BD
Bhupesh Dewangan Data Analyst Projects
Analytics portfolio

Analytical projects built around data, decisions, and measurable insight.

This page highlights portfolio work across SQL, Excel, Power BI, BigQuery, Looker Studio, statistics, and business analysis. The content is the same as your original projects, but redesigned into a cleaner, more professional portfolio experience.

Selected projects

These cards preserve your original work while improving readability, structure, and recruiter-facing presentation.

Marketing analytics
01

Marketing Campaign Analysis (FB vs AdWords)

Google Colab, hypothesis testing, regression, time series analysis

  • Applied regression, Random Forest, and ensemble learning to analyze campaign performance and reached 76% prediction accuracy for conversion rates.
  • Found a strong 0.87 correlation between Facebook ad clicks and conversions, supporting more data-driven spending decisions.
  • Used hypothesis testing to compare Facebook and AdWords performance and support smarter budget allocation.
  • Analyzed weekday conversion behavior and found stronger performance on Mondays and Tuesdays for campaign planning.
  • Identified lower CPC periods, including May and November, to support more cost-effective ad scheduling.
RegressionHypothesis TestingTime SeriesMarketing Data
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Churn prediction
02

Pilot Program for Churn Model

BigQuery, Python, Looker Studio

  • Built an AutoML pilot workflow to predict employee churn using integrated datasets with 15K+ rows.
  • Used BigQuery for merging and transforming multi-source data, then trained models in notebooks with PyCaret.
  • Selected a Random Forest model with reported 99% accuracy and built dashboards in Looker Studio for KPI tracking and churn insights.
  • Identified satisfaction level as the strongest factor and highlighted technical and support teams as higher-risk departments.
  • Reported an overall churn rate of 7% to support strategic HR decision-making.
BigQueryPyCaretLooker StudioRandom Forest
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Revenue analysis
03

Maximizing Revenue for Drivers

Statistics, hypothesis testing in Python, Google Colab

  • Used statistical analysis and hypothesis testing to identify revenue-maximizing strategies for taxi drivers.
  • Performed core preprocessing tasks such as duplicate filtering and null-value handling to improve reliability.
  • Analyzed payment behavior using charts and found a split of 67.3% card and 32.7% cash transactions.
  • Used visual analysis and cleaned data to support more meaningful statistical conclusions and operational insights.
PythonStatisticsHypothesis TestingEDA
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SQL exploration
04

Famous Painting Analysis

SQL project, pgAdmin, MS Excel, data manipulation

  • Used SQL joins, group by, and window functions to analyze artworks, artists, museums, pricing patterns, and location trends.
  • Identified dominant styles such as Impressionism and highlighted artists like Renoir, Monet, and Van Gogh.
  • Extracted 20+ insights related to pricing, market demand, consumer behavior, and museum accessibility.
SQLpgAdminExcelWindow Functions
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BI dashboard
05

Customer Churn Analysis

Power BI, MS Excel, DAX, data transformation, visualization

  • Built a churn analysis dashboard in Power BI using transformed data, reference tables, and DAX-driven calculations.
  • Created pie, line, and bar charts to improve understanding of churn segments and patterns.
  • Found key churn characteristics across gender, activity status, credit card ownership, and country-level distribution.
  • Reported churn share breakdowns including 44% female and 56% male among churned customers, with country-specific churn patterns across Spain, Germany, and France.
Power BIDAXExcelDashboarding
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Excel dashboard
06

Coffee Sales Analysis

MS Excel, pivot tables, data modeling, data visualization

  • Presented this project as an Excel-based sales dashboard and analysis portfolio piece using pivot tables and visual summaries.
  • Your pasted description appears to repeat the painting-analysis text, so this card keeps the project category but should be updated with the real coffee-sales outcomes.
  • Once you share the actual coffee project details, this section can be rewritten to match the accuracy and polish of the other cards.
ExcelPivot TablesDashboardingSales Analysis
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