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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
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Lasso
parse_dates = ['rental_date', 'return_date']
df = pd.read_csv('rental_info.csv', parse_dates=parse_dates)
df.head()
df.info()
df.describe().T
df['special_features'].value_counts()
df['rental_length_days'] = (df['return_date'] - df['rental_date']).dt.days
df.drop(['rental_date','return_date'], axis=1, inplace=True)
df.head()
df['deleted_scenes'] = df['special_features'].str.contains('Deleted Scenes').astype(int)
df.head()
df['deleted_scenes'].value_counts()
df['behind_the_scenes'] = df['special_features'].str.contains('Behind the Scenes').astype(int)
df.head()
df['behind_the_scenes'].value_counts()
#df.drop(['special_features','amount_2','length_2','rental_rate_2'], axis=1, inplace=True)
df.drop(['special_features'], axis=1, inplace=True)
df.head()
X = df.drop('rental_length_days', axis=1)
y = df['rental_length_days']
X.shape, y.shape
X.head()
y.head()
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