🚀 IBM Data Science Capstone Project – SpaceX Falcon 9 Landing Prediction

📌 Project Overview

This repository contains my IBM Data Science Capstone Project, the final course of the IBM Data Science Professional Certificate and Applied Data Science with Python Specialization on Coursera.

In this project, I assume the role of a Data Scientist working for a startup aiming to compete with SpaceX. The goal is to predict whether the first stage of the SpaceX Falcon 9 rocket will land successfully, helping the company make data-driven bidding decisions for rocket launches.

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🎯 Objectives

• Apply the complete data science methodology on a real-world dataset

• Perform:

  • Data collection (API & web scraping)
  • Data wrangling & cleaning
  • Exploratory Data Analysis (EDA)
  • Data visualization & dashboards
  • Predictive modeling (classification)
  • Model evaluation & comparison

• Present actionable insights to stakeholders

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🧠 What You’ll Learn

• Demonstrate end-to-end data science and machine learning proficiency
• Work with real-world aerospace datasets
• Build and evaluate machine learning models
• Communicate findings effectively through reports and dashboards

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🧰 Skills & Tools Used

🔹 Programming & Libraries

• Python
• Pandas
• NumPy
• Scikit-learn
• Plotly
• Matplotlib
• Seaborn

🔹 Data Science Skills

• Data Collection (API & Web Scraping)
• Data Wrangling
• Exploratory Data Analysis (EDA)
• Data Visualization
• Predictive Modeling
• Model Evaluation
• Statistical Modeling
• Data-Driven Decision Making

🔹 Machine Learning Models

• Logistic Regression
• Support Vector Machines (SVM)
• Decision Tree Classifier
• K-Nearest Neighbors (KNN)

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📂 Project Structure

IBM-Data-Science-Capstone/
│
├── data/
│   ├── raw/
│   └── processed/
│
├── notebooks/
│   ├── 01-data-collection.ipynb
│   ├── 02-data-wrangling.ipynb
│   ├── 03-eda.ipynb
│   ├── 04-visual-analytics.ipynb
│   ├── 05-predictive-analysis.ipynb
│
├── dashboard/
│   └── spacex_dashboard.py
│
├── reports/
│   └── final_presentation.pdf
│
├── README.md
└── requirements.txt

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📊 Course Modules

✅ Module 1: Introduction

• Business understanding
• Problem formulation

✅ Module 2: Exploratory Data Analysis (EDA)

• Data visualization
• Statistical insights

✅ Module 3: Interactive Visual Analytics & Dashboard

• Interactive dashboards using Plotly

✅ Module 4: Predictive Analysis (Classification)

• Model training & evaluation
• Performance comparison

✅ Module 5: Present Your Data-Driven Insights

• Final report & stakeholder presentation

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📈 Key Outcome

✔ Successfully predicted Falcon 9 first-stage landing success ✔ Identified optimal machine learning model ✔ Provided actionable insights to support competitive bidding

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🏆 Certification

• 🎓 IBM Data Science Professional Certificate
• 📜 Coursera Shareable Certificate

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🔗 Useful Links

• 🔗 GitHub Profile: https://github.com/cool0009
• 🔗 IBM Data Science Certificate: https://www.coursera.org/professional-certificates/ibm-data-science
• 🔗 Coursera: https://www.coursera.org
• 🔗 SpaceX Official Site: https://www.spacex.com

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👤 Author

Kamal Raj Vendi 🎓 Data Science Graduate 📍 GitHub: cool0009

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⭐ Acknowledgements

• IBM & Coursera instructional team
• Open-source Python community
• SpaceX public datasets