🪐 Planetary Insight Engine

An advanced Streamlit web app for predicting planetary material composition using both statistical and machine learning models.

🚀 Features

• 🪐 Planet Material Predictor
  Visually engaging interface with animated graphics and planetary theme.

• 📈 Data Analysis Module
  Exploratory Data Analysis (EDA), visualization, and insights driven by user-uploaded datasets.

• 📤 Smart File Upload
  Intelligent upload system with feedback if no file is uploaded.

• 🧠 Hybrid Modeling Approach
  Combines both statistical models (e.g., SARIMA) and machine learning models (e.g., Random Forest) for material prediction.

🧩 Modular Architecture

The project is divided into reusable components:

• app_sidebar.py – Sidebar navigation
• upload_page.py – Upload and parse datasets
• data_analysis.py – Handles exploratory data analysis
• mars_weather.py – Displays current Mars weather stats
• db_utils.py – Handles any backend or database logic
• style.css – Custom styles for visual appeal

🔍 Target Use Case

• Planetary science and astronomy students or researchers.
• Educational demonstrations for ML + space applications.
• Internal tool for data teams in space research startups.

🚧 Future Improvements

• Add model training from UI.
• Include database support for storing user sessions.
• Integrate NASA APIs for real-time planetary data.
• Enable deployment on cloud. (e.g., Streamlit Community Cloud, Azure, or AWS)