IBM Applied Data Science Capstone: SpaceX Predictive Analysis

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Project Overview

This repository contains my capstone project for the IBM Applied Data Science professional certificate. It demonstrates the complete, end-to-end data science methodology applied to a real-world business scenario.

Objective: Assuming the role of a Data Scientist for a competing aerospace startup, the primary goal of this project is to predict whether the SpaceX Falcon 9 first stage will land successfully. This predictive model helps estimate the cost of a launch, providing crucial competitive intelligence.

Methodology & Project Structure

The project follows a standard data science pipeline. Below is the breakdown of the repository files aligned with each phase of the project:

Phase	Associated Files	Description
Data Collection	jupyter-labs-spacex-data-collection-api.ipynb jupyter-labs-webscraping.ipynb	Extracting historical launch data using the official SpaceX API and web scraping techniques (BeautifulSoup).
Data Wrangling	labs-jupyter-spacex-Data wrangling.ipynb	Cleaning, formatting, and preparing the raw datasets (.csv files) for analysis.
EDA & SQL	eda-dataviz.ipynb jupyter-labs-eda-sql-coursera.ipynb	Performing Exploratory Data Analysis (EDA) using Pandas and writing SQL queries to extract key insights.
Interactive Visualization	lab_jupyter_launch_site_location.ipynb spacex_dash_app.py	Building interactive maps (Folium) to analyze launch sites and creating a dynamic dashboard using Plotly Dash.
Machine Learning	SpaceX_Machine Learning Prediction_Part_5.ipynb	Training and evaluating classification models (Logistic Regression, SVM, Decision Trees, KNN) to predict landing success.
Executive Summary	IBM-DataScience-SpaceX-Capstone-Spacex-ppt.pdf	The final presentation detailing methodology, results, and actionable conclusions for stakeholders.

Datasets

The csv files included in this repository (Spacex.csv, dataset_part_1.csv, spacex_web_scraped.csv, etc.) represent the data at various stages of cleaning and processing throughout the pipeline.

Technologies Used

• Languages: Python, SQL
• Libraries: Pandas, NumPy, Scikit-Learn, BeautifulSoup
• Visualization: Matplotlib, Seaborn, Folium, Plotly Dash
• Environments: Jupyter Notebook