Price Prediction Model

This repository contains a machine learning-based price prediction model that estimates prices of properties based on various features such as area type, availability, location, size, society, total square footage, number of bathrooms, balconies, and price.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Dataset
6. Model
7. Contributing
8. License

Introduction

The goal of this project is to create a model that can predict property prices using features such as area type, location, and other property details. This can assist users in making informed decisions when buying or selling properties.

Features

• Predict prices based on key attributes such as:
  • Area type (e.g., Built-up, Carpet, Super Built-up)
  • Availability (e.g., Ready to Move, Under Construction)
  • Location (specific neighborhood or district)
  • Size (e.g., 2 BHK, 3 BHK)
  • Society (name of the housing society)
  • Total square footage
  • Number of bathrooms
  • Number of balconies
• Scalable and easy to adapt for other attributes.
• User-friendly interface for predictions.

Installation

1. Clone the repository:

   git clone [https://github.com/your-username/price-prediction-model.git](https://github.com/Yash010704/Priceprediction-using-machinelearning.git)

Usage

1. Prepare the dataset (see Dataset).
2. Train the model using the provided scripts:

   python train_model.py

3. Use the trained model to make predictions:

   python predict.py --area_type <area_type> --availability <availability> --location <location> --size <size> --society <society> --total_sqft <total_sqft> --bath <bath> --balcony <balcony>

Example:

python predict.py --area_type "Built-up" --availability "Ready to Move" --location "Downtown" --size "2 BHK" --society "Palm Grove" --total_sqft 1200 --bath 2 --balcony 1

4. For additional options, refer to the --help command:

   python predict.py --help

Dataset

The dataset should include the following columns:

• area_type: Type of area (e.g., Built-up, Carpet, Super Built-up).
• availability: Status of the property (e.g., Ready to Move, Under Construction).
• location: Textual descriptions of neighborhoods or districts.
• size: Number of bedrooms (e.g., 2 BHK, 3 BHK).
• society: Name of the housing society.
• total_sqft: Numeric values representing the total property size.
• bath: Number of bathrooms.
• balcony: Number of balconies.
• price: Numeric values representing the property prices.

You can use a sample dataset provided in the data/ directory or prepare your own. Ensure the data is cleaned and formatted before training the model.

Model

The model is built using the following techniques:

• Data preprocessing: Handles missing values and encodes categorical features.
• Feature selection: Identifies key predictors of property prices.
• Machine learning: Uses algorithms like linear regression, random forests, or gradient boosting.

You can modify the config.py file to customize model parameters and hyperparameters.