Data Analyst with hands-on experience in Python, SQL, Excel, and Power BI. Skilled in building dashboards, automating ETL pipelines, and applying machine learning techniques to real-world datasets. Strong background in data cleaning, visualization, and extracting business insights. Passionate about solving problems with data and supporting decision-making in fast-paced environments.
Queried a used-car dataset (20k+ records) to uncover patterns in pricing, mileage, age, and transmission type.
Created mileage buckets using CASE WHEN, revealing that cars in the 15–20 kmpl range held the strongest resale value.
Compared manual vs. automatic vehicles, finding automatic models sold at ~15% higher average prices.
Identified budget-friendly, high-mileage segments (e.g., petrol cars under ₹3 lakh), delivering insights valuable for buyers and resellers.
Built an interactive dashboard displaying KPIs such as total customers, orders, sales, impressions, and ad profit.
Implemented filters for year, quarter, customer segment, and payment method for dynamic reporting.
Visualized area-wise sales via tree map and monthly order trends using bar charts.
Cleaned and pre-processed raw sales data using Pandas and NumPy.
Conducted EDA with Matplotlib and Seaborn to identify sales trends, profit margins, and regional performance.
Delivered insights on top-performing categories and customer preferences, supporting targeted business strategies.
Queried a used-car dataset to uncover patterns in pricing, mileage, age, and transmission type.
Built an interactive dashboard displaying KPIs such as total customers, orders, sales, impressions, and ad profit.
Cleaned and pre-processed raw sales data using Pandas and NumPy.