**Geospatial Data Analyst | Geographer | Data Execution and Operations | Logistics | Communication
- 📫 How to reach me: Enter your email here
- 😄 Pronouns: Shan Ram
- ⚡ Fun fact: I Love Tech and Tech Love Me
Email Me 👉 ✉️ shanram7752@gmail.com For Freelancing/Project or Anything Else. 😊😊
I'm an ambitious and growth-oriented professional eager to apply knowledge, develop expertise, and contribute to organizational success. Known for a strong work ethic, adaptability, and the ability to learn quickly in fast-paced environments.
| 🌍 Geospatial Data Analysis | Turning raw spatial/tabular data into maps, choropleths, and constituency/region-level insight |
| 🗺️ GIS & WebGIS | Building interactive Leaflet-based mapping tools — routing, scoring, dashboards |
| 📊 Data Analysis & Visualization | EDA, statistical analysis, and storytelling with Python, Pandas, and Seaborn |
| 🤖 Machine Learning | Classification, regression, and clustering with scikit-learn |
| 📈 Business Intelligence & Reporting | KNIME + Power BI dashboards, KPI reporting, executive-style overviews |
| 💼 Business & Administrative Support | Sales reporting, documentation, presentations, and design work from my internship |
| 🤝 Professional Communication | Coordinating across a small team; client- and stakeholder-facing reporting |
Geospatial & Analytics
Technical
Business & Administrative Support
Communication
West Bengal 2026 Election — Results & Swing Analytics
Geospatial analysis of the 2026 West Bengal Assembly Election — party performance, winning margins, and constituency-level 2021→2026 vote-swing mapping using choropleths.
Python GeoPandas/Folium Pandas Choropleth Mapping
Key Focus: Turns raw, tabular election results into spatial insight by joining results to constituency boundaries and visualizing shifts between two elections.
Meghalaya Smart Tourism Platform (STIS)
Interactive WebGIS Smart Tourist Information System for Meghalaya — routing, offbeat-site scoring, nearby-facility discovery, and a tourism dashboard.
JavaScript Leaflet.js WebGIS Geospatial
Key Focus: Applies GIS/geospatial scoring logic to real-world tourism planning, built for my MSc dissertation on Meghalaya's geotourism potential.
Telecom Customer Churn Analysis & Prediction
End-to-end Data Analytics + Machine Learning + Business Intelligence project on the IBM/Kaggle Telco Churn dataset — built with KNIME (cleaning, feature engineering, modelling) and Power BI (executive dashboard), verified in Python.
KNIME Power BI Python Logistic Regression / Decision Tree / Random Forest
Key Focus: Rebuilds and audits an earlier churn workflow, then turns the corrected model into a business-facing dashboard with KPIs and retention recommendations.
Retail Customer Segmentation (K-Means)
Segments 200 retail customers via K-Means clustering on income and spending score, with cluster profiles translated into marketing recommendations.
Python scikit-learn Unsupervised Learning
Key Focus: Converts an unsupervised ML output into plain-language customer segments a marketing team could actually act on.
Uber Fare Prediction — Polynomial Regression
Engineers Haversine distance and time-based features from 200k Uber rides, then fits polynomial regression to predict fare amount.
Python scikit-learn Geospatial Feature Engineering
Titanic Survival Prediction
Binary classification on the Kaggle Titanic dataset comparing Logistic Regression, Decision Tree, and Random Forest models.
Python scikit-learn Classification
A few more EDA/ML notebooks — Netflix content analytics, Zomato Kolkata restaurants, crop-yield regression, diabetes prediction, and a Seaborn visualization reference — are in the full repository list.
Transforming geospatial and data-driven insights into practical business solutions. Interested in GIS, data analysis, visualization, operations, logistics, and business processes, with a strong focus on problem-solving, communication, coordination, and continuous learning.