th>umpire1</th>\n      <th>umpire2</th>\n      <th>umpire3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>461</th>\n      <td>462</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-19</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>7</td>\n      <td>PA Patel</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>Aleem Dar</td>\n      <td>AK Chaudhary</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>462</th>\n      <td>463</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-19</td>\n      <td>2</td>\n      <td>7</td>\n      <td>2</td>\n      <td>bat</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>4</td>\n      <td>JP Duminy</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>Aleem Dar</td>\n      <td>VA Kulkarni</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>466</th>\n      <td>467</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>6</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>5</td>\n      <td>7</td>\n      <td>0</td>\n      <td>RA Jadeja</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>HDPK Dharmasena</td>\n      <td>RK Illingworth</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>468</th>\n      <td>469</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-25</td>\n      <td>10</td>\n      <td>7</td>\n      <td>10</td>\n      <td>bat</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>0</td>\n      <td>AJ Finch</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>M Erasmus</td>\n      <td>S Ravi</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>469</th>\n      <td>470</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-25</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>bat</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>7</td>\n      <td>MM Sharma</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>BF Bowden</td>\n      <td>M Erasmus</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>475</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-28</td>\n      <td>3</td>\n      <td>9</td>\n      <td>9</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>5</td>\n      <td>Sandeep Sharma</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>BF Bowden</td>\n      <td>S Ravi</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>477</td>\n      <td>2014</td>\n      <td>NaN</td>\n      <td>2014-04-30</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1</td>\n      <td>field</td>\n      <td>normal</td>\n      <td>0</td>\n      <td>10</td>\n      <td>15</td>\n      <td>0</td>\n      <td>B Kumar</td>\n      <td>Dubai International Cricket Stadium</td>\n      <td>HDPK Dharmasena</td>\n      <td>M Erasmus</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"ecb32737-0f39-476b-b52d-f74cf5eab927","_uuid":"889aec3128d425cf7e99714c570c164b069dfaf3","trusted":true},"cell_type":"code","source":"#remove any null values, winner has hence fill the null value in winner as draw\n#City is also null inthat case we will fill it with dubai\nmatches['city'].fillna('Dubai',inplace=True)\nmatches.describe()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"               id       season       team1       team2  toss_winner  \\\ncount  636.000000   636.000000  636.000000  636.000000   636.000000   \nmean   318.500000  2012.490566    5.540881    5.511006     5.371069   \nstd    183.741666     2.773026    3.329169    3.341677     3.293140   \nmin      1.000000  2008.000000    1.000000    1.000000     1.000000   \n25%    159.750000  2010.000000    3.000000    3.000000     2.000000   \n50%    318.500000  2012.000000    5.000000    5.000000     5.000000   \n75%    477.250000  2015.000000    9.000000    8.000000     7.250000   \nmax    636.000000  2017.000000   13.000000   13.000000    13.000000   \n\n       dl_applied      winner  win_by_runs  win_by_wickets  umpire3  \ncount  636.000000  636.000000   636.000000      636.000000      0.0  \nmean     0.025157    5.309748    13.682390        3.372642      NaN  \nstd      0.156726    3.288726    23.908877        3.420338      NaN  \nmin      0.000000    1.000000     0.000000        0.000000      NaN  \n25%      0.000000    2.000000     