A data science project that analyzes historical climate data to identify:

• Temperature trends
• Rainfall variations
• Anomalies
• Future climate patterns using machine learning

To simulate and analyze climate data and build a complete analytics pipeline including:

• Data preprocessing
• Trend analysis
• Anomaly detection
• Forecasting
• Interactive dashboard

• Programming Language: Python

• Libraries Used:

  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
  • Streamlit

• Climate data simulation (7000+ records)
• Yearly temperature trend analysis
• Rainfall variation analysis
• Anomaly detection
• Future temperature forecasting (ML model)
• Interactive Streamlit dashboard

Climate-Trend-Analyzer/
│
├── src/
├── data/
├── app/
├── images/
├── outputs/
│
├── main.py
└── README.md

pip install -r requirements.txt

python src/data_generator.py

python main.py

streamlit run app/dashboard.py

• Temperature trend visualization
• Rainfall analysis graphs
• Anomaly detection plot
• Forecasted climate values

• Climate shows seasonal variation patterns
• Anomalies detected in extreme values
• Forecast predicts future temperature trends

• Real-world NASA/IMD dataset integration
• Advanced ML models (LSTM, ARIMA)
• Geo-based climate analysis
• Cloud deployment

Student Project – Data Science Portfolio