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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 necessary modules.

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error as MSE
import pandas as pd
import numpy as np

Look inside the dataset.

dvd = pd.read_csv('rental_info.csv')
dvd.head()

Inspect the dataset and check for any missing and duplicated values.

dvd.describe()
dvd[dvd.duplicated()]
dvd.info()

Create a column named "rental_length_days" using the columns "return_date" and "rental_date", and add it to the pandas DataFrame. This column should contain information on how many days a DVD has been rented by a customer.

dvd['rental_date'] = pd.to_datetime(dvd['rental_date'])
dvd['return_date'] = pd.to_datetime(dvd['return_date'])
dvd['rental_length_days'] = (dvd['return_date'] - dvd['rental_date']).dt.days

Create two columns of dummy variables from "special_features".

dvd['deleted_scenes'] = (dvd['special_features'].str.contains('Deleted Scenes')).astype('int')
dvd['behind_the_scenes'] = (dvd['special_features'].str.contains('Behind the Scenes')).astype('int')

Make a pandas DataFrame called X containing all the appropriate features you can use to run the regression models, avoiding columns that leak data about the target.

Choose the "rental_length_days" as the target column and save it as a pandas Series called y

X = dvd.drop(['rental_date','return_date','rental_length_days','special_features'],axis=1)
y = dvd['rental_length_days']
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