Training.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pickle

# Load the dataset (assuming it's saved as 'housing.csv')
data = pd.read_csv('housing.csv')

# Split features and target variable
X = data.drop('price', axis=1)
y = data['price']

# 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)

# Train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Save the trained model to a file
with open('model.pkl', 'wb') as file:
    pickle.dump(model, file)