Boston House Price Prediction Using Linear Regression

Developed a machine learning regression model to predict house prices using the Boston Housing dataset. The project covered the complete machine learning workflow, including data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and performance evaluation.

Key Features

Conducted Exploratory Data Analysis (EDA) to understand data distributions and feature relationships.

Handled missing values using median and mode imputation.

Standardized numerical features using StandardScaler.

Built and trained a Linear Regression model for house price prediction.

Evaluated model performance using MAE, RMSE, and R² Score.

Visualized data insights and model performance using Matplotlib and Seaborn.

Model Performance

Mean Absolute Error (MAE): 3.15

Root Mean Squared Error (RMSE): 5.00

R² Score: 0.659

Tools & Technologies

Python

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

Outcome

Successfully developed a regression model capable of predicting house prices based on housing characteristics, demonstrating practical experience in data preprocessing, regression modeling, model evaluation, and data visualization.