text_plain_output_1.png
from matplotlib.pyplot import figure

from sklearn.tree import DecisionTreeRegressor

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sn

import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')

big_dance.columns

import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()

y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]

from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)

print("Making predictions for the following 5 March Madness's:")
print(X.head())
print('The predictions are')
print(bd_model.predict(X.head()))