🚀 Nearest Earth Objects (NEOs) Analysis 🌌

This project focuses on analyzing Near-Earth Objects (NEOs) using machine learning techniques to predict their potential hazard levels. The project involves data exploration, preprocessing, and modeling using a Random Forest classifier.

📁 Project Structure

• data/: Contains the dataset of Near-Earth Objects.
• notebooks/: Jupyter notebooks detailing each step of the analysis.
• scripts/: Python scripts used for data preprocessing, modeling, and visualization.
• results/: Output results, including model performance metrics and visualizations.

🔍 Data Exploration

Explored key attributes of NEOs, such as:

• Absolute Magnitude
• Estimated Diameter (Min & Max)
• Relative Velocity
• Miss Distance

🔧 Data Preprocessing

1. Imputation: Missing values in critical columns were handled using SimpleImputer.
2. Scaling: Features were normalized to enhance model performance and stability.

🌳 Modeling

• Model: A Random Forest classifier was implemented to predict the potential hazard levels of NEOs.
• Evaluation: Model performance was assessed using confusion matrices and visualized using seaborn.

🛠️ Tools Used

• Python 🐍
• Pandas 📊
• Seaborn 📉
• Scikit-learn 🔍

📊 Visualizations

• Pair plots for understanding relationships between features.
• Confusion matrix to evaluate the performance of the Random Forest classifier.

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/NASA-Nearest-Earth-Objects-Analysis.git