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.

A explanation of each part of the code:

Import Required Libraries

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

• pandas: Used for data manipulation and analysis.
• train_test_split: Function to split data into training and test sets.
• LinearRegression: Linear regression model from scikit-learn.
• RandomForestRegressor: Random forest regression model from scikit-learn.
• mean_squared_error: Metric to evaluate the performance of regression models.

Load the Dataset

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df = pd.read_csv("rental_info.csv")

• pd.read_csv("rental_info.csv"): Reads the CSV file into a DataFrame named df.

Convert Date Columns to Datetime

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df['rental_date'] = pd.to_datetime(df['rental_date'])
df['return_date'] = pd.to_datetime(df['return_date'])

• pd.to_datetime(): Converts the 'rental_date' and 'return_date' columns to datetime objects for date calculations.

Calculate Rental Length in Days

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df['rental_length_days'] = (df['return_date'] - df['rental_date']).dt.days

• df['return_date'] - df['rental_date']: Calculates the difference between the return and rental dates.
• .dt.days: Converts the time difference to the number of days.

Check Unique Values in special_features

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print(df['special_features'].unique())

• df['special_features'].unique(): Prints unique values in the 'special_features' column to understand its contents before creating dummy variables.

Create Dummy Variables for special_features

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df['deleted_scenes'] = df['special_features'].apply(lambda x: 1 if 'Deleted Scenes' in str(x) else 0)
df['behind_the_scenes'] = df['special_features'].apply(lambda x: 1 if 'Behind the Scenes' in str(x) else 0)

• df['special_features'].apply(lambda x: ...): Applies a lambda function to each value in 'special_features' to create binary columns.
  • 'Deleted Scenes' in str(x): Checks if 'Deleted Scenes' is part of the string representation of the feature value.
  • 'Behind the Scenes' in str(x): Checks if 'Behind the Scenes' is part of the string representation of the feature value.

Verify the Number of Ones in deleted_scenes

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print(f"Number of 'Deleted Scenes': {df['deleted_scenes'].sum()}")

• df['deleted_scenes'].sum(): Sums the values in the 'deleted_scenes' column to verify the number of entries with 'Deleted Scenes'.

Prepare Features and Target

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features = ['amount', 'amount_2', 'rental_rate', 'rental_rate_2', 'release_year', 'length', 'length_2', 'replacement_cost', 'deleted_scenes', 'behind_the_scenes',
            'NC-17', 'PG', 'PG-13', 'R']
X = df[features]
y = df['rental_length_days']

• features: List of column names used as features for the regression model.
• X = df[features]: Creates a DataFrame X with selected features.
• y = df['rental_length_days']: Series y contains the target variable.

Split the Data into Training and Test Sets

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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)

• train_test_split(): Splits the dataset into training and test sets.
  • test_size=0.2: Uses 20% of the data for testing.
  • random_state=9: Ensures reproducibility by setting a seed for the random number generator.

Initialize Models

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models = {
    'Linear Regression': LinearRegression(),
    'Random Forest': RandomForestRegressor(random_state=9)
}

• models: Dictionary with model names as keys and initialized models as values.

Train and Evaluate Each Model

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best_model = None
best_mse = float('inf')

for name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    
    print(f'{name} MSE: {mse}')
    
    if mse < best_mse:
        best_mse = mse
        best_model = model

• best_model: Variable to keep track of the best-performing model.
• best_mse: Variable to keep track of the best mean squared error.
• model.fit(X_train, y_train): Trains the model using the training data.
• model.predict(X_test): Predicts the target values for the test set.
• mean_squared_error(y_test, y_pred): Computes the mean squared error of the model's predictions.
• if mse < best_mse: Updates the best model and MSE if the current model performs better.

Output the Best Model and Its MSE

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print(f'Best Model: {best_model.__class__.__name__}')
print(f'Best MSE: {best_mse}')

• best_model.class.name: Prints the name of the best model's class.
• best_mse: Prints the best mean squared error achieved by the model.