<jupyter_start><jupyter_text>CS: GO win/loss prediction and user retention through better matchmaking<jupyter_code>import pandas as pd
import numpy as np

community = pd.read_csv('mm_master_demos.csv', sep=',', encoding='utf-8', header= 0)

def correlation_matrix(df):
    from matplotlib import pyplot as plt
    from matplotlib import cm as cm

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    cmap = cm.get_cmap('jet', 30)
    cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap)
    ax1.grid(True)
    plt.title('Player stats correlation')
    pd.options.display.max_columns = None
    labels = df.head()
    ax1.set_xticklabels(labels,fontsize=6)
    ax1.set_yticklabels(labels,fontsize=6)
    # Add colorbar, make sure to specify tick locations to match desired ticklabels
    fig.colorbar(cax)
    plt.show()
    
correlation_matrix(community)
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import warnings

warnings.filterwarnings("ignore",category=mlab.cbook.mplDeprecation)

mu = 0.13
sigma = 0.02
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)

plt.plot(x,mlab.normpdf(x, mu, sigma))
plt.show()<jupyter_output><empty_output>
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