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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 any additional modules and start coding below 
from sklearn.linear_model import Lasso, LinearRegression 
from sklearn.preprocessing import StandardScaler 
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
import matplotlib.pyplot as plt 
# Load data 
movie_rentals = pd.read_csv('rental_info.csv') 
movie_rentals.info()
# Explore Data 
movie_rentals.head(10)
# convert columns to datetime formats 
movie_rentals['rental_date'] = pd.to_datetime(movie_rentals['rental_date']) 
movie_rentals['return_date'] = pd.to_datetime(movie_rentals['return_date'])

# rental days
movie_rentals['rental_length'] = pd.to_datetime(
    movie_rentals["return_date"]) - pd.to_datetime(movie_rentals["rental_date"])  
movie_rentals['rental_length_days'] = movie_rentals['rental_length'].dt.days 

movie_rentals.info()
# Inspect special features
movie_rentals['special_features'].value_counts()
# Dummy variables 
movie_rentals["deleted_scenes"] =  np.where(
    movie_rentals["special_features"].str.contains("Deleted Scenes"), 1,0) 
movie_rentals["behind_the_scenes"] = np.where(
    movie_rentals["special_features"].str.contains("Behind the Scenes"), 1,0)  
movie_rentals.info()
# Define features and target variable
X = movie_rentals.drop(columns=['rental_date', 'return_date', 'rental_length', 'rental_length_days', 'special_features',], axis=1)
y = movie_rentals['rental_length_days']

print(X.head())

# Perform train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
# initialize lasso
lasso = Lasso(alpha=0.3, random_state=9)
lasso_coef = lasso.fit(X, y).coef_ 

print(lasso_coeffs)
# Perform feature selection 
X_lasso_train, X_lasso_test = X_train.iloc[:, lasso_coef > 0], X_test.iloc[:, lasso_coef > 0]
# Linear Regression
lr = LinearRegression()
lr = lr.fit(X_lasso_train, y_train)
y_test_pred = lr.predict(X_lasso_test) 
mse_lr_lasso = mean_squared_error(y_test, y_test_pred) 

print(mse_lr_lasso)
# Random Search 
params = {'n_estimators': np.arange(1, 101, 1), 'max_depth':np.arange(1,11,1)} 

# Initialize Random Forest 
rf = RandomForestRegressor()  

# Initialize Random Search  
rand_search = RandomizedSearchCV(rf, param_distributions=params, cv=5, random_state=5) 

# Fit 
rand_search.fit(X_train, y_train) 

# Best params 
best_params = rand_search.best_params_  

print(best_params)
# Fit Random Forest 
rf = RandomForestRegressor(n_estimators=best_params['n_estimators'], max_depth=best_params['max_depth'], random_state=9)

rf.fit(X_train, y_train) 
rf_pred = rf.predict(X_test) 
mse_rf = mean_squared_error(y_test, rf_pred) 

print(mse_rf)
# Best model and mse 
best_model = rf
best_mse = mse_rf