Data Analyst Portfolio

Resume / DVA Portfolio

Abhijeet Sinha

I build DVA-ready analysis pipelines, dashboards, and concise resume-grade project stories across real estate pricing, streaming engagement, and e-commerce retention.

3 portfolio projects spanning ML, Sheets dashboards, and Tableau analysis
21,613 King County property records used in the price predictor
5,025 cleaned Netflix user records analyzed for engagement and revenue
97.19% Olist one-order customer rate surfaced in churn analysis

Professional Summary

Analytics, visualization, and business storytelling

  • Data analytics and visualization learner with hands-on experience in data cleaning, exploratory data analysis, dashboarding, and project reporting.
  • Skilled in Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Streamlit, Excel / Google Sheets, and Tableau.
  • Built DVA projects covering predictive real estate pricing, streaming platform engagement, subscription revenue analysis, and e-commerce churn.
  • Comfortable converting raw datasets into KPI frameworks, pivot tables, interactive visuals, and concise business recommendations.

Technical Skills

Grouped by workflow

Languages

Python Pandas NumPy Data Cleaning

Libraries

Matplotlib Seaborn Plotly

Tools & Visualization

Streamlit Tableau Google Sheets Excel Pivot Tables

Analytics

EDA Segmentation Regression Retention Analysis Business Analytics

Portfolio Projects

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Resume Project Detail

ATS-friendly bullets

House Price Prediction System

Python, Pandas, NumPy, Scikit-learn, Plotly, Streamlit, ReportLab | GitHub

  • Built a Random Forest regression workflow on 21,613 King County property sales, using engineered features such as house age, renovation flag, amenity score, and zipcode encoding.
  • Developed a Streamlit app to predict property price, confidence range, investment score, market status, comparable homes, and a downloadable PDF advisory report.
  • Improved model performance from linear regression baseline R2 of about 0.67 to Random Forest R2 of about 0.88, reducing MAE from roughly $110K to $80K.

User Engagement & Revenue Optimization for Streaming Platform

Google Sheets, Excel, Pivot Tables, Dashboarding, Revenue Analytics | GitHub

  • Cleaned and standardized a Netflix-style user behavior dataset from over 10,000 raw records into 5,025 analysis-ready rows and about 20 analytical columns.
  • Created pivot tables to analyze subscription revenue, active-rate retention, device engagement, content-type performance, genre depth, and churn behavior.
  • Designed an executive dashboard with slicers for subscription plan, country, device type, content type, and active status to support monetization decisions.

Olist Customer Churn Analysis

Python, Pandas, Tableau, EDA, KPI Design, Report Writing | GitHub

  • Contributed as Report Lead on an e-commerce churn capstone analyzing 99,441 raw Olist orders and a processed 105,000-row analytical sample across 19 columns.
  • Helped frame churn around 97.19% one-order customers, 2.81% repeat rate, R$14.69M total revenue, and delivery / review / payment segmentation.
  • Translated dashboard insights into retention recommendations including first-purchase vouchers, high-spend loyalty tiers, review recovery, and regional pilots.

Abhijeet Sinha

Data Analyst / Visualization Builder

abhijeet.2024@nst.rishihood.edu.in
linkedin.com/in/abhijeetnst | github.com/abhijeetnst
portfolio.abhijeet.pw

Professional Summary

Data analytics and visualization learner skilled in Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Streamlit, Excel / Sheets, and Tableau. Experienced in data cleaning, exploratory analysis, KPI dashboards, and translating analysis into business recommendations across real estate pricing, streaming engagement, and customer retention projects.

Skills

Languages: Python

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly

Tools & Visualization: Streamlit, Excel, Google Sheets, Tableau, Pivot Tables, GitHub

Analytics: Data Cleaning, EDA, Dashboarding, Segmentation, Regression, Retention Analysis, KPI Design

Projects

House Price Prediction System

Python, Pandas, NumPy, Scikit-learn, Plotly, Streamlit, ReportLab | GitHub

  • Built a Random Forest regression workflow on 21,613 King County property sales using engineered features including house age, renovation flag, amenity score, and zipcode encoding.
  • Developed a Streamlit app to predict property price, confidence range, investment score, market status, comparable homes, and a downloadable PDF advisory report.
  • Improved model performance from linear regression baseline R2 of about 0.67 to Random Forest R2 of about 0.88, reducing MAE from roughly $110K to $80K.

User Engagement & Revenue Optimization for Streaming Platform

Google Sheets, Excel, Pivot Tables, Dashboarding, Revenue Analytics | GitHub

  • Cleaned and standardized a Netflix-style user behavior dataset from over 10,000 raw records into 5,025 analysis-ready rows and about 20 analytical columns.
  • Created pivot tables to analyze subscription revenue, active-rate retention, device engagement, content-type performance, genre depth, and churn behavior.
  • Designed an executive dashboard with slicers for subscription plan, country, device type, content type, and active status to support monetization decisions.

Olist Customer Churn Analysis

Python, Pandas, Tableau, EDA, KPI Design, Report Writing | GitHub

  • Contributed as Report Lead on an e-commerce churn capstone analyzing 99,441 raw Olist orders and a processed 105,000-row analytical sample across 19 columns.
  • Helped frame churn around 97.19% one-order customers, 2.81% repeat rate, R$14.69M total revenue, and delivery / review / payment segmentation.
  • Translated dashboard insights into retention recommendations including first-purchase vouchers, high-spend loyalty tiers, review recovery, and regional pilots.