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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

# For lasso
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Run OLS
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Random forest
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
rental_info = pd.read_csv("rental_info.csv")
rental_info['rental_date'] = pd.to_datetime(rental_info['rental_date'])
rental_info['return_date'] = pd.to_datetime(rental_info['return_date'])
rental_info["rental_length"] = pd.to_datetime(rental_info["return_date"]) - pd.to_datetime(rental_info["rental_date"])
rental_info["rental_length_days"] = rental_info["rental_length"].dt.days
rental_info["deleted_scenes"] = rental_info['special_features'].str.contains("Deleted Scenes")*1

rental_info["behind_the_scenes"] = rental_info['special_features'].str.contains("Behind the Scenes")*1
X = rental_info[['amount', 'release_year', 'rental_rate',
       'length', 'replacement_cost', 'NC-17', 'PG',
       'PG-13', 'R', 'amount_2', 'length_2', 'rental_rate_2',
        'deleted_scenes', 'behind_the_scenes']]

y = rental_info['rental_length_days']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
lasso = Lasso(alpha=0.3, random_state=9)

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]
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)
# Random forest hyperparameter space
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)
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