🚀 Hi there! I'm Bin Feng, a Business Intelligence Engineer with a burning passion for all things Data Science and Machine Learning. I thrive on the thrill of exploring data, extracting insights, and turning them into actionable strategies.
📊 My journey in this field has been incredible, but I'm always hungry for more knowledge and skills. I firmly believe that continuous learning is the key to staying at the forefront of this dynamic industry. That's why I'm constantly seeking opportunities to sharpen my skills and delve into advanced models.
🤝 Collaboration is at the heart of my work ethic. I'm eager to team up with like-minded individuals to create something truly exceptional. Whether it's a groundbreaking project or a fascinating experiment, I'm all ears for fresh ideas and open to any advice or suggestions that can elevate our work.
💡 Let's innovate, explore, and make a positive impact together. Feel free to reach out, and let's embark on this exciting journey of data-driven discovery!
Thanks for connecting! 🌟
The main objective of this project is to explore different methods in exploratory data analysis and feature engineering. In the project, you can find the following:
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model development
The data was collected from realtor.com and special thanks to Ahmed Shahriar Sakib for the efforts of mataining the dataset in Kaggle.
More details about the dataset can be found: USA Real Estate Dataset
This exercise has mainly focused on EDA and feature engineering practices. After cleaning and preprocessing the given dataset, we then built a basic random forest model to estimate the house price. As we can see from the final results, we are able to obtain a pretty decent performance.
We can improve the performance by further hyperparameters tuning, instead of using default values for the model.
Please let me know your thoughts on this notebook, I would like to hear any advice and suggestions. Thanks for reading! Happy learning!