0.000000        0.000000      NaN  \n50%      0.000000    5.000000     0.000000        4.000000      NaN  \n75%      0.000000    8.000000    20.000000        7.000000      NaN  \nmax      1.000000   14.000000   146.000000       10.000000      NaN  ","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>id</th>\n      <th>season</th>\n      <th>team1</th>\n      <th>team2</th>\n      <th>toss_winner</th>\n      <th>dl_applied</th>\n      <th>winner</th>\n      <th>win_by_runs</th>\n      <th>win_by_wickets</th>\n      <th>umpire3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>318.500000</td>\n      <td>2012.490566</td>\n      <td>5.540881</td>\n      <td>5.511006</td>\n      <td>5.371069</td>\n      <td>0.025157</td>\n      <td>5.309748</td>\n      <td>13.682390</td>\n      <td>3.372642</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>183.741666</td>\n      <td>2.773026</td>\n      <td>3.329169</td>\n      <td>3.341677</td>\n      <td>3.293140</td>\n      <td>0.156726</td>\n      <td>3.288726</td>\n      <td>23.908877</td>\n      <td>3.420338</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>2008.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>159.750000</td>\n      <td>2010.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>318.500000</td>\n      <td>2012.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>0.000000</td>\n      <td>5.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>477.250000</td>\n      <td>2015.000000</td>\n      <td>9.000000</td>\n      <td>8.000000</td>\n      <td>7.250000</td>\n      <td>0.000000</td>\n      <td>8.000000</td>\n      <td>20.000000</td>\n      <td>7.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>636.000000</td>\n      <td>2017.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>1.000000</td>\n      <td>14.000000</td>\n      <td>146.000000</td>\n      <td>10.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"3b8b05ed-f20e-4713-9d2e-3daf430eceb6","_uuid":"30ab2dcb21ad5f951be6c56c25b5166d6c388692","trusted":true},"cell_type":"code","source":"dicVal = encode['winner']\nprint(dicVal['MI']) #key value\nprint(list(dicVal.keys())[list(dicVal.values()).index(2)]) #find key by value search ","execution_count":8,"outputs":[{"output_type":"stream","text":"1\nKKR\n","name":"stdout"}]},{"metadata":{"_cell_guid":"41a6cbb9-0588-43b8-928d-ab5b7de43b9b","_uuid":"a789672d66e938d717c3e2f22d8be3263666afd2","trusted":true},"cell_type":"code","source":"matches = matches[['team1','team2','city','toss_decision','toss_winner','venue','winner']]\nmatches.head(6)","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"   team1  team2       city toss_decision  toss_winner  \\\n0     10      3  Hyderabad         field            3   \n1      1     11       Pune         field           11   \n2      8      2     Rajkot         field            2   \n3     11      9     Indore         field            9   \n4      3      7  Bangalore           bat            3   \n5      8     10  Hyderabad         field           10   \n\n                                       venue  winner  \n0  Rajiv Gandhi International Stadium, Uppal      10  \n1    Maharashtra Cricket Association Stadium      11  \n2     Saurashtra Cricket Association Stadium       2  \n3                     Holkar Cricket Stadium       9  \n4                      M Chinnaswamy Stadium       3  \n5  Rajiv Gandhi International Stadium, Uppal      10  ","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>team1</th>\n      <th>team2</th>\n      <th>city</th>\n      <th>toss_decision</th>\n      <th>toss_winner</th>\n      <th>venue</th>\n      <th>winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>3</td>\n      <td>Hyderabad</td>\n      <td>field</td>\n      <td>3</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>11</td>\n      <td>Pune</td>\n      <td>field</td>\n      <td>11</td>\n      <td>Maharashtra Cricket Association Stadium</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>2</td>\n      <td>Rajkot</td>\n      <td>field</td>\n      <td>2</td>\n      <td>Saurashtra Cricket Association Stadium</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11</td>\n      <td>9</td>\n      <td>Indore</td>\n      <td>field</td>\n      <td>9</td>\n      <td>Holkar Cricket Stadium</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>7</td>\n      <td>Bangalore</td>\n      <td>bat</td>\n      <td>3</td>\n      <td>M Chinnaswamy Stadium</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8</td>\n      <td>10</td>\n      <td>Hyderabad</td>\n      <td>field</td>\n      <td>10</td>\n      <td>Rajiv Gandhi International Stadium, Uppal</td>\n      <td>10</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"1fd6e17a-7031-4205-b6e9-3a62a2f4c4fc","_uuid":"0dde32b2190ed99290afe50a5955db11f54c725a","trusted":true},"cell_type":"code","source":"df = pd.DataFrame(matches)\ndf.describe()","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"            team1       team2  toss_winner      winner\ncount  636.000000  636.000000   636.000000  636.000000\nmean     5.540881    5.511006     5.371069    5.309748\nstd      3.329169    3.341677     3.293140    3.288726\nmin      1.000000    1.000000     1.000000    1.000000\n25%      3.000000    3.000000     2.000000    2.000000\n50%      5.000000    5.000000     5.000000    5.000000\n75%      9.000000    8.000000     7.250000    8.000000\nmax     13.000000   13.000000    13.000000   14.000000","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>team1</th>\n      <th>team2</th>\n      <th>toss_winner</th>\n      <th>winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n      <td>636.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.540881</td>\n      <td>5.511006</td>\n      <td>5.371069</td>\n      <td>5.309748</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.329169</td>\n      <td>3.341677</td>\n      <td>3.293140</td>\n      <td>3.288726</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>9.000000</td>\n      <td>8.000000</td>\n      <td>7.250000</td>\n      <td>8.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>13.000000</td>\n      <td>14.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"c6e07308-d14e-48f6-966e-b34a1aa26053","_uuid":"628c0dbd5a657da7f751f9e88dccb2a307102ad0","trusted":true},"cell_type":"code","source":"#Here we are trying to find correlation between toss and win..\ntemp1=df['toss_winner'].value_counts(sort=True)\ntemp2=df['winner'].value_counts(sort=True)\n#Mumbai won most toss and also most matches\nprint('No of toss winners by each team')\nfor idx, val in temp1.iteritems():\n   print('{} -> {}'.format(list(dicVal.keys())[list(dicVal.values()).index(idx)],val))\nprint('No of match winners by each team')\nfor idx, val in temp2.iteritems():\n   print('{} -> {}'.format(list(dicVal.keys())[list(dicVal.values()).index(idx)],val))","execution_count":11,"outputs":[{"output_type":"stream","text":"No of toss winners by each team\nMI -> 85\nKKR -> 78\nDD -> 72\nRCB -> 70\nKXIP -> 68\nCSK -> 66\nRR -> 63\nDC -> 43\nSRH -> 35\nPW -> 20\nGL -> 15\nRPS -> 13\nKTK -> 8\nNo of match winners by each team\nMI -> 92\nCSK -> 79\nKKR -> 77\nRCB -> 73\nKXIP -> 70\nRR -> 63\nDD -> 62\nSRH -> 42\nDC -> 29\nRPS -> 15\nGL -> 13\nPW -> 12\nKTK -> 6\nDraw -> 3\n","name":"stdout"}]},{"metadata":{"_cell_guid":"65da1127-a7a1-437a-b7c7-c0106c7780a3","_uuid":"104a5b07ffe006951aeda1326c20ea934477cbb7","trusted":true},"cell_type":"code","source":"#shows that Mumbai won most matches followed by Chennai\ndf['winner'].hist(bins=50)","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7fd0e74a2990>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_cell_guid":"2be0656b-0d94-49f5-801f-228f41fab11c","_uuid":"0840fba728809b1387abf191b0652741941b29e6","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nfig = plt.figure(figsize=(8,4))\nax1 = fig.add_subplot(121)\nax1.set_xlabel('Team')\nax1.set_ylabel('Count of toss wins')\nax1.set_title(\"toss winners\")\ntemp1.plot(kind='bar')\n\nax2 = fig.add_subplot(122)\ntemp2.plot(kind = 'bar')\nax2.set_xlabel('Team')\nax2.set_ylabel('count of matches won')\nax2.set_title(\"Match winners\")","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"Text(0.5, 1.0, 'Match