Skip to content

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 Utility Function

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error as MSE

# Import any additional modules and start coding below
from datetime import datetime
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor, RandomForestRegressor

Read and Check Summary of dataframe

# read the data
data = pd.read_csv('rental_info.csv')
data
data.info()
# print(data['release_year'].unique())
# print(data['amount'].unique())

Feature Engineering

Make rental_length_day column

# feature engineering : make rental_length_days column that contain diff day between return_date and rent_date
data['rental_length_days'] = (pd.to_datetime(data['return_date']) - pd.to_datetime(data['rental_date'])).dt.days
data.head()

Create 2 Columns : Deleted Scenes and Behind the Scenes

# deleted_scenes column
data['deleted_scenes'] = np.where(data['special_features'].str.contains('Deleted Scenes'),1,0)
# behind_the_scenes column
data['behind_the_scenes'] = np.where(data['special_features'].str.contains('Behind the Scenes'),1,0)
data.head()

Feature Selection

# choose random column for features columns
columns = (data.columns).to_list()
X_columns =  columns[2:7] + columns[8:15] + columns[-2:]
X = data[X_columns]

# target column
y_column = columns[-3]
y = data[y_column]
# print(len(X_columns))

Split Data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
‌
‌
‌