I use this space to document my adventures in data science, sharing how I’ve tackle various challenges, discover useful insights from data, and more. Key concepts such as data cleaning, exploratory data analysis (EDA), feature engineering, and model building are frequently explored.
Below are some of my favorite articles, or you can visit the blog page to see my most recent posts. You can also view my personal website at JK Azalekor
Here is a sample of articles which I feel give you a good idea of what this site is all about.
This in-depth blog covers a complete machine learning pipeline built to predict credit default risk. It uses real-world financial data, advanced models (XGBoost, Random Forest), and ends with a deployed Streamlit application. The entire project is publicly available via GitHub and includes thorough evaluation and hyperparameter tuning steps.
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This analysis covers how to calculate the returns of security in Python, the volatility of financial assets, and how to measure them. Also, why diversification of assets in a portfolio is good and how to efficiently diversify your financial assets are discussed.
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This is an exploratory data analysis of a global employee survey conducted in 2021. It spans several countries in Europe, the Americas, and other parts of the world. Employees from various industries took part in the survey, making it very diverse. I hope you find insights interesting.
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This is a deep dive into the best performances of 100 meter runs for each year. The analysis is done for both male and female categories. At the end, there is a prediction for athletes who are most likely to podium in the upcoming Olympics.
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