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Built a customer churn analysis project using Python, SQL, and Excel to identify customers at risk of leaving. Performed data cleaning, exploratory analysis, and created visualizations to support retention strategies.
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I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.
Synthetic demo results:
• 20 raw order records
• 16 clean rows
• 2 duplicates removed
• 2 invalid rows quarantined
• $3,690 paid revenue reconciled
The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.
The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.
For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?
Title:
💳 Customer Churn Analysis Dashboard
Description:
Built an interactive Customer Churn Analysis Dashboard to identify customer churn patterns, retention trends, churn risk, customer lifetime value, and key factors influencing customer attrition. Designed dynamic KPI cards, predictive insights, and interactive visualizations using Microsoft Excel, SQL, and Power BI to help businesses improve customer retention, reduce churn, and make data-driven decisions.
Netflix has a massive content library, but how well is it balanced? Are they meeting audience preferences? The client needed a clear, data-backed understanding of their content composition specifically, the ratio of movies to TV shows, genre distribution, regional availability, and content rating trends, to inform strategic decisions about future content acquisition and production.
I conducted a comprehensive analysis of Netflix's content library using data sourced from Kaggle. My process and deliverables included:
1. Data Preparation (Excel): Cleaned and structured the raw dataset for analysis, ensuring data quality and consistency.
2. Data Querying (SQL): Wrote queries to extract key metrics—content count by type, genre distribution, rating categories, and country-wise availability.
3. Data Visualization (Tableau): Created interactive dashboards and visualizations to bring the data to life, including:
i. Bar Charts: Showed the dominance of Drama, Documentaries, and Stand-up Comedy as top genres.
ii. Map Visualization: Highlighted the USA (2,818 titles) and India (972 titles) as the countries with the most content.
iii. Pie Chart: Visualized the 69.6% (movies) vs. 30.4% (TV shows) split.
- Statistical Analysis (t-test): Performed a t-test confirming a statistically significant difference between movie and TV show uploads (t-value = 56.60, p-value = 0).
- Trend Analysis: Identified the 2019 peak in content uploads and the subsequent decline in 2020–2021, correlating with the rise of competing platforms and the COVID-19 economic downturn.
Strategic Recommendations: Provided actionable insights for Netflix:
i. Increase TV show content to balance audience preferences.
2. Experiment with short-form content to attract broader audiences.
3. Conduct user surveys to align content strategy with viewer demand.
4. Study competitor patterns and emerging trends to stay ahead.
A clear, data-driven analysis that provided Netflix with a comprehensive understanding of its content library and actionable recommendations to improve viewer engagement and retention.
