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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 and view the data
rental_dvd_df= pd.read_csv("rental_info.csv")
rental_dvd_df.head()
#data preprocessing
rental_dvd_df.isnull().sum()
rental_dvd_df.info()
#rental length days column creation
from datetime import date

#converting to datetime object

rental_dvd_df['rental_date']= pd.to_datetime(rental_dvd_df['rental_date'])
rental_dvd_df['return_date']= pd.to_datetime(rental_dvd_df['return_date'])

rental_dvd_df['rental_length_days']= rental_dvd_df['return_date']-rental_dvd_df['rental_date']

#convert to days

rental_dvd_df['rental_length_days']= rental_dvd_df['rental_length_days'].dt.days


rental_dvd_df.head()
rental_dvd_df['rental_date'].dtype
data= pd.DataFrame(rental_dvd_df['special_features'].unique())
data
import pandas as pd

#get dummies for special features column
def get_dummy_special_features(df):
    df['deleted_scenes'] = df['special_features'].apply(lambda x: 1 if 'Deleted Scenes' in x else 0)
    df['behind_the_scenes'] = df['special_features'].apply(lambda x: 1 if 'Behind the Scenes' in x else 0)
    return df


rental_df = get_dummy_special_features(rental_dvd_df)
print(rental_df)
rental_df.tail(10)
#Create features for the model
X= rental_df.drop(["rental_date", "return_date", "rental_length_days","special_features"], axis=1)
print(X.shape)
#target data
y= rental_df["rental_length_days"]
print(y.shape)
X_train, X_test,y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=9)
#lasso
from sklearn.linear_model import Lasso
lasso= Lasso(alpha=0.3, random_state=9)
lasso.fit(X_train, y_train)
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