CAPSTONE-PROJECT-SPACE-X

Project Overview

This repository contains my capstone project for:

• The IBM Data Science Professional Certificate (Final Course)
• The Applied Data Science with Python Specialization

In this real-world simulation, I assumed the role of a Data Scientist at a startup competing with SpaceX, following the complete Data Science lifecycle from data collection to model deployment.

Project Objectives

• ✅ Predict SpaceX Falcon 9 first-stage landing success
• 🚀 Develop competitive bidding insights for rocket launches
• 📊 Showcase end-to-end Data Science skills:
  • Data collection & wrangling
  • Exploratory data analysis (EDA)
  • Data visualization
  • Machine learning modeling
  • Model evaluation & optimization
  • Stakeholder reporting

Key Features

• Real-world scenario: Mimics actual Data Science work at a space tech company
• Comprehensive methodology: Covers all stages of the Data Science process
• Practical application: Results directly applicable to launch cost predictions
• Portfolio-ready: Demonstrates competency in:
  • Python data science stack (Pandas, NumPy, Scikit-learn)
  • Statistical analysis
  • Machine learning (classification)
  • Data storytelling

Learning Context

This capstone serves as the culmination of my IBM/Python Specialization journey, focusing on applying previously learned concepts rather than introducing new material. The project demonstrates my ability to:

• Work with complex, real-world datasets
• Make data-driven decisions
• Communicate technical results effectively

Business Impact

The predictive models and insights developed here enable:

• More accurate cost projections for rocket launches
• Competitive bid strategies against SpaceX
• Risk assessment for first-stage reusability

Technologies Used

Languages & Libraries:

• Python 3
• Pandas (Data Wrangling)
• Scikit-learn (ML Modeling)
• Seaborn/Matplotlib (Visualization)
• SQL (Data Extraction)