King County House

# House Price Prediction

A Machine Learning regression project that predicts house prices based on different property features such as living area, number of bedrooms and bathrooms, location, house grade, condition, and other characteristics.

The project uses the **King County House Sales dataset** and follows a complete Machine Learning workflow, starting from data exploration and cleaning to model training, evaluation, and predicting prices for new houses.

### What I did

* Explored and analyzed the house sales dataset.

* Checked missing values, duplicates, and unusual data points.

* Extracted useful information from the sale date.

* Selected relevant features for price prediction.

* Split the data into training and testing sets.

* Trained and compared multiple regression models.

* Evaluated the models using MAE, RMSE, and R².

* Analyzed feature importance to understand which factors have more influence on house prices.

* Used the trained model to predict the price of a new house.

### Technologies

**Python · Pandas · NumPy · Matplotlib · Scikit-learn**

### Machine Learning Models

* Linear Regression

* Random Forest Regressor

* Gradient Boosting Regressor

### Goal

The main goal of this project was to build a practical regression model and understand the complete workflow of a real-world Machine Learning problem, from raw data to price prediction.