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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
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import Lasso
from sklearn.tree import DecisionTreeRegressor


# Import any additional modules and start coding below
rental_info = pd.read_csv('rental_info.csv', parse_dates=['rental_date', 'return_date'])
rental_info.head()
rental_info.describe()
  1. Getting the number of rental days.
rental_info['rental_length_days'] = (rental_info['return_date'] - rental_info['rental_date']).dt.days
rental_info.head()
  1. Adding dummy variables using the special features column.
rental_info['behind_the_scenes'] = rental_info['special_features'].str.contains('Behind the Scenes').astype(int)
rental_info['deleted_scenes'] = rental_info['special_features'].str.contains('Deleted Scenes').astype(int)
  1. Executing a train-test split
columns_to_drop = ['rental_date', 'return_date', 'special_features']#, 'amount_2', 'rental_rate_2', 'length_2']
rental_info.drop(columns=columns_to_drop, inplace=True)
rental_info.columns
X = rental_info.drop(columns=['rental_length_days'])#.values
y = rental_info['rental_length_days']#.values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
  1. Performing feature selection
lasso = Lasso(random_state=9, alpha=0.01)
lasso.fit(X_train, y_train)
lasso_coef = lasso.coef_
print(lasso_coef)
names = rental_info.drop(columns=['rental_length_days']).columns
plt.barh(names, lasso_coef)
plt.xticks(rotation=90)
plt.show()
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