https://github.com/ahmed-naserelden/astro-success-analytics

This project analyzes key factors influencing success in the Space Race using data science techniques. It includes data collection, machine learning modeling, and insightful visualizations to predict mission outcomes.
https://github.com/ahmed-naserelden/astro-success-analytics

data dataanalysis python

Last synced: 28 days ago
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This project analyzes key factors influencing success in the Space Race using data science techniques. It includes data collection, machine learning modeling, and insightful visualizations to predict mission outcomes.

• Host: GitHub
• URL: https://github.com/ahmed-naserelden/astro-success-analytics
• Owner: Ahmed-Naserelden
• Created: 2023-10-24T04:15:36.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-16T21:49:34.000Z (4 months ago)
• Last Synced: 2024-09-18T03:28:47.140Z (4 months ago)
• Topics: data, dataanalysis, python
• Language: Jupyter Notebook
• Homepage:
• Size: 25.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Astro-Success-Analytics: Winning the Space Race

This repository contains the final project for the **Applied Data Science Capstone**, where I applied data science techniques to analyze and predict success factors in the **Space Race**. This project follows the complete data science workflow, from data acquisition to machine learning model building and evaluation.

## Project Overview

The **Space Race** was a 20th-century competition between two Cold War rivals, the Soviet Union and the United States, to achieve superior spaceflight capability. This project aims to explore key factors that contributed to the success of space missions and to predict the likelihood of success using machine learning techniques.

![Space Race Visualization](https://th.bing.com/th/id/R.a771c09aaea5a9bb102295426694316c?rik=2Zhe0dRuf3II3Q&pid=ImgRaw&r=0)  

### Key Steps:

1. **Data Collection**: Data was gathered from multiple sources, including historical records, space mission databases, and public datasets.

2. **Data Wrangling**: The raw data was cleaned and pre-processed to handle missing values, outliers, and formatting inconsistencies.

3. **Exploratory Data Analysis (EDA)**: Visualizations and statistical techniques were used to explore trends, patterns, and anomalies in the dataset.

4. **Modeling**: Various machine learning algorithms, such as logistic regression, decision trees, and random forests, were applied to predict mission success.

5. **Evaluation**: Model performance was evaluated using metrics like accuracy, precision, recall, and F1-score to determine the best-performing model.

## Key Technologies and Libraries

- **Python**: Core programming language for data manipulation and modeling.

- **Jupyter Notebooks**: For documenting and visualizing the analysis process.

- **Pandas, NumPy**: Used for data manipulation, cleaning, and transformation.

- **Matplotlib, Seaborn**: For data visualization and generating insightful plots.

- **Scikit-learn**: For building and evaluating machine learning models.

## How to Run

1. Clone the repository:

   ```bash

   git clone https://github.com/Ahmed-Naserelden/Applied-Data-Science-Capstone.git

  

2. Navigate to the project directory:

    ```bash

    cd Astro-Success-Analytics/

## Conclusion

This project showcases how data science techniques can be applied to historical data to predict outcomes in complex real-world scenarios like the Space Race. The insights gained can be used to understand what factors contributed most to mission success, providing a better understanding of space exploration challenges.