Greetings,

My name is Soumyadeep Das and I am a student at Techno India University. In this project, I will be exploring a dataset related to House Price Prediction.

This challenge is focused on house price prediction in India, where the goal is to accurately predict the prices of properties using 12 influencing factors. Buyers consider various factors besides the size of the house, which makes predicting house prices a complex task. The dataset has been collected from various property aggregators across India.

For this project, I have utilized the K-Nearest Neighbors (KNN) regression algorithm to predict house prices. KNN is a non-parametric algorithm that uses the distance between instances to predict the output value. In the context of this project, the KNN regressor analyzes the 12 influencing factors provided in the dataset to determine the nearest neighbors to a given property, and then predicts the price of the property based on the prices of its neighbors.

This project is Intel OneAPI Optimized(Intel extension for scikit-learn, which is a version of scikit-learn optimized for Intel architecture). This means that the project has been specifically designed to run efficiently on Intel hardware, potentially improving performance and reducing runtime.