regression_model.py

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

# Load the cleaned dataset with sentiment
data = pd.read_csv('/path/to/sentiment_IMDB.csv')

# Features and target variable
X = data.drop(['Name', 'Image-src', 'Description', 'Name-href', 'Rating'], axis=1)
y = data['Rating']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the Linear Regression model
model = LinearRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = model.predict(X_test)

# Compute the Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_pred)

# Print the MSE
print('Mean Squared Error:', mse)