I'm a software engineering graduate with a passion for data science. I enjoy turning complex data into actionable insights through machine learning, visualization, and analytics. This portfolio showcases my work in customer segmentation, sales forecasting, and supply chain optimization—focused on solving real-world business problems with data-driven solutions.
I have hands-on experience in data analysis, machine learning, time series forecasting, and customer segmentation. My skill set includes Python, Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. I'm proficient in data preprocessing, model evaluation, and communicating insights through clear visualizations and business-focused reporting.
Python
Based on data from Kaggle's mall dataset I utilized clustering algorithms to enhance targeted marketing. K-means algorithm was used to create clusters of customers with similar incomes and spending habits for the purpose of creating specialed ad compaigns.
Based on data from Kaggle's Walmart dataset I created a SARIMA model to forecast future sales. The data was divided into training data and test data to verify and optimize the strength of the SARIMA model. Sales forecasting is used to help stores keep from under or overstocking.