winners')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"_cell_guid":"57b35b58-1b3b-4daa-be57-68bc0a622426","_uuid":"682eba17b4a5cddfe49b4da05161d34b4d36332d","trusted":true},"cell_type":"code","source":" df.apply(lambda x: sum(x.isnull()),axis=0) \n    #find the null values in every column","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"team1            0\nteam2            0\ncity             0\ntoss_decision    0\ntoss_winner      0\nvenue            0\nwinner           0\ndtype: int64"},"metadata":{}}]},{"metadata":{"_cell_guid":"dfb2d961-787a-4813-9ee5-dfcf1672a539","_uuid":"64c4d738d22a0dc73f4551b32e3083913e2218be","trusted":true},"cell_type":"code","source":"#Find cities which are null\ndf[pd.isnull(df['city'])]","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"Empty DataFrame\nColumns: [team1, team2, city, toss_decision, toss_winner, venue, winner]\nIndex: []","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>team1</th>\n      <th>team2</th>\n      <th>city</th>\n      <th>toss_decision</th>\n      <th>toss_winner</th>\n      <th>venue</th>\n      <th>winner</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"d3f5ba9e-e7c5-4ef0-b2d4-38e5f1d13265","_uuid":"beec98d9a0c04713fa822cb787ad8fe9b4f6ce93","trusted":true},"cell_type":"code","source":"#building predictive model\nfrom sklearn.preprocessing import LabelEncoder\nvar_mod = ['city','toss_decision','venue']\nle = LabelEncoder()\nfor i in var_mod:\n    df[i] = le.fit_transform(df[i])\ndf.dtypes ","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"team1            int64\nteam2            int64\ncity             int64\ntoss_decision    int64\ntoss_winner      int64\nvenue            int64\nwinner           int64\ndtype: object"},"metadata":{}}]},{"metadata":{"_cell_guid":"b0c3f786-995e-46f9-9bc6-e4ea50a91829","_uuid":"25a27bbcf19fb3253f51a52fd8010ee62cdde757","trusted":true},"cell_type":"code","source":"#Import models from scikit learn module:\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split \nfrom sklearn.model_selection import KFold   #For K-fold cross validation\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn import metrics\nfrom sklearn.model_selection import LeaveOneOut \n#Generic function for making a classification model and accessing performance:\ndef classification_model(model, data, predictors, outcome):\n  model.fit(data[predictors],data[outcome])\n  predictions = model.predict(data[predictors])\n  accuracy = metrics.accuracy_score(predictions,data[outcome])\n  print('Accuracy of the model is : %s' % '{0:.3%}'.format(accuracy))\n  kf = KFold(n_splits=5)\n  kf.get_n_splits()\n  #loo = LeaveOneOut()\n  #loo.get_n_splits(data[predictors])\n  error = []\n  for train, test in kf.split(data[predictors]):\n    train_predictors = (data[predictors].iloc[train,:])\n    train_target = data[outcome].iloc[train]\n    model.fit(train_predictors, train_target)\n    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n \n  print('Cross-Validation Score : %s' % '{0:.3%}'.format(np.mean(error)))\n\n  model.fit(data[predictors],data[outcome]) ","execution_count":18,"outputs":[]},{"metadata":{"_cell_guid":"534e353c-240d-44b9-a580-fa059e6e2827","_uuid":"8613122d2ed389eae87f248de0dfebc487293590","trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\noutcome_var=['winner']\npredictor_var = ['team1','team2','toss_winner']\nmodel = LogisticRegression()\nclassification_model(model, df,predictor_var,outcome_var)\nprint(\"Here we used linear regression model for predication\")","execution_count":19,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n","name":"stderr"},{"output_type":"stream","text":"Accuracy of the model is : 24.057%\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n","name":"stderr"},{"output_type":"stream","text":"Cross-Validation Score : 21.225%\nHere we used linear regression model for predication\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n","name":"stderr"}]},{"metadata":{"_cell_guid":"690b1e88-d916-45a9-a53e-b1f43f51e869","_uuid":"065454402927c2e1848d64749e899302f5d5f1c8","trusted":true},"cell_type":"code","source":"df.head(3)","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"   team1  