SeaRiseIQ is a hybrid data science framework that combines classical time series forecasting with quantum-inspired classification to analyze and predict global mean sea level (GMSL) trends.

Using NASA's satellite-based GMSL dataset (with and without Glacial Isostatic Adjustment), this project:

• Forecasts future sea levels using ARIMA and Linear Regression
• Classifies rising vs. non-rising trends using Logistic Regression, SVM, and a simulated Quantum SVM (QSVM)
• Compares model performance using statistical metrics and visual diagnostics

• 📊 Time series forecasting using ARIMA
• 🔁 Regression-based prediction with Lag Features
• 🧠 Trend classification using Logistic Regression, SVM, and Quantum SVM
• 🧪 Exploratory data analysis and visual insights
• 📦 Clean, reproducible pipeline in Python (Jupyter Notebook)
• 📉 Performance evaluation: MSE, MAE, R², F1-Score, Confusion Matrix
• 🔍 Comparison of GMSL with vs. without GIA correction

• Python 3.x
• Pandas, NumPy, Matplotlib, Seaborn
• scikit-learn
• statsmodels
• Qiskit Machine Learning (simulated QSVM)

• Source: NASA PO.DAAC
• File: GMSL_TPJAOS_5.2.txt converted to CSV using custom Python script
• Columns include:
  • GMSL_GIA, GMSL_noGIA, year_fraction, etc.

• Sea level rise plots (with and without GIA)
• Difference analysis (~3.91 mm avg)
• ARIMA forecast with 95% confidence intervals
• Residual plots, ACF/PACF analysis
• Trend classification plots and model comparisons

• Integration with live dashboards using Streamlit or Dash
• Extension to multi-class trend labeling
• Deploying real quantum models (e.g., IBM Q backend)
• Using LSTM or hybrid models for deeper learning