Telco Customer Churn
Understanding why customers leave.
I bring curiosity to the numbers and clarity to the story.
A recent engineering graduate using SQL, Python, and Power BI to turn raw data into meaningful business decisions.

Real questions, thoughtful analysis, and insights that go beyond the dashboard.
Understanding why customers leave.
A clearer picture of sales performance.
Context-aware communication, when it matters.
I’m a 2026 Computer Science Engineering graduate from The Oxford College of Engineering, Bengaluru, with a CGPA of 8.5/10 and a genuine curiosity for what data can tell us.
Through two analytics internships and hands-on projects, I’ve worked with everything from customer churn to sales performance. I enjoy cleaning the messy details, asking the right questions, and making the findings easy to understand.
Detail-oriented, comfortable communicating insights, and ready to keep learning — I’m looking to begin my career in Data Analytics and Business Intelligence.
Analyzed telecom data for 7,043 customers. Cleaned and transformed datasets using Power Query, then developed an interactive Power BI dashboard with DAX measures and KPI cards to identify the customer segments most at risk of churn.
Used SQL to analyze a restaurant dataset and developed Power BI visualizations comparing ratings across cities and cuisines. Presented location and performance patterns through interactive dashboards.
A growing foundation across analytics, programming, and intelligent systems.
Interactive dashboards · KPI cards · Cross-filtering visuals · Data transformation · Business insights · Analytical problem-solving
Uncover trends, patterns, and business drivers in raw data using SQL, Python, Pandas, NumPy, and Excel.
Build interactive Power BI dashboards with DAX measures, KPI cards, Power Query, and cross-filtering visuals.
Turn analytical findings into recommendations for customer retention, sales planning, and performance monitoring.
Make complex findings clear with Power BI, Tableau, Matplotlib, and Seaborn, for every audience.
Prepare reliable datasets by resolving missing values, correcting data types, and removing duplicates.
Apply entry-level machine learning and Generative AI to practical solutions with Python, Flask, and Firebase.
Telecom customers analyzed
in the churn project
Month-to-month contract churn
in the telecom dataset
Revenue from Bars vs Bites
in the sales project
Engineering CGPA
Class of 2026
Project findings and academic highlights, not live business metrics.
The Oxford College of Engineering · Bengaluru, India
Interested in data analytics, business intelligence, or data-driven problem solving? I’d be happy to connect.