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script.py

import codecademylib3_seaborn
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# load and investigate the data here:
data = pd.read_csv('tennis_stats.csv')
df = pd.DataFrame(data)

# perform exploratory analysis here:

#Function that returns the R value of after performing linear regression
def scr(x_values, y_values):
    x_train, x_test, y_train, y_test = train_test_split(x_values, y_values,train_size = 0.80, test_size = 0.20)
    lr = LinearRegression()
    lr.fit(x_train, y_train)
    y_predict = lr.predict(x_test)
    R = lr.score(x_train, y_train)
    return (R)

## perform single feature linear regressions here:

#Following set of code calculates the R score of each feature and only prints the value above 0.8
print ('\nOutcome: Wins Models')
for i in df.columns[2:20]:
    sc = scr(df[[i]],df[['Wins']])
    if sc > 0.8:
        print (i)
        print (sc)

print ('\n\nOutcome: Losses Models')
for i in df.columns[2:20]:
    sc = scr(df[[i]],df[['Losses']])
    if sc > 0.8:
        print (i)
        print (sc)

print ('\n\nOutcome: Winnings Models')
for i in df.columns[2:20]:
    sc = scr(df[[i]],df[['Winnings']])
    if sc > 0.8:
        print (i)
        print (sc)

print ('\n\nOutcome: Ranking Models')
for i in df.columns[2:20]:
    sc = scr(df[[i]],df[['Ranking']])
    if sc > 0.8:
        print (i)
        print (sc)
        
        
## perform two feature linear regressions here:
#based on the single feature regression, following features were selected
print ('Model: BreakPointsOpportunities, ReturnGamesPlayed')
print (scr(df[['BreakPointsOpportunities', 'ReturnGamesPlayed']], df['Winnings']))
print ('Model: BreakPointsOpportunities, ServiceGamesPlayed')
print (scr(df[['BreakPointsOpportunities', 'ServiceGamesPlayed']], df['Winnings']))
print ('Model: ReturnGamesPlayed, ServiceGamesPlayed')
print (scr(df[['ReturnGamesPlayed', 'ServiceGamesPlayed']], df['Winnings']))


## perform multiple feature linear regressions here:
#all three combines
print ('Model: BreakPointsOpportunities, ReturnGamesPlayed, ServiceGamesPlayed')
print (scr(df[['BreakPointsOpportunities', 'ReturnGamesPlayed', 'ServiceGamesPlayed']], df['Winnings']))
print ('finish')