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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.
```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```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['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']]
data.info()``````
``````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
lasso.fit(X_train, 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 = ols.fit(X_lasso_train, 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),
'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)

# Random forest gives lowest MSE so:
best_model = rf
best_mse = mse_random_forest``````