Entry-level Data Analyst skilled in SQL, Python & Power BI | Turning data into actionable insights.

Prajwal Devade  • Peerlist

Built an interactive Power BI dashboard analyzing 89,740+ Spotify tracks across 114 genres and 31,430+ artists.Key Insights:- The Beatles top the chart with 279 tracks- Average popularity score: 33.24/100- Top songs hit popularity score of 99- 91.45% tracks are non-explicit- High energy tracks tend to have higher danceabilityDataset: Spotify Tracks Dataset (Kaggle)

Spotify Streaming Insights Dashboard

Analyzed 1,025 patient records from the UCI Heart Disease dataset to uncover key risk patterns using Power BI.Key KPIs:- Disease Rate: 51.3%- Total Patients: 1,025- Average Age: 54- Average Cholesterol: 246Key Insights:- Asymptomatic chest pain shows highest disease correlation- Males have significantly higher disease rate than females- Reversible defect thalassemia most linked to heart disease- Low-tenure patients most at riskFeatures:- Interactive slicers for Age, Gender, Chest Pain Type- DAX measures for Disease Rate %- Clean medical theme — Red/Green color coding- Visuals: Donut, Bar Charts, Scatter Plot

Heart Disease Risk Analytics Dashboard

Analyzed 7,043 telecom customer records to identify churn patterns and retention opportunities. Tracked 26.5% Churn Rate using DAX measures — 1,869 churned vs 5,174 retained customers. Identified Month-to-month contracts as highest churn segment (3.9K). Discovered Fiber optic users and Electronic check payment show highest churn. Built interactive slicers for Contract, Internet Service and Gender with Navy Blue professional dark theme.

Telecom Customer Churn Dashboard

Analyzed bike store sales data to extract actionable business insights. Tracked ₹85.3M Revenue, ₹32.2M Profit across 113K orders with 37.8% profit margin. Identified 2015 as peak performance year. Discovered Bikes category as highest revenue segment. Found United States and Australia leading in country-wise sales with DAX measures for KPI tracking.

Bike Store Performance Dashboard

Analyzed 1,000 German bank loan applications to identify credit risk and default patterns. Tracked 700 Good Loans (70%) vs 300 Bad Loans (30%). Identified Radio/Television as most common loan purpose. Discovered existing credit history customers show highest default risk. Built interactive slicers for Purpose, Housing and Employment filtering.

German Credit Risk Dashboard

Analyzed 1,03,000+ OLA ride bookings to uncover performance patterns in Bengaluru. Tracked ₹57M+ Total Revenue with 62% Ride Success Rate. Identified 39K cancelled rides broken down by Customer vs Driver reasons. Built day-wise revenue trend, vehicle type treemap and booking status donut chart with payment method analysis.

Bengaluru OLA Performance Analysis

Analyzed mobile sales data across top brands and cities in India. Tracked ₹769M+ Total Sales, 19.2K Units Sold across 3,835 transactions. Compared brand performance across Xiaomi, Vivo, Samsung, Apple and OnePlus. Built interactive slicers for real-time filtering by Brand and City with dark-themed professional dashboard.

Mobile Sales Dashboard

Cleaned and analyzed 9,994 sales records to identify business performance patterns. Discovered discount-driven profit loss across underperforming segments. Tracked Total Sales, Profit, Orders and Average Order Value through dynamic KPI cards. Built interactive dashboard with filters for category and regional analysis.

Sales Data Analysis Dashboard

Built an interactive HR Attrition Dashboard using Power BI on a dataset of 1,470 employees.Key Metrics:- Attrition Rate: 16.12%- Attrition Count: 237- Avg Monthly Income: $6.50K- Avg Age: 37Key Insights:- R&amp;D has highest attrition (133)- Lab Technicians most affected (62)- Overtime employees leave more (127 vs 110)- 26-35 age group most at risk (116)Tools: Power BI | DAX | Data Visualization

HR Attrition Dashboard

- Total completed orders: Track the total number of successfully completed orders over time. - Average delivery times of completed orders across all cities: Measure delivery efficiency across regions. - Payment methods used: Analyze the preferred payment methods and their distribution. - Total revenue generated over time**: Assess sales performance trends over time. - Total revenue generated according to cities: Identify cities with the highest and lowest revenue generation. - Customer ratings: Analyze customer satisfaction based on ratings. - Highest average order value according to cuisine types: Discover which cuisine types generate the highest order values. - Total delivery times as per different order statuses: Examine delivery times across order statuses (e.g., completed, canceled). - Delivery times according to different cuisine types: Compare delivery times based on different types of cuisines. - Average number of items per order for different payment modes: Understand customer ordering behavior across various payment methods.

Zomato Data Analysis

Developed a Financial Fraud Detection system during my internship at Infotact Solutions. The project combined Python-based machine learning with Power BI dashboards to identify and visualize fraudulent activities in financial transactions.🔹 Approach:<ul><li>Cleaned and preprocessed 100K+ credit card transactions in Python.</li><li>Built a Random Forest Classifier to predict fraudulent cases.</li><li>Evaluated the model using Confusion Matrix, Precision-Recall, and ROC Curve.</li></ul>🔹 Key Insights from Dashboard:<ul><li>Fraud Rate: 1% of total transactions (1K frauds out of 100K).</li><li>Top High-Risk Cities: Chicago, San Diego, Dallas.</li><li>Monthly Fraud Trends: Peaks in January and July.</li><li>Fraud by Transaction Type: Refunds and Purchases equally impacted.</li><li>Fraud vs Legitimate Distribution clearly showing class imbalance.</li></ul>🔹 Impact: Enabled risk teams to quickly analyze fraud hotspots, trends, and transaction anomalies, making fraud detection more interpretable for decision-makers.

Financial Fraud Detection Dashboard

Worked on a Shipfast Logistics case study using Python for data cleaning and Looker Studio for dashboarding to address key operational challenges.Problem: Optimize shipment processes to reduce delays and costs while improving customer satisfaction and profitability.Key Metrics Analyzed:<ul><li>Total Sales: ₹2.3M</li><li>Total Profit: ₹286.4K</li><li>Unique Orders: 5,009</li><li>Average Sales: ₹229.86</li><li>Unique Customers: 793</li></ul>Key Findings:<ul><li>High cancellation rates caused by service quality issues.</li><li>Sales increasing, but declining service ratings may reduce repeat customers.</li><li>Consumer segment led in sales; corporate and home office underperformed.</li><li>West region contributed 31.6% of sales, Central &amp; South lagged.</li></ul>Impact: The analysis revealed improvement opportunities in delivery times, service quality, and regional strategy to retain customers and improve profitability.

Shipfast Logistics Case Study | Looker Studio

Data Analytics professional at Schenider Electric having 2+ years of experience with Excel,SQL, Python,Tableau

The BlinkIT Analytics Project is a data-driven initiative aimed at optimizing operations and driving revenue growth for BlinkIT, a leading grocery retail chain. This project focused on analyzing sales trends, outlet performance, and customer buying behaviors to deliver actionable insights.Using Excel for data preparation and analysis, combined with interactive dashboards in Looker Studio, the project identified top-performing outlets, high-revenue product categories, and pricing strategies aligned with customer preferences.The insights provided have supported strategic decision-making across operations and marketing teams, helping BlinkIT enhance efficiency, improve profitability, and align its offerings with market demands.This project highlights the transformative power of data analytics in creating impactful business solutions.