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Created May 18, 2015 02:46
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Titans.py Question - Log. Regression Model May 17
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gistfile1.txt
nfl = pd.concat([nfl2000, nfl2001, nfl2002, nfl2003, nfl2004, nfl2005, nfl2006, nfl2007, nfl2008, nfl2009, nfl2010, nfl2011, nfl2012, nfl2013], axis=0)
nfl['WinLoss'] = np.where(nfl.ScoreOff > nfl.ScoreDef, 1, 0)
nfl.columns
feature_cols = ['Date', 'FirstDownDef', 'FirstDownOff', 'FumblesDef', 'FumblesOff', 'Line', 'Opponent', 'PassAttDef', 'PassAttOff', 'PassCompDef', 'PassCompOff', 'PassIntDef', 'PassIntOff', 'PassYdsDef', 'PassYdsOff', 'PenYdsDef', 'PenYdsOff', 'PuntAvgOff', 'RushAttDef', 'RushAttOff', 'RushYdsDef', 'RushYdsOff', 'SackNumDef', 'SackNumOff', 'SackYdsDef', 'SackYdsOff', 'ScoreDef', 'ScoreOff', 'Site', 'TeamName', 'ThirdDownPctDef', 'ThirdDownPctOff', 'TimePossDef', 'TimePossOff', 'TotalLine', 'Totalline', 'Totalline ', 'WinLoss']
X = nfl[feature_cols]
y = nfl
# Train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 2)
# Fit model -- In the linear regression model the dependent variable y is considered continuous, whereas in logistic regression it is categorical, i.e., discrete. In application, the former is used in regression settings while the latter is used for binary classification or multi-class classification (where it is called multinomial logistic regression)
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test) # Predict
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