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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 MultiLabelBinarizer
from sklearn.linear_model import LinearRegression
# from sklearn.metrics import root_mean_squared_error
from sklearn.model_selection import cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor ,RandomForestRegressor
df = pd.read_csv('rental_info.csv',parse_dates=['rental_date','return_date'])
df.info()
df.describe(include='all')
df
df['rental_length_days'] = (df['return_date']-df['rental_date']).dt.days
unique_features = set()
unique_special_features = df['special_features'].value_counts().index.str.strip('{}')
for usf in [usf.split(',') for usf in unique_special_features]:
    for sp in usf:
        unique_features.add(sp)
unique_features
for unique_feature in unique_features:
    df[unique_feature] = df['special_features'].str.contains(unique_feature).astype(int)
X = df[df.columns.difference(['rental_length_days', 'rental_date', 'return_date', 'special_features','Commentaries','Trailers'])]
y=df['rental_length_days']
X.rename(columns={'"Behind the Scenes"': 'behind_the_scenes','"Deleted Scenes"': 'deleted_scenes'}, inplace=True)
X.columns
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=9)
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