linear_model.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import sys

# try whatever you want
element = 'hour'

df = pd.read_csv('../improved_data_set/turnstile_weather_v2.csv', index_col=0)
dk = pd.DataFrame(df.groupby([element])['ENTRIESn_hourly'].mean().reset_index())

X = dk[[element]]
y = dk['ENTRIESn_hourly']

## fit a OLS model with intercept on TV and Radio
X = sm.add_constant(X)
est = sm.OLS(y, X).fit()

print est.summary()

# DRAW 
# We pick 100 hundred points equally spaced from the min to the max
X_prime = np.linspace(X[element].min(), X[element].max(), 100)[:, np.newaxis]
X_prime = sm.add_constant(X_prime)  # add constant as we did before

# Now we calculate the predicted values
y_hat = est.predict(X_prime)

plt.scatter(X[element], y, alpha=0.3)  # Plot the raw data
plt.xlabel(element)
plt.ylabel("Hourly Entries")
plt.plot(X_prime[:, 1], y_hat, 'r', alpha=0.9)  # Add the regression line, colored in red
plt.show()