🚀 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 Imputation: Missing values in critical columns were handled using SimpleImputer. 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.