A personal collection of machine learning, geospatial analysis, and data visualization projects developed using Google Colaboratory. These notebooks showcase techniques in EDA, spatial mapping, classification models, and urban form studies with popular open datasets.
- 🧠 Projects Overview
- 🚀 Getting Started
- 🛠️ Tools & Libraries
- 📈 Visual Output Samples
- 📌 Highlights & Results
- 🤝 Contributions
- 📄 License
- 📫 Contact
| Notebook | Description |
|---|---|
| EDA.ipynb | Exploratory Data Analysis with key statistical summaries, feature distributions, and visual correlation insights. |
| ESDA_Choropleth_Map.ipynb | Spatial analysis with a Choropleth map to highlight regional/geographical patterns. |
| Gapminder_data.ipynb | Temporal country-wise data visualization using Altair with interactive trends from the Gapminder dataset. |
| Mobile_Payment_Fraud_Detection_Project.ipynb | Machine learning classification pipeline for detecting fraudulent mobile transactions. |
| Titanic_Over_80_Accuracy_for_most_Models.ipynb | Classic Kaggle Titanic dataset analyzed with multiple models reaching >86% accuracy. |
| Urban_Form_Short_1.ipynb | Geospatial urban form analysis using momepy and geopandas for morphology-based metrics. |
All notebooks are compatible with Google Colaboratory — no installation required.
To run any notebook:
- Open the desired
.ipynbfile on GitHub. - Click the "Open in Colab" badge at the top (or upload to Colab).
- Follow inline
pip installinstructions if packages are missing.
Alternatively, to run locally:
git clone https://github.com/neetmadann/Portfolio-GeoSpatial-and-Machine-Learning.git
cd Portfolio-GeoSpatial-and-Machine-Learning
pip install -r requirements.txt # optional, if available