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Bangalore Home Prices Prediction 🏠💰

Overview: This repository contains a machine learning project aimed at predicting home prices in Bangalore, India. The project utilizes various data science techniques and machine learning algorithms to analyze and predict home prices based on different features such as location, total square feet area, number of bathrooms, and number of bedrooms.

Dataset: The dataset used in this project is sourced from Kaggle and comprises information about house prices in Bangalore. It includes features such as area type, availability, location, size, total square feet, number of bathrooms, number of balconies, and price. The dataset provides a rich source of information for building predictive models to estimate home prices accurately.

Tools and Libraries Used:

• Python 🐍
• Pandas 🐼
• NumPy 🔢
• Matplotlib 📊
• Seaborn 🌊
• Scikit-learn 🧠
• Jupyter Notebook 📓
• Kaggle 📊

Project Structure:

• data: This directory contains the dataset used for analysis and model training.
• notebooks: This directory contains Jupyter Notebooks with the code for data exploration, data cleaning, feature engineering, model building, and evaluation.
• README.md: This file provides an overview of the project, including dataset description, tools used, project structure, and instructions for running the code.

Why Should You Use This Model? 🤔

• Accurate Price Prediction: The model predicts home prices accurately based on various features, helping buyers and sellers make informed decisions.
• Insightful Analysis: Through data exploration and visualization, the model provides insights into factors influencing home prices in Bangalore, aiding in market analysis.
• Customizable: The model can be adapted to include additional features or trained on different datasets for predicting home prices in other locations.
• Time-Saving: Instead of manual estimation, the model automates the process of predicting home prices, saving time for real estate professionals and buyers.
• Risk Mitigation: By providing accurate price estimates, the model helps mitigate the risk of overpaying or underselling properties.

Steps Involved:

1. Data Loading: Load the dataset into a pandas DataFrame.
2. Data Exploration: Explore the dataset to understand its structure and features.
3. Data Cleaning: Handle missing values, perform feature engineering, and clean data inconsistencies.
4. Feature Engineering: Add new features to enhance model performance.
5. Dimensionality Reduction: Reduce the dimensionality of categorical features.
6. Outlier Removal: Identify and remove outliers to improve model accuracy.
7. Model Building: Utilize linear regression to build a predictive model for home prices.
8. Model Evaluation: Evaluate the model's performance using various metrics and techniques.

Usage: To run the code and reproduce the results:

1. Clone the repository to your local machine.
2. Navigate to the 'notebooks' directory.
3. Open Jupyter Notebook and run the provided notebooks in sequential order.

Conclusion: This project demonstrates the application of data science and machine learning techniques to predict home prices in Bangalore. The predictive model developed as part of this project can be used by real estate stakeholders, buyers, and sellers to estimate property prices accurately. The code and methodology provided in this repository can be adapted and extended for similar predictive modeling tasks in other domains.

Contributing: Contributions to this project are welcome. Feel free to submit issues, suggest improvements, or open pull requests.

License: This project is licensed under the MIT License. See the LICENSE file for more details.