team2  city  toss_decision  toss_winner  venue  winner\n0     10      3    14              1            3     23      10\n1      1     11    25              1           11     16      11\n2      8      2    27              1            2     25       2","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>team1</th>\n      <th>team2</th>\n      <th>city</th>\n      <th>toss_decision</th>\n      <th>toss_winner</th>\n      <th>venue</th>\n      <th>winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>3</td>\n      <td>14</td>\n      <td>1</td>\n      <td>3</td>\n      <td>23</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>11</td>\n      <td>25</td>\n      <td>1</td>\n      <td>11</td>\n      <td>16</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>2</td>\n      <td>27</td>\n      <td>1</td>\n      <td>2</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"3a586194-f1c8-48d7-824d-f4381044201b","_uuid":"ce2d007ee9da6d41b6a64957df1723a8bacd625f","trusted":true},"cell_type":"code","source":"model = RandomForestClassifier(n_estimators=70)\noutcome_var = ['winner']\npredictor_var = ['team1', 'team2', 'venue', 'toss_winner','city','toss_decision']\nclassification_model(model, df,predictor_var,outcome_var) ","execution_count":21,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n  # This is added back by InteractiveShellApp.init_path()\n","name":"stderr"},{"output_type":"stream","text":"Accuracy of the model is : 89.151%\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n","name":"stderr"},{"output_type":"stream","text":"Cross-Validation Score : 46.690%\n","name":"stdout"}]},{"metadata":{"_cell_guid":"f27dbad3-c868-493c-b072-89ea73014c72","_uuid":"fb5810bde199a275fdc668aabc3bc3c1bbb5f8f0","trusted":true},"cell_type":"code","source":"df.head(7)","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"   team1  team2  city  toss_decision  toss_winner  venue  winner\n0     10      3    14              1            3     23      10\n1      1     11    25              1           11     16      11\n2      8      2    27              1            2     25       2\n3     11      9    15              1            9     11       9\n4      3      7     2              0            3     14       3\n5      8     10    14              1           10     23      10\n6      2      1    22              1            1     34       1","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>team1</th>\n      <th>team2</th>\n      <th>city</th>\n      <th>toss_decision</th>\n      <th>toss_winner</th>\n      <th>venue</th>\n      <th>winner</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>3</td>\n      <td>14</td>\n      <td>1</td>\n      <td>3</td>\n      <td>23</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>11</td>\n      <td>25</td>\n      <td>1</td>\n      <td>11</td>\n      <td>16</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>2</td>\n      <td>27</td>\n      <td>1</td>\n      <td>2</td>\n      <td>25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11</td>\n      <td>9</td>\n      <td>15</td>\n      <td>1</td>\n      <td>9</td>\n      <td>11</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>14</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8</td>\n      <td>10</td>\n      <td>14</td>\n      <td>1</td>\n      <td>10</td>\n      <td>23</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2</td>\n      <td>1</td>\n      <td>22</td>\n      <td>1</td>\n      <td>1</td>\n      <td>34</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"644e273c-62ab-4461-a57f-0d5d97ec0faf","_uuid":"e15b52ae9402f0ac22d521a08056204941fe7582","trusted":true},"cell_type":"code","source":"#'team1', 'team2', 'venue', 'toss_winner','city','toss_decision'\nteam1='RCB'\nteam2='KKR'\ntoss_winner='RCB'\ninput=[dicVal[team1],dicVal[team2],'14',dicVal[toss_winner],'2','1']\ninput = np.array(input).reshape((1, -1))\noutput=model.predict(input)\nprint(list(dicVal.keys())[list(dicVal.values()).index(output)]) #find key by value search