Master's in Data Science — Deakin University
AI / ML / Data Engineering enthusiast. I build privacy-aware GenAI apps, ML models, and analytics dashboards.

📫 adarshkrishs@gmail.com | 🔗 linkedin.com/in/adarshkrishnasuresh

⚡ EV Charging Demand & Infrastructure Intelligence (Australia)

https://github.com/AdarshS123/ev-demand-intelligence-au

End-to-end EV infrastructure analytics system built with live API ingestion, ETL pipelines, PostgreSQL schema design, KPI computation, demand forecasting, FastAPI deployment, and Streamlit dashboard. Tech: Python, PostgreSQL, SQLAlchemy, Scikit-learn, FastAPI, Streamlit, Plotly

🧠 Building a RAG Chatbot (Local)

https://github.com/AdarshS123/rag-chatbot-local

Retrieval-Augmented Generation system using document retrieval with LLMs. Tech: Python, LangChain, LlamaIndex, FAISS, Docker

🏥 Hospital Inpatient Analytics

https://github.com/AdarshS123/Hospital_Inpatient_Analytics

Healthcare analytics project with interactive dashboards for inpatient data. Tech: SQL, Power BI, Data Modeling

💳 Credit Card Spend & Risk Analytics

https://github.com/AdarshS123/Credit-card-spend-risk-analytics

Exploratory data analysis and risk insights on customer spending patterns. Tech: Python, Pandas, NumPy, Matplotlib, Jupyter Notebook

📊 Retail Performance Analytics Dashboard

https://github.com/AdarshS123/Retail-Performance-Analytics-Dashboard-

End‑to‑end retail analytics system covering data generation, ETL workflows, KPI computation, and interactive dashboarding. Includes synthetic dataset creation, automated data cleaning, and multi‑page Power BI dashboards for sales, customers, and product performance. Tech: Python, Pandas, NumPy, Power BI, SQL, Data Modeling, ETL Pipelines

📊 Financial Analytics Dashboard (SQL + Excel)

https://github.com/AdarshS123/financial-analytics-dashboard

End-to-end financial data analysis project focused on revenue, expense, and profitability trends. Includes SQL-based data aggregation, profit margin analysis, budget vs actual comparison, and an interactive Excel dashboard with KPI cards and visual insights. Tech: MySQL, SQL, Excel, Data Analysis, Data Visualization

📊 Melbourne Rental Market Intelligence (2000–2025)

https://github.com/AdarshS123/melbourne-rental-market-intelligence

End-to-end rental market analytics project analysing suburb-level rental trends across Melbourne from 2000 to 2025. Includes data cleaning and transformation, time-series analysis (YoY growth, CAGR), regional affordability insights, and an interactive Power BI dashboard with KPI cards, heatmaps, and trend visualisations. Tech: Python, Pandas, Power BI, Data Analysis, Data Visualization

📊 Sales Performance Dashboard (Excel)

https://github.com/AdarshS123/sales-dashboard-excel

End-to-end sales analytics project focused on revenue, profitability, and operational performance across states, service categories, and sales agents. Includes data cleaning and standardisation using Excel functions (TRIM, IFERROR, XLOOKUP), KPI development (total sales, profit, order volume, average order value), and interactive dashboard design using PivotTables, slicers, and visual insights. Tech: Excel, Data Cleaning, Data Analysis, Data Visualization, PivotTables, Business Intelligence

📊 Customer Segmentation & Revenue Intelligence Dashboard (Power BI)

https://github.com/AdarshS123/customer-segmentation-dashboard

End-to-end customer analytics project focused on segmentation, revenue intelligence, and retention strategy. Includes data cleaning and transformation, DAX-based KPI computation (Total Revenue, Profit, AOV, CLV, Recency, Frequency), and an interactive multi-visual Power BI dashboard analysing customer behaviour, service performance, and regional trends. Features customer segmentation (High Value, At Risk, Mid Value, Low Engagement) and actionable insights to improve retention and business growth. Tech: Power BI, DAX, Power Query, Excel, Data Analysis, Data Visualization, Business Intelligence

📊 Sales Forecasting using Predictive Analytics

https://github.com/AdarshS123/sales-forecasting-project

End-to-end predictive analytics project focused on forecasting sales using historical retail data. Includes data cleaning, feature engineering, exploratory data analysis (EDA), and machine learning model development using Random Forest regression. Demonstrates model evaluation using MAE, RMSE, and R², along with insights into model limitations and opportunities for improvement. Tech: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, OpenPyXL

Python · SQL · R

Pandas · NumPy · Scikit-learn · Matplotlib

LangChain · LlamaIndex · FAISS · RAG

Power BI · Data Modeling · DAX

Docker · AWS · FastAPI · PostgreSQL