Data Analyst • Business Intelligence • Data Visualization

Transforming raw data into meaningful insights, interactive dashboards, and data-driven business decisions.

I'm an aspiring Data Analyst with a strong foundation in SQL, Python, Power BI, Advanced Excel, and Data Visualization. I enjoy transforming complex datasets into meaningful business insights through analytical thinking, interactive dashboards, and data storytelling.

• 📊 Passionate about Business Analytics & BI
• 🐍 Skilled in Python for Data Analysis
• 🗄️ Comfortable working with SQL & MySQL
• 📈 Building interactive Power BI dashboards
• 🧹 Interested in Data Cleaning & Exploratory Data Analysis
• 🤖 Learning Machine Learning for predictive analytics
• 💻 Explore my Portfolio to discover my projects, skills, and journey as a Data Analyst.

Collect → Clean → Analyze → Visualize → Communicate → Recommend

I believe great analytics is not only about finding numbers — it's about helping businesses make better decisions.

Business Objective

Analyze telecom customer behavior to identify churn patterns and develop a predictive machine learning solution supported by an interactive Power BI dashboard.

• Cleaned and standardized telecom customer data
• Performed SQL-based customer and churn analysis
• Conducted Exploratory Data Analysis using Python
• Created customer segments and tenure groups
• Built a Random Forest classification model
• Evaluated feature importance and model performance
• Designed an interactive Power BI dashboard
• Generated business-oriented churn recommendations

SQL • MySQL • Python • Pandas • NumPy • Scikit-learn • Power BI • DAX • Excel

🔗 Repository: View GitHub Repository

📊 Dashboard: View Power BI Dashboard

📄 Documentation: View Project Report

Identified customer behavior and segments associated with higher churn risk to support data-driven customer retention strategies.

Business Objective

Analyze employee data to understand attrition factors and support HR decision-making through predictive analytics.

• Employee data preprocessing
• Exploratory Data Analysis
• Attrition trend analysis
• Feature engineering
• Random Forest classification
• Model evaluation
• Feature importance analysis
• HR business insights

Python • Pandas • NumPy • Matplotlib • Scikit-learn • Power BI

🔗 Repository: View GitHub Repository

📊 Dashboard: View Power BI Dashboard

📄 Documentation: View Project Report

Used employee data to identify potential attrition drivers and provide insights that can support employee retention strategies.

Business Objective

Create an interactive business intelligence dashboard for monitoring sales performance and key business KPIs.

• Revenue analysis
• Monthly sales trends
• Regional performance
• Customer performance
• Product/category analysis
• KPI monitoring
• Interactive dashboard storytelling

Power BI • SQL • Excel • DAX • Power Query

🔗 Repository: View GitHub Repository

📊 Dashboard: View Power BI Dashboard

📄 Documentation: View Project Report

Designed the dashboard to help decision-makers quickly understand sales performance, trends, customer behavior, and regional opportunities.

I focus on building dashboards that answer business questions rather than simply displaying charts.

• Power Query
• Data Cleaning
• Data Transformation
• Data Modeling
• Star Schema
• Table Relationships
• DAX Measures
• Calculated Columns
• KPI Design
• Time Intelligence
• Drill-through Reports
• Interactive Filters & Slicers
• Business Storytelling

Goal: Transform business data into clear, actionable visual insights for decision-makers.

SQL is one of my primary tools for transforming relational datasets into analysis-ready information.

• SELECT
• WHERE
• GROUP BY
• HAVING
• JOINs
• CASE Statements
• Subqueries
• Common Table Expressions
• Window Functions
• Aggregate Functions
• Date Functions
• NULL Handling
• Views
• Query Optimization
• Data Validation

SELECT • WHERE • GROUP BY • HAVING • JOINs • CASE • Subqueries • CTEs • Window Functions • Aggregate Functions • Date Functions • NULL Handling • Views • Query Optimization

SELECT
    CustomerSegment,
    COUNT(*) AS Customers,
    SUM(Revenue) AS TotalRevenue
FROM customers
GROUP BY CustomerSegment
ORDER BY TotalRevenue DESC;

I'm actively building my career in the following roles:

• Data Analyst
• Business Intelligence Analyst
• Junior Data Analyst
• Reporting Analyst
• Business Analyst

My professional focus combines:

Data + Business + Visualization + Decision Making

My resume is available below.

“Good analysis explains what happened. Great analysis explains why it happened and what to do next.”

I believe data becomes valuable only when it helps people make informed business decisions.

Let's turn data into meaningful decisions. 📊

⭐ If you find my projects interesting, feel free to explore my repositories and connect with me on LinkedIn.

☕ Sᴜᴘᴘᴏʀᴛ Mʏ Wᴏʀᴋ

If you find my projects helpful, consider supporting my work!