House price prediction is a fundamental application of machine learning in real estate. The objective of this project is to develop a model that predicts the price of a house based on various factors such as location, size, amenities, market trends, and economic indicators. The project involves data collection, preprocessing, exploratory data analysis (EDA), feature selection, model training, and evaluation using regression models like Linear Regression, Decision Trees, Random Forest, and XGBoost.
Programming Languages: Python, R, SQL Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras (if using deep learning) Software/Tools: Jupyter Notebook, Google Colab, Tableau (for visualization), PostgreSQL/MySQL (for database management) Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://scikit-learn.org/stable/
https://archive.ics.uci.edu/
https://www.zillow.com/research/
