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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 as mse
from sklearn.linear_model import Lasso 
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
# Import any additional modules and start coding below

df=pd.read_csv('rental_info.csv')
df['rental_date']=pd.to_datetime(df['rental_date'])
df['return_date']=pd.to_datetime(df['return_date'])
df['rental_length_days']=df['return_date']-df['rental_date']
df['rental_length_days']=df['rental_length_days'].dt.days
df['deleted_scenes']=np.where(df['special_features'].str.contains('Deleted Scenes'),1,0)
df['behind_the_scenes']=np.where(df['special_features'].str.contains('Behind the Scenes'),1,0)
print(df.columns)
col_to_drop=['rental_date','return_date','special_features','rental_length_days']
X=df.drop(col_to_drop,axis=1)
y=df['rental_length_days']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=9)
lasso=Lasso(alpha=0.3,random_state=9).fit(X_train,y_train)
lasso_mse=mse(y_test,lasso.predict(X_test))
print(lasso_mse)

rdg=Ridge(alpha=0.3).fit(X_train,y_train)
ridge_mse=mse(y_test,rdg.predict(X_test))
print(ridge_mse)

rfg=RandomForestRegressor().fit(X_train,y_train)
rfg_mse=mse(y_test,rfg.predict(X_test))
print(rfg_mse)

best_model=rfg
best_mse=rfg_mse
best_model
best_mse