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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.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor


# Import any additional modules and start coding below

df=pd.read_csv("rental_info.csv",parse_dates=["rental_date","return_date"])
df

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)
df["rental_lenght_days"]=(df["return_date"]-df["rental_date"]).dt.days
df_model=df.drop(["rental_date","return_date","special_features"],axis=1)
df_model
X=df_model.drop("rental_lenght_days",axis=1)
y=df_model["rental_lenght_days"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
model=RandomForestRegressor(n_estimators=500, random_state=42)
model.fit(X_train,y_train)
y_pred=model.predict(X_test)
print(f"r2 train: {model.score(X_train,y_train)}")
print(f"r2 test: {model.score(X_test,y_test)}")
best_model=model
best_mse=mean_squared_error(y_test,y_pred)
print(f"MSE: {best_mse}")
print(f"Model: {best_model}")