001 : Price Prediction by ML

This project tackles building a real estate price prediction model using machine learning. The final product will be a user-friendly website where you can input property details and get an estimated price.

Executive Summary: Real Estate Price Prediction Project

Objective:

Develop a machine learning model to predict property prices based on key features for a real estate company. This model will be integrated into a user-friendly website for price estimations.

Process:

  1. Data Acquisition: Obtain a property dataset from Kaggle containing features like square footage, bedrooms, bathrooms, and location.
  2. Data Cleaning and Preprocessing: Clean the data for inconsistencies and missing values. Engineer additional features to improve model performance.
  3. Model Building: Train a machine learning model (potentially using techniques like dimensionality reduction and outlier removal) to predict property prices based on the prepared data.
  4. Model Deployment: Export the trained model for integration with the web application.
  5. Web Application Development: Build a website using HTML, CSS, and JavaScript that interacts with the back-end server to enable users to input property features and receive predicted prices.

Tools and Technologies:

  • Programming Language: Python
  • Data Analysis Libraries: Pandas
  • Data Visualization Libraries: Matplotlib
  • Machine Learning Libraries: Scikit-learn
  • Web Framework: Flask
  • Front-end Development: HTML, CSS, JavaScript

Outcomes:

  • A machine learning model capable of predicting property prices based on relevant features.
  • A user-friendly web application for real estate price estimations.

Benefits:

  • Improved pricing accuracy for real estate listings.
  • Enhanced user experience for property buyers and sellers.
  • Increased efficiency in the real estate valuation process.

Target Audience:

This project is intended for data scientists and developers interested in real estate applications and web development using machine learning. The resulting web application can be a valuable tool for real estate professionals and potential home buyers.