Welcome to my Data Science Portfolio, a curated collection of projects developed during the Workearly Data Science Bootcamp. This portfolio highlights my hands-on experience in data analysis, data visualization, and machine learning, using tools like Python, SQL, Tableau, and key libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn.
Each project is designed to solve real-world business challenges through data-driven insights, predictive modeling, and compelling visual storytelling.
- Languages: Python (Pandas, NumPy, Scikit-learn), SQL
- Visualization: Matplotlib, Seaborn, Tableau
- Data Processing: Jupyter Notebook
- Machine Learning: Supervised Learning Models (Scikit-learn)
Objective: Analyze financial and operational data of a company to uncover trends and provide strategic insights.
Key Skills: Data wrangling, financial KPIs analysis, visual dashboards.
Highlights:
- Performed comprehensive data cleaning and transformation
- Created visualizations for revenue trends and profit margins
- Identified actionable insights for business growth
Objective: Explore sales data to identify purchasing patterns and optimize inventory.
Key Skills: Exploratory Data Analysis (EDA), seasonal trend analysis, visualization.
Highlights:
- Analyzed sales volume across product categories and time periods
- Built visual dashboards to track peak sales periods
- Recommended inventory adjustments based on demand forecasting
Objective: Analyze historical betting data to detect profitable betting patterns and evaluate risks.
Key Skills: Statistical analysis, hypothesis testing, risk evaluation.
Highlights:
- Explored betting odds and outcomes to find high-probability strategies
- Visualized win/loss ratios across different market segments
- Assessed ROI of various betting approaches
Objective: Provide in-depth sales performance analysis for a retail environment, with a focus on customer segmentation and forecasting.
Key Skills: Predictive modeling, customer segmentation, advanced EDA.
Highlights:
- Clustered customers based on buying behavior
- Built predictive models to forecast future sales
- Delivered insights into customer lifetime value (CLV)
These projects reflect my ability to approach diverse business problems with data-driven solutions. From uncovering trends to forecasting outcomes, each project demonstrates a unique facet of my data science skillset, ready to be applied in professional environments.
I’m open to collaboration, feedback, and new opportunities. Feel free to reach out or explore more of my work!