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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