Live Application

🔗 https://housepredict112.streamlit.app/

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🏠 Intelligent Property Price Advisory System

Overview

This project is an AI-driven real estate analytics system that predicts property prices using historical housing data and provides structured buying or investment recommendations.

It combines machine learning for price prediction with an agent-based advisory module to assist users in making informed real estate decisions.

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Objectives

• Predict property prices or price ranges
• Identify key price-driving factors
• Provide investment/buying insights
• Build an interactive user interface
• Extend into an agentic AI advisory assistant

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Dataset

• Kaggle Housing Prices Dataset
• Dataful Real Estate Dataset

Features include location, size, rooms, age, and amenities.

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Tech Stack

• Languages: Python
• Libraries: pandas, NumPy, scikit-learn
• Frontend: Streamlit
• AI/ML: LangGraph / LangChain
• Vector DB: FAISS / ChromaDB (optional)

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Features

• Property data upload
• Price prediction
• Model performance metrics
• Advisory report generation
• Market insight summaries

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Project Structure

• data/: Contains raw and processed datasets.
• notebooks/: Jupyter notebooks for data exploration and model training.
• src/: Source code for the application.
• requirements.txt: Python dependencies.

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Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook / Lab

Installation

1. Clone the repository

   git clone https://github.com/Nandann018-ux/Valdyr.git
   cd Valdyr

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate 

3. Install Dependencies

   pip install -r requirements.txt

Usage

1. Run Jupyter Lab

   jupyter lab
   
   #or
   
   python3 -m jupyter lab

   Navigate to notebooks/data_exploration.ipynb to start exploring the data.