I’m a Data Analyst who specializes in data cleaning, analysis, and visualization. I work with Python, SQL, Excel, and Power BI to turn messy data into accurate insights and easy-to-understand reports. Reliable, detail-oriented, and deadline-driven.
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This interactive dashboard offers a comprehensive analysis of BlinKit’s sales performance across various product categories, outlet types, and regions. Designed in Power BI, it visualizes critical KPIs including total sales, average sales, item count, and customer ratings. The dashboard helps stakeholders monitor trends, identify top-performing outlets, and make data-driven decisions on product placement and customer engagement.
Key Insights:
💰 Total Sales: $1.20M across 8,523 items with an average rating of 3.9
📊 Sales Breakdown: By item type, fat content, outlet tier, and location
🏪 Outlet Performance: Comparison by size, type, and establishment year
📈 Trend Analysis: Sales trends from 2010–2021 with peak performance in 2019
📍 Filter Panel: Dynamic filters for outlet location, size, and item type
Tools Used: Power BI • DAX • Excel • Data Modeling • Data Cleaning
This interactive Power BI dashboard provides a comprehensive analysis of Amazon Prime Video’s content library from 1920 to 2021. It offers key insights into the platform’s global distribution, content trends, and viewer ratings, helping stakeholders understand the depth and diversity of the streaming service.
Key Insights:
📊 Total Titles: 9,655 across 519 genres and 5,771 directors
🕒 Time Range: Content spans from 1920 to 2021
⭐ Ratings Breakdown: Visual summary of age group content ratings
🌍 Geographic Distribution: Heatmap showing where content was first released
🎭 Genre Distribution: Drama and Comedy dominate the platform’s content
🎥 Content Type: Majority are movies (80.82%) vs. TV shows (19.18%)
📈 Release Trend: Line chart reveals steady growth in content post-2000
Tools Used: Power BI • Data Modeling • DAX • Visualization Design
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