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Project: Predicting Movie Rental Durations

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 matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV
from sklearn.metrics import mean_squared_error as MSE
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

# Import any additional modules and start coding below
# data import and preprocessing
df = pd.read_csv('rental_info.csv', parse_dates=['rental_date', 'return_date'])
df.info()
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', na=False), 1, 0)
df['behind_the_scenes'] = np.where(df['special_features'].str.contains('Behind the Scenes', na=False), 1, 0)
df.head(10)
SEED = 9
X = df.drop(['special_features', 'rental_length_days', 'return_date', 'rental_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=SEED)
#best feature selection
lasso = Lasso(alpha=0.1, random_state=SEED)
lasso_coef = lasso.fit(X_train, y_train).coef_
X_train_lasso, X_test_lasso = X_train.iloc[:, lasso_coef>0], X_test.iloc[:, lasso_coef>0]
models = {'Linear Regression':LinearRegression(), 'Decision Tree Regressor':DecisionTreeRegressor(), 'Random Forest Regressor':RandomForestRegressor()}
results= []
for model in models.values():
    kf = KFold(n_splits=6, random_state=SEED, shuffle=True)
    cv_results = cross_val_score(model, X_train_lasso, y_train, cv=kf)
    results.append(cv_results)
plt.boxplot(results, labels=models.keys())
plt.show()
rf = RandomForestRegressor(random_state=SEED)
params_rf = {'n_estimators': [300,400,500], 'max_depth':[4,6,8], 'max_features': ['log2', 'sqrt']}
#print(rf.get_params())
grid_rf = GridSearchCV(estimator=rf, param_grid=params_rf, cv=3, scoring='neg_mean_squared_error', n_jobs=-1)
grid_rf.fit(X_train, y_train)
best_hyper = grid_rf.best_params_
print('Best hyperparameter: \n', best_hyper)
best_model = grid_rf.best_estimator_
#predicting test set
y_pred = best_model.predict(X_test)
best_mse = MSE(y_test,y_pred)
print('The best model is: {} and its MSE is: {}'.format(best_model, best_mse))