I used Python and libraries like Pandas, Seaborn and Matplotlib to analyze the Prosper Loan dataset. After loading and cleaning the data, I created visualizations to identify patterns and relationships between variables. Finally, I used statistical techniques like hypothesis testing to draw insights and make predictions about the loan data
I used Python codes to assess the Online Sales data quality, clean the dataset, and ensure data integrity. I put specific checks in place to ensure accurate analysis and visualization.
I created an interactive dashboard using Power BI for a Data Visualization project that offered insights from the Parch and Posey Dataset.To present the order and sales data based on product demand, region, and time period, I designed various visualizations such as bar and line charts. Additionally, I utilized filters and slicers to enable users to interact with the data and analyze it from diverse viewpoints.
I wrote SQL queries to extract and filter data based on specific criteria. To retrieve data from multiple tables, I used JOIN statements to combine related data based on shared columns. Finally, I reviewed the queries and made any necessary revisions to improve performance or readability.