📊 End-to-End Data Analysis Portfolio

Welcome to my data analytics portfolio! This repository houses a collection of end-to-end projects demonstrating my ability to solve real-world problems using data.

📂 Repository Structure & Industry Verticals

I have organized my projects according to standard Economic Sectors (Verticals) to demonstrate domain-specific analytics.

Data-Analytics-Portfolio/
│
├── .gitignore                   <-- CRITICAL: Ignores virtual envs, .DS_Store, and large data files
├── README.md                    <-- The Main Portfolio Landing Page
│
│   # -----------------------------------------------------------------------
│   # PROJECT 1: RETAIL / E-COMMERCE (Vertical: Logistics, Trade & Services)
│   # Focus: Engineering + Full Stack Analytics (Python -> SQL -> Tableau)
│   # -----------------------------------------------------------------------
├── 01_Ecomm_Retail_E2E/
│   ├── README.md                <-- 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Recommendation) & Project-specific docs
│   ├── requirements.txt         <-- Libraries: selenium, pandas, sqlalchemy, scikit-learn
│   │
│   ├── data/
│   │   ├── raw/                 <-- Scraped JSON/CSVs (e.g., amazon_prices.csv)
│   │   └── processed/           <-- Cleaned CSVs ready for SQL import
│   │
│   ├── scripts/                 <-- The Automation Engines (Data Engineering)
│   │   ├── 01_scraper_bot.py    <-- Selenium/BS4 script
│   │   └── 02_cleaning_etl.py   <-- Pandas script to clean & push to SQL
│   │
│   ├── sql/                        <-- 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Database Logic)
│   │   ├── schema_setup.sql        <-- CREATE TABLE code (Star Schema design)
│   │   └── analytical_queries.sql  <-- Complex queries used for analysis
│   │
│   ├── models/                      <-- 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (ML Models)
│   │   ├── churn_prediction.ipynb   <-- Notebook for Customer Churn (Logistic Regression)
│   │   └── sales_forecasting.ipynb  <-- Notebook for Time Series
│   │
│   └── dashboards/                         <-- 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
│       ├── Retail_Executive_Dashboard.twb  <-- Tableau Workbook
│       └── images/                         <-- Screenshots of dashboard for the README
│
│   # -----------------------------------------------------------------------
│   # PROJECT 2: E-SPORTS / STRATEGY (Vertical: Tech, Media & Strategy)
│   # Focus: Storytelling & Tool Mastery (Excel + Power BI)
│   # -----------------------------------------------------------------------
├── 02_E-Sports_Chess_Analysis/
│   ├── README.md
│   ├── data/                    <-- 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Power Query)
│   │   ├── raw_games.csv        <-- Kaggle dataset
│   │   └── processed_excel.xlsx <-- The Excel file with Power Query steps
│   │
│   └── dashboards/                      <-- 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
│       ├── Chess_Opening_Strategy.pbix  <-- Power BI File
│       └── images/                      <-- Screenshots of dashboard
│
│   # -----------------------------------------------------------------------
│   # PROJECT 3: BANKING (Vertical: BFSI)
│   # Focus: Statistical Rigor & Business Logic (Python + SQL + Tableau)
│   # -----------------------------------------------------------------------
├── 03_BFSI_Credit_Risk/
│   ├── README.md
│   ├── requirements.txt
│   ├── data/
│   │   └── loan_defaults.csv
│   │
│   ├── analysis/
│   │   ├── 01_EDA_and_cleaning.ipynb    <-- Diagnostic Analytics (Correlation matrix)
│   │   └── 02_risk_prediction_model.ipynb <-- 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Logistic Reg/Decision Tree)
│   │
│   └── strategy_report/
│       └── Credit_Risk_Strategy.pdf     <-- 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Recommendation)

---

🚀 Portfolio Highlights

Project	Vertical/Domain	Type	Tech Stack	Key Business Insight
01. Retail E2E Pipeline	🛒 Logistics & Trade (Retail/E-Comm)	Full Stack	Python (Selenium), SQL, Tableau	Developed a competitor price tracker and churn prediction model to identify high-risk customer segments.
02. Chess Analytics	♟️ Tech & Media (E-Sports/Strategy)	Descriptive/Diagnostic	Excel, Power BI	Analyzed opening repertoires to visualize win-rates; proved specific openings increase win probability by 15%.
03. Credit Risk Model	🏦 BFSI (Banking & Finance)	Predictive/Prescriptive	Python (Scikit-Learn), SQL, Tableau	Built a Logistic Regression model to predict loan defaults; recommended a strategy to reduce risk exposure by 12%.

---

📂 Project Details

1. The "Hero" Project: Retail Competitor & Sales Analytics

• The Goal: Build a completely automated pipeline to track competitor pricing and analyze internal sales health.
• The Workflow:
  • Extract: Web scraped product data using Selenium & BeautifulSoup.
  • Process: Cleaned data with Pandas and stored in a PostgreSQL database.
  • Analyze: Performed Customer Churn modeling (Binary Classification) and Sales Forecasting.
  • Visualize: Connected Tableau to the SQL database for a live executive dashboard.

2. Chess Opening Analysis

• The Goal: Translate complex game data into a visual story for non-technical users.
• The Workflow: Used Excel Power Query for data transformation and Power BI for interactive filtering of chess opening effectiveness.

3. BFSI Loan Default Prediction

• The Goal: Reduce financial risk by predicting bad loans before they are approved.
• The Workflow: Used Python for Diagnostic Analysis (Correlation Matrices) to find root causes of default, and Predictive Modeling (Logistic Regression) to flag high-risk applicants.

