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Project: Predicting Movie Rental Durations
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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
    import pandas as pd
    from datetime import datetime as dt
    data = pd.read_csv('rental_info.csv')
    data['rental_date'] = pd.to_datetime(data['rental_date'])
    data['return_date'] = pd.to_datetime(data['return_date'])
    data['rental_length_days'] = (data['return_date'] - data['rental_date']).dt.days
    data['deleted_scenes'] = [1 if "Deleted Scenes" in special_features else 0 for special_features in data['special_features']]
    data['behind_the_scenes'] = [1 if "Behind the Scenes" in special_features else 0 for special_features in data['special_features']]
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import Lasso
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import mean_squared_error
    from sklearn.model_selection import RandomizedSearchCV
    import numpy as np
    # Choose columns to drop
    cols_to_drop = ["special_features", "rental_length_days", "rental_date", "return_date"]
    # Define X and y
    X = data.drop(cols_to_drop, axis=1)
    y = data["rental_length_days"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
    # Create the Lasso model
    lasso = Lasso(alpha=0.3, random_state=9) 
    # Train the model and access the coefficients, y_train)
    lasso_coef = lasso.coef_
    # Perform feature selectino by choosing columns with positive coefficients
    X_lasso_train, X_lasso_test = X_train.iloc[:, lasso_coef > 0], X_test.iloc[:, lasso_coef > 0]
    # Run OLS models on lasso chosen regression
    ols = LinearRegression()
    ols =, y_train)
    y_test_pred = ols.predict(X_lasso_test)
    mse_lin_reg_lasso = mean_squared_error(y_test, y_test_pred)
    # Random forest hyperparameter space
    param_dist = {'n_estimators': np.arange(1,101,1),
    # 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, 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),y_train)
    rf_pred = rf.predict(X_test)
    mse_random_forest= mean_squared_error(y_test, rf_pred)
    # Random forest gives lowest MSE so:
    best_model = rf
    best_mse = mse_random_forest