Welcome to my portfolio!

Here, data drives decision-making. I utilize SQL for efficient data management, Python and R for advanced analysis, Power BI and Tableau for impactful visualizations. My projects are dedicated to transforming complex data into actionable insights, enabling organizations to make informed decisions and achieve their objectives. Each project showcases my expertise in converting data into practical solutions that enhance business performance.

Adidas Sales Analysis Dashboard in Power BI

The Power BI project dedicated to Adidas sales analysis provides an in-depth look at the company's performance across the United States, leveraging advanced data visualization tools to offer valuable insights. This dynamic dashboard features a range of visualizations to depict sales trends by month, state, region, product, and retailer, allowing users to view data from multiple perspectives. With key performance indicators and detailed analyses, the dashboard helps users understand and leverage sales trends effectively. Interactive features such as slicers and filters enable users to drill down into specific data points and tailor their analysis to meet their needs.

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Blinkit Grocery Sales Analysis Dashboard in Power BI

Discover the power of data analytics with our comprehensive Power BI dashboard tailored for Blinkit's grocery sales. This dynamic dashboard offers a holistic view of business performance through meticulously crafted visualizations and key metrics. It captures essential metrics such as total sales, average sales, number of items sold, and average customer ratings, providing critical insights to evaluate overall performance and identify areas for improvement.

Building an Interactive HR Analytics Dashboard in Power BI

This Power BI dashboard provides a comprehensive analysis of employee data through dynamic visualizations such as heat maps, bar charts, donut charts, and matrix charts. It highlights key performance indicators (KPIs) like attrition rates, active employee counts, and job satisfaction levels, offering HR managers clear insights to support data-driven decision-making. The matrix chart, in particular, effectively displays job satisfaction ratings, enabling HR teams to better interpret employee feedback and trends.

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Netflix dashboard in Tableau

The Netflix dashboard in Tableau provides a powerful platform to visualize and analyze data on movies and TV shows, offering insights into content trends, regional availability, and viewer preferences. By plotting the number of movies and TV shows added over time using line charts or area graphs, we can uncover growth patterns in Netflix's content library. This analysis helps identify periods of accelerated expansion or slowdowns, which may correlate with marketing campaigns, strategic shifts, or external events. Additionally, temporal analysis can reveal user engagement patterns, such as spikes in activity following new releases or seasonal trends.

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PROJECTS

British Airways Reviews (Tableau)

This Tableau dashboard offers a comprehensive analysis of British Airways customer reviews, focusing on key metrics such as sentiment analysis, service quality, and temporal trends. By utilizing dynamic filters and interactive features, the dashboard provides actionable insights to improve customer satisfaction.

Overview:

The project demonstrates proficiency in data visualization, analyzing customer feedback to assess overall satisfaction with British Airways. The dashboard allows us to explore data across metrics like overall ratings, food quality, and service experience, with dynamic filters for date and geography. This approach helps highlight patterns in customer experiences, identify areas for improvement, and offer insights into performance across different classes and regions.

Video Game Sales Analysis Dashboard (Tableau)

The dashboard’s standout feature is its interactive visualizations, using parameters and calculated fields to display multiple sales zones, such as North America, Europe, and Japan, on a single graph. This streamlines data viewing and enhances clarity.

Another key feature is time range filtering, allowing users to focus on specific periods. This helps identify sales trends during events like product launches or holiday seasons, revealing shifts in consumer behavior.

The dashboard also enables regional comparisons, providing insights into the best-performing markets. By comparing areas like North America and Japan, businesses can tailor their strategies accordingly.

Aesthetically, dynamic charts like area graphs and tree maps make data visually appealing and easy to interpret. KPI tracking and top 10 filters further enhance the dashboard, offering a quick overview of top-selling games and platforms.

Overall, the dashboard’s intuitive design and powerful analysis tools make it an essential resource for businesses to optimize video game sales across regions and time periods.

Toman Bike Shop - Database Analysis Report (SQL & Power BI)

Goal:
This analysis will decide whether Toman Bike Shop will hike their prices this coming year using the available data of bike rentals and their price sensitivity. We used SQL and Power BI to analyze the revenue trends, customer segments, and seasonal demand in order to understand what impact a price increase will have on ridership, profits, and revenues.

Database Insights:
Seasonal Trends: Besides, rentals also reflect a very strong seasonal demand cycle, peaking in summer and holiday months, with low returns in the lean months, though residual activities due to their daily commutation needs are not uncommon.

Customer Segmentation: Most users are young, 20-35-year-old city dwellers; they use the service to go to work and for leisure. This category is less sensitive to price, uses the service more frequently, and especially uses mountain and electric bikes.

Revenue Trends: Current pricing strategies ensure a consistent revenue level throughout the peak seasons. However, from the data obtained, even in previous slight price increases, demand was almost equal. This suggests that some leeway may be allowed for price increases without seriously affecting the rental volumes.

Price Elasticity: Elastic price analysis indicates that riders are not responsive to small increases in prices. In those situations where the price increased 5-10%, rental activity either showed no decline or showed minimal decline. The customer is consistent with their use of the service-in particular, bike renters of mid-to-higher level bikes-despite the change in pricing.

Recommendations:
Apply a Reasonable Price Increment: With this price elasticity in mind, a modest price increase of 5-10% can be done during peak seasons, summer, and holidays. This will keep demand maximized while volumes remain stable during that time. During off-peak hours, prices can remain flat or be slightly discounted to maintain the ridership of cost-conscious riders.

Price targeting in Premium bikes: The electric and mountain bikes are most attractive to younger, urban riders who are relatively price insensitive. However, pricing each of these two bike types 10-15% higher will significantly enhance profit without deterring regular riders. The price of the standard bike would not be adjusted to prevent deterring budget-conscious customers.

Seasonal Pricing Strategy: Toman Bike Share should consider dynamic pricing to maximize revenue throughout the year. This can allow higher pricing during summer and holiday periods to maximize revenue, while special promotions or discounts given during the off-peak months can help to keep interest in riding and consistent usage of the bikes.

Customer Loyalty Programs: In this regard, to offset this possible concern with the price-sensitive customers, introduce a loyalty or subscription program that discounts frequent users or special rates for extended use. This encourages long-term customer retention even at higher prices.

Price to Better the Service: Any price increase should be justified by enhanced services. These could include the fact that the bikes are better maintained, the service area is expanded, or there are added benefits like bettered customer support. Marketing these improvements in tandem with the price adjustments would smooth out the positive customer experience and dampen negative reactions to increased costs.

Conclusion:
By applying dynamic pricing, in particular, to peak hours, Toman Bike Share can increase its pricing next year without significantly decreasing the number of riders. In this light, the company should seek an effective balance of dynamic pricing along with customer loyalty programs that would add to the revenues and profits while maintaining a good customer base. Pricing must be flexible enough to accommodate all market conditions and customer preferences.