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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
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import RandomizedSearchCV

from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor

# Import any additional modules and start coding below

1 - Getting the number of rental days

# Read in the data
df_rental = pd.read_csv('rental_info.csv')
df_rental.head()
df_rental.info()
df_rental.describe()
df_rental.isna().sum()
# Converting 'return_date' and 'rental_date' into pandas datetime format
df_rental['rental_length'] = pd.to_datetime(df_rental['return_date']) - pd.to_datetime(df_rental['rental_date'])
df_rental[['return_date', 'rental_date', 'rental_length']].head()
# Extracting number of days from 'rental_length'
df_rental['rental_length_days'] = df_rental['rental_length'].dt.days
df_rental['rental_length_days'].head()
df_rental.info()

Adding dummy variables using the special features column

# Value counts of 'special_features' column
df_rental['special_features'].value_counts()
# Adding dummy variable for deleted scenes
df_rental['deleted_scenes'] = np.where(df_rental['special_features'].str.contains('Deleted Scenes'), 1, 0)
# Adding dummy variable for behind the scenes
df_rental['behind_the_scenes'] = np.where(df_rental['special_features'].str.contains('Behind the Scenes'), 1, 0)
df_rental[['special_features', 'deleted_scenes', 'behind_the_scenes']].head()
df_rental[['special_features', 'deleted_scenes', 'behind_the_scenes']].sample(10)
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