{"cells":[{"metadata":{},"cell_type":"markdown","source":"# overview\n#### This kernel is based on last year's [Basic Starter Kernel](https://www.kaggle.com/addisonhoward/basic-starter-kernel-ncaa-men-s-dataset-2019).\n#### I added total score feature calculated on a yearly basis :)"},{"metadata":{},"cell_type":"markdown","source":"## Import Library & Load Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import GridSearchCV","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"tourney_result = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/WDataFiles_Stage1/WNCAATourneyCompactResults.csv')\ntourney_seed = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/WDataFiles_Stage1/WNCAATourneySeeds.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# deleting unnecessary columns\ntourney_result = tourney_result.drop(['DayNum', 'WScore', 'LScore', 'WLoc', 'NumOT'], axis=1)\ntourney_result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Merge Seed"},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_result = pd.merge(tourney_result, tourney_seed, left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'], how='left')\ntourney_result.rename(columns={'Seed':'WSeed'}, inplace=True)\ntourney_result = tourney_result.drop('TeamID', axis=1)\ntourney_result = pd.merge(tourney_result, tourney_seed, left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], how='left')\ntourney_result.rename(columns={'Seed':'LSeed'}, inplace=True)\ntourney_result = tourney_result.drop('TeamID', axis=1)\ntourney_result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_seed(x):\n    return int(x[1:3])\n\ntourney_result['WSeed'] = tourney_result['WSeed'].map(lambda x: get_seed(x))\ntourney_result['LSeed'] = tourney_result['LSeed'].map(lambda x: get_seed(x))\ntourney_result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Merge Score"},{"metadata":{"trusted":true},"cell_type":"code","source":"season_result = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/WDataFiles_Stage1/WRegularSeasonCompactResults.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"season_win_result = season_result[['Season', 'WTeamID', 'WScore']]\nseason_lose_result = season_result[['Season', 'LTeamID', 'LScore']]\nseason_win_result.rename(columns={'WTeamID':'TeamID', 'WScore':'Score'}, inplace=True)\nseason_lose_result.rename(columns={'LTeamID':'TeamID', 'LScore':'Score'}, inplace=True)\nseason_result = pd.concat((season_win_result, season_lose_result)).reset_index(drop=True)\nseason_result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"season_score = season_result.groupby(['Season', 'TeamID'])['Score'].sum().reset_index()\nseason_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_result = pd.merge(tourney_result, season_score, left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'], how='left')\ntourney_result.rename(columns={'Score':'WScoreT'}, inplace=True)\ntourney_result = tourney_result.drop('TeamID', axis=1)\ntourney_result = pd.merge(tourney_result, season_score, left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], how='left')\ntourney_result.rename(columns={'Score':'LScoreT'}, inplace=True)\ntourney_result = tourney_result.drop('TeamID', axis=1)\ntourney_result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_win_result = tourney_result.drop(['Season', 'WTeamID', 'LTeamID'], axis=1)\ntourney_win_result.rename(columns={'WSeed':'Seed1', 'LSeed':'Seed2', 'WScoreT':'ScoreT1', 'LScoreT':'ScoreT2'}, inplace=True)\ntourney_win_result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_lose_result = tourney_win_result.copy()\ntourney_lose_result['Seed1'] = tourney_win_result['Seed2']\ntourney_lose_result['Seed2'] = tourney_win_result['Seed1']\ntourney_lose_result['ScoreT1'] = tourney_win_result['ScoreT2']\ntourney_lose_result['ScoreT2'] = tourney_win_result['ScoreT1']\ntourney_lose_result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Prepare Training Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_win_result['Seed_diff'] = tourney_win_result['Seed1'] - tourney_win_result['Seed2']\ntourney_win_result['ScoreT_diff'] = tourney_win_result['ScoreT1'] - tourney_win_result['ScoreT2']\ntourney_lose_result['Seed_diff'] = tourney_lose_result['Seed1'] - tourney_lose_result['Seed2']\ntourney_lose_result['ScoreT_diff'] = tourney_lose_result['ScoreT1'] - tourney_lose_result['ScoreT2']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tourney_win_result['result'] = 1\ntourney_lose_result['result'] = 0\ntourney_result = pd.concat((tourney_win_result, tourney_lose_result)).reset_index(drop=True)\ntourney_result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Train"},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = tourney_result.drop('result', axis=1)\ny_train = tourney_result.result\nX_train, y_train = shuffle(X_train, y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"logreg = LogisticRegression()\nparams = {'C': np.logspace(start=-5, stop=3, num=9)}\nclf = GridSearchCV(logreg, params, scoring='neg_log_loss', refit=True)\nclf.fit(X_train, y_train)\nprint('Best log_loss: {:.4}, with best C: {}'.format(clf.best_score_, clf.best_params_['C']))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Prepare Test Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/WSampleSubmissionStage1_2020.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df['Season'] = test_df['ID'].map(lambda x: int(x[:4]))\ntest_df['WTeamID'] = test_df['ID'].map(lambda x: int(x[5:9]))\ntest_df['LTeamID'] = test_df['ID'].map(lambda x: int(x[10:14]))\ntest_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df = pd.merge(test_df, tourney_seed, left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'], how='left')\ntest_df.rename(columns={'Seed':'Seed1'}, inplace=True)\ntest_df = test_df.drop('TeamID', axis=1)\ntest_df = pd.merge(test_df, tourney_seed, left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], how='left')\ntest_df.rename(columns={'Seed':'Seed2'}, inplace=True)\ntest_df = test_df.drop('TeamID', axis=1)\ntest_df = pd.merge(test_df, season_score, left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'], how='left')\ntest_df.rename(columns={'Score':'ScoreT1'}, inplace=True)\ntest_df = test_df.drop('TeamID', axis=1)\ntest_df = pd.merge(test_df, season_score, left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], how='left')\ntest_df.rename(columns={'Score':'ScoreT2'}, inplace=True)\ntest_df = test_df.drop('TeamID', axis=1)\ntest_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df['Seed1'] = test_df['Seed1'].map(lambda x: get_seed(x))\ntest_df['Seed2'] = test_df['Seed2'].map(lambda x: get_seed(x))\ntest_df['Seed_diff'] = test_df['Seed1'] - test_df['Seed2']\ntest_df['ScoreT_diff'] = test_df['ScoreT1'] - test_df['ScoreT2']\ntest_df = test_df.drop(['ID', 'Pred', 'Season', 'WTeamID', 'LTeamID'], axis=1)\ntest_df","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Predict & Make Submission File"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_preds = clf.predict_proba(test_df)[:, 1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/WSampleSubmissionStage1_2020.csv')\nsubmission_df['Pred'] = test_preds\nsubmission_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df['Pred'].hist()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}