Project: Predicting Movie Rental Durations

Beta

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

# Read data
df = pd.read_csv("rental_info.csv")
print(df.head())
print(df.info())

# Get the number of rental days
df["rental_length"] = pd.to_datetime(df["return_date"]) - pd.to_datetime(df["rental_date"])
df["rental_length_days"] = df["rental_length"].dt.days
print(df.head())
print(df.info())

# Column special_features
print(df["special_features"].value_counts())

# Create a dummy variable - "deleted_scenes"
df["deleted_scenes"] = np.where(df["special_features"].str.contains("Deleted Scenes"), 1, 0)

# Create a dummy variable - "behind_the_scenes"
df["behind_the_scenes"] = np.where(df["special_features"].str.contains("Behind the Scenes"), 1, 0)
print(df.head())
print(df.info())

# Define the predictor variables and the target variable
X = df.drop(["rental_date", "return_date", "special_features", "rental_length", "rental_length_days"], axis=1)
y = df["rental_length_days"]

print(X.info())
print(y.info())

# Split the train data and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

# Lasso Regression
from sklearn.linear_model import Lasso
lasso = Lasso(random_state=9, alpha=0.001).fit(X_train, y_train)
print(X_train.columns[lasso.coef_ > 0])

lasso = Lasso(random_state=9, alpha=0.01).fit(X_train, y_train)
print(X_train.columns[lasso.coef_ > 0])

import numpy as np

lasso = Lasso(random_state=9, alpha=0.001).fit(X_train, y_train)
print(X_train.iloc[:, lasso.coef_ > 0])