I am an AI & Machine Learning Specialist with expertise in developing and deploying machine learning models for various applications. With a strong foundation in deep learning, natural language processing, and data science, I am dedicated to building innovative solutions that address real-world challenges.
This repository contains a Streamlit-based application designed for predicting house prices in the USA using a linear regression model. The application provides an interactive interface where users can input details about a house, such as square footage, number of bedrooms, bathrooms, floors, and zip code, to receive an estimated price.
- User Input: The application allows users to enter information about the house's characteristics.
- Prediction: The model generates an estimated price based on the input features.
- Model: The predictions are based on a linear regression model trained with historical housing data.
- User Interface: Built using Streamlit, providing a simple and intuitive interface for users to interact with.
- Streamlit: For creating interactive web applications.
- Scikit-learn: For loading and using the linear regression model.
- NumPy: For handling numerical operations and preparing input data.
- Joblib: For loading the pre-trained model.
The linear regression model used in this project has been trained on historical data to predict house prices based on several features. The model is designed to provide users with an estimated price based on the input they provide, helping them understand potential market value.
Feel free to connect with me for any questions or collaborations:
- GitHub: Ahmad-Ali-Rafique
- LinkedIn: Ahmad Ali Rafique
Thank you for checking out my project! I am always looking for new challenges and opportunities to apply my skills and knowledge in machine learning and AI.