output","execution_count":23,"outputs":[{"output_type":"stream","text":"KKR\n","name":"stdout"}]},{"metadata":{"_cell_guid":"627cb3be-ba94-442a-8654-d12e2a62f1e1","_uuid":"7f7def069941961f1f7a545811a55a09a3c88081","trusted":true},"cell_type":"code","source":"#'team1', 'team2', 'venue', 'toss_winner','city','toss_decision'\nteam1='DC'\nteam2='DD'\ntoss_winner='DC'\ninput=[dicVal[team1],dicVal[team2],'23',dicVal[toss_winner],'14','0']\ninput = np.array(input).reshape((1, -1))\noutput=model.predict(input)\nprint(list(dicVal.keys())[list(dicVal.values()).index(output)]) #find key by value search output","execution_count":24,"outputs":[{"output_type":"stream","text":"DD\n","name":"stdout"}]},{"metadata":{"_cell_guid":"0c264381-dfaa-480f-a4a7-fe97700a01ae","_uuid":"6abb285371edce31eb5ec505d1c01ecfae310f11","trusted":true},"cell_type":"code","source":"#feature importances: If we ignore teams, Venue seems to be one of important factors in determining winners \n#followed by toss winning, city\n#we notice that team1 and team2 account for highest value for reason that it is either if these value \n#with toss winner going to be winner. So we could ignore team2 and team1\nimp_input = pd.Series(model.feature_importances_, index=predictor_var).sort_values(ascending=False)\nprint(imp_input)","execution_count":25,"outputs":[{"output_type":"stream","text":"team2            0.253067\nteam1            0.224570\nvenue            0.172315\ntoss_winner      0.166882\ncity             0.149618\ntoss_decision    0.033547\ndtype: float64\n","name":"stdout"}]},{"metadata":{"_cell_guid":"81eb6be7-e63a-4274-bb43-eb8baf59db1e","_uuid":"2936eb2d944a1ea27cf07c6c55880fc7b055b38a","trusted":true},"cell_type":"code","source":"#okay from the above prediction on features, we notice toss winner has least chances of winning matches\n#but does the current stats shows the same result\n#df.count --> 577 rows\n#Previously toss_winners were about 50.4%, with 2017 IPL season, it has reached 56.7%. As data matures, so does\n# the changes in the predictions\nimport matplotlib.pyplot as mlt\nmlt.style.use('fivethirtyeight')\ndf_fil=df[df['toss_winner']==df['winner']]\nslices=[len(df_fil),(577-len(df_fil))]\nmlt.pie(slices,labels=['Toss & win','Toss & lose'],startangle=90,shadow=True,explode=(0,0),autopct='%1.1f%%',colors=['r','g'])\nfig = mlt.gcf()\nfig.set_size_inches(6,6)\nmlt.show()\n# Toss winning does not gaurantee a match win from analysis of current stats and thus \n#prediction feature gives less weightage to that ","execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x432 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"_cell_guid":"04f04da4-a4f1-4e51-82ba-abd5b8825979","_uuid":"6a38764f476f980259486ffab2572d711ad6404d","trusted":true},"cell_type":"code","source":"#top 2 team analysis based on number of matches won against each other and how venue affects them?\n#Previously we noticed that CSK won 79, RCB won 70 matches\n#now let us compare venue against a match between CSK and RCB\n#we find that CSK has won most matches against RCB in MA Chidambaram Stadium, Chepauk, Chennai\n#RCB has not won any match with CSK in stadiums St George's Park and Wankhede Stadium, but won matches\n#with CSK in Kingsmead, New Wanderers Stadium.\n#It does prove that chances of CSK winning is more in Chepauk stadium when played against RCB.\n# Proves venue is important feature in predictability\nimport seaborn as sns\nteam1=dicVal['MI']\nteam2=dicVal['CSK']\nmtemp=matches[((matches['team1']==team1)|(matches['team2']==team1))&((matches['team1']==team2)|(matches['team2']==team2))]\nsns.countplot(x='venue', hue='winner',data=mtemp,palette='Set2')\nmlt.xticks(rotation='vertical')\nleg = mlt.legend( loc = 'upper right')\nfig=mlt.gcf()\nfig.set_size_inches(10,6)\nmlt.show()\n#use --> le.classes_[18] to get stadium details","execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 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\n"},"metadata":{}}]},{"metadata":{"_cell_guid":"040fce71-101f-4428-b55e-f2fd9b5abb42","_uuid":"d61f0abfb4b0587de19343b6e334440f703510da","trusted":true},"cell_type":"code","source":"le.classes_[34]","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"'Wankhede 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