Project Overview

This project focuses on predicting house prices in King County, Washington, USA, using machine learning techniques. The dataset contains information on homes sold in King County, including various features like the number of bedrooms, bathrooms, square footage, and more. The goal is to build an accurate predictive model using this dataset and apply data cleaning, transformation, and various machine learning models to achieve the best performance.

Objectives

• Data Cleaning & Transformation: Prepare the dataset by handling missing values, transforming variables, and scaling data as needed.
• Feature Engineering: Identify and create important features that can improve model performance.
• Model Training & Evaluation: Apply multiple machine learning models (e.g., Linear Regression, Decision Trees, Random Forests, etc.) and evaluate them based on key performance metrics such as RMSE.
• Model Optimization: Fine-tune the best-performing model to further improve accuracy and prediction power.

Dataset

The dataset used for this project is publicly available and contains various details about properties sold in King County, such as:

• id: Unique identifier for the house
• date: Date of the sale
• price: Price of the house
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• sqft_living: Square footage of the living space
• sqft_lot: Square footage of the lot
• floors: Number of floors
• waterfront: Whether the property has a waterfront view
• view, condition, grade: Property condition-related features
• sqft_above, sqft_basement: Square footage of the house above and below ground level
• yr_built, yr_renovated: Year built and year of renovation
• zipcode, lat, long: Geographical location features

Steps Taken

Data Exploration and Analysis:

• Load and explore the dataset to understand its structure and the relationships between features.
• Visualize the data to detect trends and insights.

Data Preprocessing:

• Handle missing values and outliers.
• Transform categorical variables and normalize/standardize numeric features.
• Feature selection based on correlation and importance.

Modeling:

• Train multiple machine learning models (e.g., Linear Regression, Decision Trees, Random Forests).
• Evaluate models using metrics like RMSE, MAE, and R².

Model Tuning and Optimization:

• Use techniques like Grid Search and Cross-Validation to find the best hyperparameters.
• Compare models to choose the best-performing one for deployment.

Evaluation and Conclusion:

• Analyze the final model's performance and highlight its strengths and limitations.
• Discuss potential improvements and future steps.

Technologies Used

• Programming Language: Python
• Libraries: pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Machine Learning Models: Linear Regression, Decision Trees, Random Forests, Gradient Boosting, etc.

View the notebook here