Section two working towards a finding a model in which to predict the 2018 NCAA tourney results. This segment takes the cleaned csv file from part I and further manipulates it into a format appropriate for fitting to models. The primary problem with the current format is the fact each row revolves around a game. This code will split the rows into two- One for the winner and one for the loser.

```
import pandas as pd
```

The primary problem with the current format is the fact each row revolves around a game. This code will iterate through each row and split it into two- One for the winner and one for the loser. Results of a 1 for the winner and 0 for the loser are then added to provide the models label variables in which to test with.

As normal, step one will import data and look at current data. I took some time and threw together a quick correlation matrix in R before this and detemined a few unneccesary variables. Length is one. Another I'll cut later on is Offensive Rebound.

```
#import data and trim unnecessary columns
data_dir = '../data/'
df = pd.read_csv('../data/MM_Cleaned_2.csv')
df.drop(labels=['WLength', 'LLength'], inplace=True, axis=1)
df.head()
```

I then wanted to incorperate an RPI variable for each tearm/year.. A var I missed to include in part I. Thanks to Kaggle, this was easy to achieve as a dataset included in the comp had this held within. I import this and pull out RPI data, then merge with the working dataframe.

```
'''Import RPI and Connect'''
df_massey = pd.read_csv(data_dir + 'MasseyOrdinals.csv')
df_rpi = df_massey[(df_massey.SystemName == 'RPI') & (df_massey.RankingDayNum == 133)]
df_rpi.drop(labels=['SystemName', 'RankingDayNum'], inplace=True, axis=1)
df_winrpi = df_rpi.rename(columns={'TeamID' : 'WTeamID', 'OrdinalRank' : 'WOrdinalRank'})
df_lossrpi = df_rpi.rename(columns={'TeamID' : 'LTeamID', 'OrdinalRank' : 'LOrdinalRank'})
df_dummy = pd.merge(left=df, right=df_winrpi, on=['Season', 'WTeamID'])
df = pd.merge(left=df_dummy, right=df_lossrpi, on=['Season', 'LTeamID'])
df['Rank_Diff'] = df['WOrdinalRank'] - df['LOrdinalRank']
df.head()
```

Not to split the rows into winners and losers.. with included 1, 0 results. I simply pull each var of interest andf put it into either a win or loss dataframe. I incorperate the differential scores by adding the actual to the winning team and the opposite sign for the losing team... These vars will be the primary values of interest in model I imagine.

```
'''TRAIN cols'''
df_wins = pd.DataFrame()
df_wins['Season'] = df['Season']
df_wins['TeamID'] = df['WTeamID']
df_wins['OtherTeamID'] = df['LTeamID']
df_wins['Loc'] = df['WLoc']
df_wins['Coach'] = df['WCoach']
df_wins['Seed'] = df['WSeed']
df_wins['PPG'] = df['WSeason_ppg']
df_wins['AST'] = df['WSeason_ast']
df_wins['FGP'] = df['WSeason_FGPercent']
df_wins['FGP3'] = df['WSeason_FGPercent3']
df_wins['FTP'] = df['WSeason_FTPercent']
df_wins['OR'] = df['WSeason_OR']
df_wins['DR'] = df['WSeason_DR']
df_wins['STL'] = df['WSeason_stl']
df_wins['BLK'] = df['WSeason_blk']
df_wins['Rank'] = df['WOrdinalRank']
df_wins['PPG_Diff'] = df['Season_ppg_diff']
df_wins['SEED_Diff'] = df['SeedDiff']
df_wins['FGP_Diff'] = df['Season_FGP_Diff']
df_wins['AST_Diff'] = df['Season_ast_diff']
df_wins['FGP3_Diff'] = df['Season_FGP3_Diff']
df_wins['FTP_Diff'] = df['Season_FTP_Diff']
df_wins['OR_Diff'] = df['Season_OR_diff']
df_wins['DR_Diff'] = df['Season_DR_diff']
df_wins['STL_Diff'] = df['Season_stl_diff']
df_wins['BLK_Diff'] = df['Season_blk_diff']
df_wins['Rank_Diff'] = df['Rank_Diff']
df_wins['Result'] = 1
df_losses = pd.DataFrame()
df_losses['Season'] = df['Season']
df_losses['TeamID'] = df['LTeamID']
df_losses['OtherTeamID'] = df['WTeamID']
df_losses['Loc'] = df['LLoc']
df_losses['Coach'] = df['LCoach']
df_losses['Seed'] = df['LSeed']
df_losses['PPG'] = df['LSeason_ppg']
df_losses['AST'] = df['LSeason_ast']
df_losses['FGP'] = df['LSeason_FGPercent']
df_losses['FGP3'] = df['LSeason_FGPercent3']
df_losses['FTP'] = df['LSeason_FTPercent']
df_losses['OR'] = df['LSeason_OR']
df_losses['DR'] = df['LSeason_DR']
df_losses['STL'] = df['LSeason_stl']
df_losses['BLK'] = df['LSeason_blk']
df_wins['Rank'] = df['LOrdinalRank']
df_losses['PPG_Diff'] = -df['Season_ppg_diff']
df_losses['SEED_Diff'] = -df['SeedDiff']
df_losses['FGP_Diff'] = -df['Season_FGP_Diff']
df_losses['AST_Diff'] = -df['Season_ast_diff']
df_losses['FGP3_Diff'] = -df['Season_FGP3_Diff']
df_losses['FTP_Diff'] = -df['Season_FTP_Diff']
df_losses['OR_Diff'] = -df['Season_OR_diff']
df_losses['DR_Diff'] = -df['Season_DR_diff']
df_losses['STL_Diff'] = -df['Season_stl_diff']
df_losses['BLK_Diff'] = -df['Season_blk_diff']
df_losses['Rank_Diff'] = -df['Rank_Diff']
df_losses['Result'] = 0
```

With these splits done, lets combine them!

```
df_predictions = pd.concat((df_wins, df_losses))
df_predictions.TeamID = df_predictions.TeamID.astype('category')
df_predictions.head()
```

Looks all good. We have a new dataframe in pandas with each team's stats per year//other information of interest along with the necessary label variable of game Result (w/l). Time to print it and I'll have the final model building phase up soon!

```
'''PRINT OUT CLEAN CSV'''
df_predictions.to_csv('../data/train_data_diff.csv', index=False)
```