Skip to content

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
rental = pd.read_csv('rental_info.csv')
rental['return_date'] = pd.to_datetime(rental['return_date'])
rental['rental_date'] = pd.to_datetime(rental['rental_date'])
rental['rental_length_days'] = (rental['return_date']-rental['rental_date']).dt.days


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.drop(columns=['special_features'],inplace=True)

X = rental.drop(columns=['rental_length_days','return_date','rental_date'])
y = rental['rental_length_days']
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=9)
from sklearn.linear_model import Lasso
import numpy as np

lasso = Lasso(alpha=0.3, random_state=9) 

# Train the model and access the coefficients
lasso.fit(X_train, y_train)
lasso_coef = lasso.coef_
X_lasso_train, X_lasso_test = X_train.iloc[:, lasso_coef > 0], X_test.iloc[:, lasso_coef > 0]
lasso_coef
from sklearn.linear_model import LinearRegression
ols = LinearRegression()
ols = ols.fit(X_lasso_train, y_train)
y_test_pred = ols.predict(X_lasso_test)
mse_lin_reg_lasso = mean_squared_error(y_test, y_test_pred)

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
param_dist = {'n_estimators': np.arange(1,101,1),
          'max_depth':np.arange(1,11,1)}
rf = RandomForestRegressor()
rand_search = RandomizedSearchCV(rf, 
                                 param_distributions=param_dist, 
                                 cv=5, 
                                 random_state=9)
rand_search.fit(X_train, y_train)
hyper_params = rand_search.best_params_

rf = RandomForestRegressor(n_estimators=hyper_params["n_estimators"], 
                           max_depth=hyper_params["max_depth"], 
                           random_state=9)
rf.fit(X_train,y_train)
rf_pred = rf.predict(X_test)
mse_random_forest= mean_squared_error(y_test, rf_pred)

# Random forest gives lowest MSE so:
best_model = rf
best_mse = mse_random_forest
print(best_mse)