https://github.com/siddhantborse/atmosviz

Atmos Viz is a Python-based project designed to analyze, visualize, and predict global temperature trends across various cities and countries using time-series analysis and advanced data science techniques. Leveraging historical climate data, this project integrates machine learning models, geospatial mapping, and interactive visualizations to unco
https://github.com/siddhantborse/atmosviz

geopandas geospatial-analysis gis matplotlib numpy pandas plotly python scikit-learn seaborn shapefiles time timeseries-analysis timeseries-data

Last synced: 1 day ago
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Atmos Viz is a Python-based project designed to analyze, visualize, and predict global temperature trends across various cities and countries using time-series analysis and advanced data science techniques. Leveraging historical climate data, this project integrates machine learning models, geospatial mapping, and interactive visualizations to unco

• Host: GitHub
• URL: https://github.com/siddhantborse/atmosviz
• Owner: siddhantborse
• Created: 2024-12-18T22:03:13.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-18T22:23:33.000Z (about 2 months ago)
• Last Synced: 2025-02-12T19:08:38.640Z (1 day ago)
• Topics: geopandas, geospatial-analysis, gis, matplotlib, numpy, pandas, plotly, python, scikit-learn, seaborn, shapefiles, time, timeseries-analysis, timeseries-data
• Language: HTML
• Homepage:
• Size: 12.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🌍 **Atmos Viz: Global Temperature Analysis and Prediction**

### **Overview**

**Atmos Viz** is a Python-based project designed to analyze, visualize, and predict global temperature trends across various cities and countries. Leveraging historical climate data, this project combines **machine learning models**, **geospatial mapping**, and **interactive visualizations** to uncover meaningful insights into temperature variations over time.

---

### **Project Features**

- **Data Preprocessing**: Efficiently handles large-scale datasets, cleans missing values, and extracts features like **Year**, **Month**, and cyclic seasonal trends.

- **Geospatial Mapping**: Visualizes temperature data geographically using **shapefiles** and tools like **GeoPandas** and **Plotly**.

- **Machine Learning Prediction**: Forecasts future temperature trends using **Linear Regression** and **Random Forest** models.

- **Interactive Visualizations**: Generates engaging charts, heatmaps, and world maps for better analysis and understanding.

- **Performance Evaluation**: Measures model accuracy using metrics like **MAE**, **RMSE**, and **R²**.

---

### **Technologies Used**

- **Programming Language**: Python

- **Libraries**:

  - Data Handling: `Pandas`, `NumPy`

  - Visualization: `Matplotlib`, `Seaborn`, `Plotly`

  - Geospatial Mapping: `GeoPandas`

  - Machine Learning: `Scikit-learn`

  - GIS Tools: **Shapefiles**

---

### **Project Workflow**

1. **Data Preprocessing**

   - Load the temperature dataset (sourced from Kaggle, originally from Berkeley Earth).

   - Clean and normalize missing temperature values using **monthly means**.

   - Extract cyclic seasonal trends using sine and cosine transformations.

2. **Model Development**

   - Use **Linear Regression** and **Random Forest** for predictive modeling.

   - Train models using features like **Year**, **sin_month**, and **cos_month**.

   - Evaluate performance using **MAE**, **RMSE**, and **R²** metrics.

3. **Visualization**

   - **Geospatial Maps**: Display temperature data using **GeoPandas** and **Plotly**.

   - **Time-Series Trends**: Plot temperature trends with trendlines for specific cities.

   - **Interactive Maps**: Visualize data with **hover information** for cities on the map.

   - **Heatmaps & Boxplots**: Analyze monthly and regional temperature variations.

   

## Contributions

### Contributions are welcome! If you'd like to improve this project, please fork the repository and create a pull request.

## Contact

### For any queries or suggestions, feel free to contact me:

## Email: [email protected]

## VPortfolio: Siddhant Borse

## ⭐ If you like this project, star it on GitHub! ⭐

---

### **Project Directory Structure**

```plaintext

Atmos-Viz/

│

├── Package/

│   ├── data_preprocessing.py      # Data loading, cleaning, and feature extraction

│   ├── models.py                  # Machine learning model training

│   ├── evaluation.py              # Model evaluation metrics

│   ├── viz.py                     # Visualization functions (maps, charts, heatmaps)

│

├── shapefiles/                    # GIS shapefiles for mapping

│

├── data/                          # Raw and preprocessed temperature datasets

│

├── main.py                        # Main script for model execution and visualization

│

├── requirements.txt               # List of dependencies

│

└── README.md                      # Project documentation