Estimating the value of a house is one of the most important challenges in the real estate industry. The price of a property depends on several factors such as area, number of bedrooms, bathrooms, parking facilities, furnishing status, and location-related features.
In this project, I developed a Machine Learning model that predicts house prices using property-related information. The project covers the complete data science workflow, including data exploration, preprocessing, visualization, model training, evaluation, and interpretation of results.
This project was completed as part of my Data Science Internship to gain practical experience in applying machine learning techniques to real-world business problems.
The main objective of this project is to:
- Predict house prices using machine learning algorithms.
- Identify the factors that have the greatest impact on property value.
- Compare different regression models and evaluate their performance.
- Generate insights that can help buyers, sellers, and real estate businesses make informed decisions.
The dataset contains information about residential properties, including:
- Area
- Number of Bedrooms
- Number of Bathrooms
- Number of Stories
- Parking Availability
- Air Conditioning
- Basement Availability
- Guest Room Availability
- Furnishing Status
- Preferred Area
- House Price (Target Variable)
Dataset Source:
https://www.kaggle.com/datasets/yasserh/housing-prices-dataset
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-Learn
- Loaded the dataset using Pandas
- Explored data structure and feature information
- Checked for missing values
- Checked for duplicate records
- Removed duplicate entries
- Converted categorical variables into numerical format
- Prepared the dataset for machine learning models
Two regression models were implemented:
A simple and interpretable regression model used as a baseline approach.
An ensemble learning algorithm capable of capturing complex relationships within the data.
The models were evaluated using:
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- R² Score
After evaluation, Linear Regression achieved slightly better performance on this dataset, indicating that the relationship between the available features and house prices is relatively linear.
The following visualizations were created to better understand the dataset and model performance:
- House Price Distribution Histogram
- Correlation Heatmap
- Actual vs Predicted Price Scatter Plot
- Feature Importance Analysis
Some of the most influential factors affecting house prices were:
- Property Area
- Number of Bathrooms
- Parking Availability
- Air Conditioning
- Preferred Area
The analysis showed that larger properties with better amenities generally have higher market values.
Based on the analysis, real estate businesses can improve pricing strategies by focusing on features that significantly influence property value. Properties with larger areas, additional bathrooms, parking spaces, and modern amenities tend to attract higher prices and greater buyer interest.
Potential enhancements for this project include:
- Hyperparameter tuning for better model performance
- Testing advanced algorithms such as XGBoost
- Building an interactive web application using Streamlit
- Deploying the model for real-time predictions
B.Tech Computer Science and Engineering
Lovely Professional University (LPU)
Aspiring Data Scientist | Machine Learning Enthusiast | Python Developer
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