House Price Prediction for Bangalore Locations: A Comprehensive End-to-End Data Science Project-2025

House Price Prediction for Bangalore: A Comprehensive End-to-End Data Science Project

Project Objective

The goal of this project is to build a predictive model for estimating house prices in Bangalore. By analyzing key features such as location, square footage, number of bedrooms, and other relevant factors, this model aims to assist buyers, sellers, and real estate professionals in making data-driven decisions.

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Project Overview

This project adheres to a systematic data science workflow, encompassing:

1. Problem Understanding
2. Data Collection
3. Data Preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature Engineering
6. Model Development
7. Model Evaluation
8. Deployment

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Steps Followed in the Project

1. Problem Understanding

• Define the problem: Predict house prices based on input features.
• Identify key features: Location, total square footage, number of bedrooms (BHK), number of bathrooms, and other influential attributes.

2. Data Collection

• Gathered housing data for Bangalore from reliable sources.
• Key attributes included:
  • total_sqft
  • location
  • BHK
  • bathrooms
  • price

3. Data Preprocessing

• Handled missing values:
  • Imputed missing values or removed rows with significant gaps.
• Cleaned data:
  • Removed duplicates and outliers.
  • Ensured consistency in data types (e.g., converted total_sqft to numeric).
• Scaled features: Applied normalization or standardization where needed.

4. Exploratory Data Analysis (EDA)

• Analyzed data distributions and identified trends:
  • Examined the price distribution and feature correlations.
  • Created scatter plots, histograms, and heatmaps for visualization.
• Key findings:
  • Certain locations significantly influence house prices.
  • "Price per square foot" emerged as a critical metric.

5. Feature Engineering

• Converted categorical data (e.g., location) into numerical formats using one-hot or label encoding.
• Created new features, such as price_per_sqft for more granular insights.
• Handled high-cardinality features by grouping rare categories into "Other."

6. Model Development

• Split the data into training and testing sets.
• Trained multiple models, including:
  • Linear Regression
  • Lasso Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • Gradient Boosting Regressor
• Performed hyperparameter tuning using GridSearchCV and RandomizedSearchCV to optimize model performance.

7. Model Evaluation

• Evaluated models using metrics:
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R-squared (R²)
• Selected the best-performing model based on evaluation results.

8. Deployment

• Developed a web application using Flask/FastAPI:
  • Users can input features like location, BHK, and total_sqft to predict house prices.
• Provided a user-friendly interface for seamless interaction.

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Files and Directories

• data/: Contains the dataset.
• notebooks/: Jupyter Notebooks for EDA, preprocessing, and modeling.
• models/: Serialized models in .pkl format.
• app/: Flask or FastAPI application for deployment.
• README.md: Comprehensive project documentation.

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Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Flask/FastAPI
• Tools: Jupyter Notebook, VS Code

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Results and Insights

• Achieved an R-squared score of 98% on the test set.
• Key insights:
  • Identified locations with the highest average house prices.
  • Highlighted the most influential features, such as location and price_per_sqft.

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Future Work

• Integrate additional data sources (e.g., proximity to amenities, crime rates) to improve accuracy.
• Enhance the web interface with interactive visualizations using Plotly or Dash.
• Extend the model to predict house prices in other cities.

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This project demonstrates the power of data science in solving real-world problems. By following a structured workflow and leveraging advanced machine learning techniques, we developed a robust predictive model to assist stakeholders in Bangalore’s real estate market. The complete code and resources are available on GitHub for replication and further exploration.

Sourse Code Available On GitHub: Click Here