House Price Prediction using ML.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


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from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score


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data = pd.read_csv('BostonHousing.csv')


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data.head()


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data.info()


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print(data.isnull().sum())


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X = data.drop("medv", axis=1)  # Features (independent variables)
y = data["medv"]               # Target (dependent variable: Median value of homes)


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# 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)


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model = LinearRegression()
model.fit(X_train, y_train)


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# Predict on the test set
y_pred = model.predict(X_test)


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# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)


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print(f"Mean Squared Error: {mse}")
print(f"R-squared Score: {r2}")


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# Visualize the predicted vs actual values
plt.figure(figsize=(8, 6))
sns.scatterplot(x=y_test, y=y_pred)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted Prices")
plt.show()


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