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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.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression

# Import any additional modules and start coding below
# Read the data
df = pd.read_csv('rental_info.csv')

# Create rental_length_days column
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']).dt.days

# Create dummy variables for special_features
df['deleted_scenes'] = df['special_features'].str.contains('Deleted Scenes', na=False).astype(int)
df['behind_the_scenes'] = df['special_features'].str.contains('Behind the Scenes', na=False).astype(int)

# Prepare feature set X (exclude columns that leak target info)
leak_cols = ['rental_date', 'return_date', 'rental_length_days', 'special_features']
X = df.drop(columns=leak_cols)
y = df['rental_length_days']

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

# Try Linear Regression
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred_lr = lr.predict(X_test)
mse_lr = mean_squared_error(y_test, y_pred_lr)

# Try Random Forest
rf = RandomForestRegressor(random_state=9)
rf.fit(X_train, y_train)
y_pred_rf = rf.predict(X_test)
mse_rf = mean_squared_error(y_test, y_pred_rf)

# Choose the best model
if mse_lr < mse_rf and mse_lr < 3:
    best_model = lr
    best_mse = mse_lr
elif mse_rf < mse_lr and mse_rf < 3:
    best_model = rf
    best_mse = mse_rf
else:
    # If neither is below 3, choose the one with lowest MSE
    best_model = lr if mse_lr < mse_rf else rf
    best_mse = min(mse_lr, mse_rf)

print("Best model:", type(best_model).__name__)
print("Best MSE:", best_mse)