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Project: Predicting Movie Rental Durations
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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.
```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```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``````