🌘 Eclipse Prediction Data Preprocessing and EDA

This repository contains a Jupyter notebook focused on preprocessing and exploratory data analysis (EDA) of a solar eclipse dataset, which includes features like path width, central duration, Eclipse Time, Delta T (s), Lunation Number, Saros Number, Eclipse Type, Gamma, Eclipse Magnitude and datetime. The goal is to prepare clean and standardized data for use in an eclipse prediction model.

📂 Contents

• Research_01.ipynb — Main notebook for data preprocessing and EDA.
• Handling of missing values in key features.
• Manual standardization of datetime columns.
• Visual and statistical exploration of numeric eclipse data and feature-based storytelling
• the final solar_final.csv file for further improvements.

🔧 Features Processed

• Path Width (km)
• Central Duration
• Datetime (standardized manually due to exceptions)
• Eclipse types (encoded)
• Eclipse time (standerdized amnually) and more.

📊 Techniques Used

• Missing value imputation
• Datetime parsing and formatting
• Frequency distribution plots
• Descriptive statistics

🚀 Getting Started

Requirements

• Python 3.8+
• Jupyter Notebook
• pandas, matplotlib, seaborn, numpy, folium & plotly

You can install the requirements using:

pip install pandas matplotlib seaborn numpy folium plotly

or

pip install -r requirements.txt