---

📊 Industry Overview

Just for the overview am providing all the core sector/industries data analyst work on (Just for information) :

🛠️ Technical Stack & Workflow

Analytics Phase	Primary Tools	Secondary Tools
Descriptive	SQL, PowerBI, Tableau	Excel
Diagnostic	Excel, SQL, PowerBI, Tableau	Python
Predictive	Python (Matplotlib/Seaborn)	SQL
Prescriptive	Python	Excel, PowerBI, Tableau

Tools Specifics

• Python(TRANSFORMER - Cleaning & Modelling): Pandas, NumPy, Scikit-Learn (ML), Matplotlib/Seaborn(DataViz).

• SQL (FETCHER - Large Dataset): PostgreSQL (Mostly), MySQL.

• BI & Viz: PowerBI (DAX, Power-Query), Tableau, Excel (AUDITOR - VBA/Pivot/Lookups).

• Others: Git, GitHub (Pages/Repo), VSCode, Jupyter, pgAdmin/Dbeaver (PostgreSQL).

  #	Vertical Name [70% work is in 3 vertical & desc/diag]	Economic Sector Classification	Industries Included (ICI, GICS, ISIC)	Analyst Focus Topic/proj/example
  1	Energy, Resources & Utilities [Dominant Analytics : Prescriptive]	Primary (Mining) Secondary (Power/Refining)	• India Core: Coal, Crude Oil, Natural Gas, Refinery Products, Electricity. • Global: Renewable Energy, Water Supply, Waste Management.	• Demand Forecasting & Load Balancing • Predictive Maintenance (Asset Management) • Production Optimization, EDA • Environmental Impact Analysis
  2	Agriculture, Food & Staples [Dominant Analytics : Descriptive]	Primary (Farming) Secondary (Processing)	• India Core: Fertilizers. • Global: Farming, Fishing, Forestry, Food & Beverage Processing (FMCG).	• Yield Prediction & Crop Modeling • Supply Chain Cold-Chain Integrity • Commodity Price Trend Analysis • Soil & Weather Pattern Correlation • Inventory Spoilage Reduction
  3	Heavy Manufacturing (Industrial 4.0) [Dominant Analytics : Diagnostic]	Secondary (Production)	• India Core: Steel, Cement. • Global: Automotive, Aerospace, Chemicals, Machinery, Textiles	• Quality Control (Six Sigma/Defect Rates) • OEE (Overall Equipment Effectiveness) • Supply Chain & Vendor Risk Management • Production Cycle Time Analysis • Safety Incident Reporting
  4	Construction & Real Estate [Dominant Analytics : Descriptive]	Secondary (Building) Tertiary (Leasing/Sales)	• Global: Infrastructure, Residential & Commercial Real Estate, PropTech, Smart Cities.	• Project Cost Overrun Estimation • Market Valuation & Price Indices • Rental Yield & ROI Analysis • Geographic/Spatial (GIS) Analysis • Occupancy & Vacancy Rate Tracking
  5	Logistics, Trade & Consumer Services [Dominant Analytics : Predictive]	Tertiary (Service & Distribution)	• Global: Retail, E-commerce, Wholesale, Transport (Rail/Air/Ship), Warehousing, Tourism.	• Route Optimization & Fleet Management • Customer Segmentation & Churn Analysis • Market Basket Analysis (Cross-selling) • Delivery Time Performance Metrics • Inventory Turnover & Demand Planning
  6	BFSI (Banking, Fin. Service & Insurance) [Dominant Analytics : Predictive]	Tertiary (Service) Quaternary (Analysis)	• Global: Commercial Banks, Insurance, Fintech, Stock Markets, Wealth Management.	• Credit Risk Assessment & Scoring • Fraud Detection Algorithms • Customer Lifetime Value (CLV) • Portfolio Performance Analysis • Claims Processing Efficiency
  7	Healthcare & Life Science [Dominant Analytics : Diagnostic]	Tertiary (Care) Quaternary (R&D)	• Global: Hospitals, Pharmaceuticals, Biotech, Medical Devices, Public Health.	• Patient Readmission Prediction • Clinical Trial Data Analysis • Epidemiology & Disease Mapping • Hospital Resource Utilization • Drug Efficacy Modeling
  8	Tech, Media & Strategy [Dominant Analytics : Desc/Diagnostic]	Quaternary (Knowledge) Quinary (Decision Making)	• Global: IT Services, Telecom, Education, Media, Sports, Government Policy, NGOs.	• Sentiment Analysis (NLP) • User Engagement & A/B Testing • Policy Impact Assessment • Educational Assessment Metrics • Player/Team Performance Analytics

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💻 How to Run These Projects

If you wish to run the code locally, follow the steps below:

1️⃣ Clone the repository

git clone https://github.com/sonimonish00/Data-Analytics-Portfolio.git

2️⃣ Navigate to the project folder

cd Data-Analytics-Portfolio/01_Ecomm_Retail_E2E

3️⃣ Install dependencies (for Python projects)

pip install -r requirements.txt

4️⃣ Open the Notebook/File

Launch Jupyter Lab, or open the .pbix / .sql files in their respective applications.

📫 Contact & Feedback

I am always open to feedback or collaboration opportunities!

GitHub: [sonimonish00](https://github.com/sonimonish00)

LinkedIn: [Monish Soni](https://www.linkedin.com/in/monishsoni/)

Email: sonimonish00[at]gmail[dot]com

Portfolio Website: [sonimonish00](https://sonimonish00.github.io/)