gist for medium blogs ..

sklearn_ex.py

# importing necessary modules ... 
from sklearn.linear_model import LinearRegression

# creating a copy of our existing dataframe ... 
df_copy = df

# one hot encoding .. pre-requisite for LinearRegression ... 
df_copy['origin'] = pd.get_dummies(df_copy['origin'])

# creating Training set and Test set from what we have learn't from above ... 
x_train = df_copy.iloc[0:300,1:8]
x_test = df_copy.iloc[300:393,1:8]
y_true = df_copy.iloc[0:300,0:1]
y_test = df_copy.iloc[300:393,0:1]

# initialising the Linear Regression model .. 
linear_regression_model = LinearRegression()

# fitting the model onto our features / X_train ...
linear_regression_model.fit(x_train,y_true)

# predicting the result on test_set ... 
y_pred = linear_regression_model.predict(x_test)