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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
import datetime

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression

df = pd.read_csv("rental_info.csv", parse_dates=["rental_date", "return_date"])
df.head()
df["rental_length_days"] = (df["return_date"] - df["rental_date"]).dt.days
df.head()
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.info()
X = df.drop(["rental_length_days", "special_features", "rental_date", "return_date"], 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)

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
    
linreg = LinearRegression()
linreg.fit(X_train, y_train)
linreg_pred = linreg.predict(X_test)
linreg_mse = mean_squared_error(y_test, linreg_pred)
print("LinearRegressor has a MSE of {}".format(linreg_mse))

dt = DecisionTreeRegressor()
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)
dt_mse = mean_squared_error(y_test, dt_pred)
print("DecisionTreeRegressor has a MSE of {}".format(dt_mse))

# Random forest hyperparameter space
param_dist = {'n_estimators': np.arange(1,101,1),
          'max_depth':np.arange(1,11,1)}

# Create a random forest regressor
rf = RandomForestRegressor()

# Use random search to find the best hyperparameters
rand_search = RandomizedSearchCV(rf, 
                                 param_distributions=param_dist, 
                                 cv=5, 
                                 random_state=9)

# Fit the random search object to the data
rand_search.fit(X_train, y_train)

# Create a variable for the best hyper param
hyper_params = rand_search.best_params_

# Run the random forest on the chosen hyper parameters
rf = RandomForestRegressor(n_estimators=hyper_params["n_estimators"], 
                           max_depth=hyper_params["max_depth"], 
                           random_state=9)
rf.fit(X_train,y_train)
rf_pred = rf.predict(X_test)
mse_random_forest= mean_squared_error(y_test, rf_pred)

print("RandomForestRegressor has a MSE of {}".format(mse_random_forest))
best_model = dt
best_mse = dt_mse