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A DVD rental company needs your help! They want to figure out how many days a customer will rent a DVD for based on some features and has approached you for help. They want you to try out some regression models which will help predict the number of days a customer will rent a DVD for. The company wants a model which yeilds a MSE of 3 or less on a test set. The model you make will help the company become more efficient inventory planning.

The data they provided is in the csv file rental_info.csv. It has the following features:

  • "rental_date": The date (and time) the customer rents the DVD.
  • "return_date": The date (and time) the customer returns the DVD.
  • "amount": The amount paid by the customer for renting the DVD.
  • "amount_2": The square of "amount".
  • "rental_rate": The rate at which the DVD is rented for.
  • "rental_rate_2": The square of "rental_rate".
  • "release_year": The year the movie being rented was released.
  • "length": Lenght of the movie being rented, in minuites.
  • "length_2": The square of "length".
  • "replacement_cost": The amount it will cost the company to replace the DVD.
  • "special_features": Any special features, for example trailers/deleted scenes that the DVD also has.
  • "NC-17", "PG", "PG-13", "R": These columns are dummy variables of the rating of the movie. It takes the value 1 if the move is rated as the column name and 0 otherwise. For your convinience, the reference dummy has already been dropped.
import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Import any additional modules and start coding below
# read csv file
rental = pd.read_csv('rental_info.csv')
rental.head()
rental.info()
# Convert dates to datetime dtype
rental['rental_date'] = pd.to_datetime(rental['rental_date'])
rental['return_date'] = pd.to_datetime(rental['return_date'])

# Calculate length of rental days
rental['rental_length_days'] = (rental['return_date'] - rental['rental_date']).dt.days

rental.head()
# create dummy variables from special features column
rental['deleted_scenes'] = np.where(rental['special_features'].str.contains('Deleted Scenes'), 1, 0)
rental['behind_the_scenes'] = np.where(rental['special_features'].str.contains('Behind the Scenes'), 1, 0)

rental.head()
rental.rental_length_days.unique()
# create X, y dataframes
X = rental.drop(columns=['rental_date', 'return_date', 'special_features', 'rental_length_days'])
y = rental['rental_length_days']
# Preprocessing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
# model training
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

models = {'Linear Regression': LinearRegression(),
         'Support Vector Regressor': SVR(),
         'Random Forest Regressor': RandomForestRegressor(random_state=9)}

for name, model in models.items():
    print(name, '\n--------------------------------------------')

    # fitting model
    model.fit(X_train, y_train)

    # model evaluation
    print(f'Training MSE: {mean_squared_error(y_train, y_pred=model.predict(X_train))}')
    print(f'Testing MSE: {mean_squared_error(y_test, y_pred=model.predict(X_test))}\n\n')
# Saving best Model
best_model = RandomForestRegressor(random_state=9)
best_mse = 2.030141907417274