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

df['rental_length_days'] = (df['return_date'] - df['rental_date']).dt.days
#adding dummy variables using the 'special_features' column for Deleted and Behind scenes only
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.head()
X = df.drop(columns=[
    "rental_date",
    "return_date",
    "special_features",
    "rental_length_days"
])
y = df["rental_length_days"]
X_train, X_test, y_train, y_test = train_test_split(
    X, y, 
    test_size=0.20,       # 20% test set
    random_state=9,      
)
from sklearn.linear_model import Lasso

# 1. Fit Lasso regression
lasso = Lasso(alpha=0.01, random_state=9)
lasso.fit(X_train, y_train)

# 2. Extract coefficients
lasso_coef = pd.Series(lasso.coef_, index=X_train.columns)

# 3. Select features with non-zero importance
important_features = lasso_coef[lasso_coef > 0] 
print("Selected features:")
print(important_features)

# 4. Subset X_train and X_test
X_train_selected = X_train.loc[:, important_features.index]
X_test_selected = X_test.loc[:, important_features.index]

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import mean_squared_error, r2_score
lin_reg = LinearRegression()
lin_reg.fit(X_train_selected, y_train)

lin_pred = lin_reg.predict(X_test_selected)

tree = DecisionTreeRegressor(random_state=9)
tree.fit(X_train_selected, y_train)

tree_pred = tree.predict(X_test_selected)

rf = RandomForestRegressor(random_state=9)
rf.fit(X_train_selected, y_train)

rf_pred = rf.predict(X_test_selected)

score_linreg = mean_squared_error(y_test, lin_pred)
score_tree = mean_squared_error(y_test, tree_pred)
score_rf = mean_squared_error(y_test, rf_pred)
mse_dict = {
    'LinearRegression': score_linreg,
    'DecisionTree': score_tree,
    'RandomForest': score_rf
}

model_dict = {
    'LinearRegression': lin_reg,
    'DecisionTree': tree,
    'RandomForest': rf
}

best_model_name = min(mse_dict, key=mse_dict.get)
best_model = model_dict[best_model_name]
best_mse = mse_dict[best_model_name]