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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