Applied Data Science Capstone - SpaceX

This repository is the final project of the IBM Data Science course I took from the online education platform Coursera.
Thanks for all the support and encouragement for all Coursera teams!

Purpose of the repo: Real-time dashboard application using the data of SpaceX, a space transportation company, under the roof of the materials I acquired during the whole course.

You can review the pdf file that I have prepared with the application details here.

Valuable knowledge gained in the course:

• Data Science Methodology
• Python Structure and Fundamentals
• Data Analysis with Python
• Data Visualization with Python
• Machine Learning with Python
• Databases and SQL for Data Science
  

Additionally, I had the opportunity to use IBM Cloud Services throughout this course.

CONTENTS

1. Data Collection: In this file, data collection is performed by making a get request to the SpaceX API. It also includes some basic data editing and formatting.
2. Web Scraping: Web scrap Falcon 9 launch records with BeautifulSoup:
   • Extraction a Falcon 9 launch records HTML table from Wikipedia
   • Parsing the table and convert it into a Pandas data frame
3. Data Wrangling: Performing exploratory data analysis and determine training labels
   • Exploratory Data Analysis
   • Determining Training Labels
4. EDA with SQL: Using this Python notebook;
   • Understanding the Spacex DataSet
   • Loading the dataset into the corresponding table in a Db2 database
   • Executing SQL queries to answer assignment questions
5. EDA Data Visualization: Performing exploratory data analysis and feature engineering using pandas and matplotlib
   • Exploratory Data Analysis
   • Preparing Data Feature Engineering
6. Launch Site Location: This lab contains the following stages:
   • Marking all launch sites on a map
   • Marking the success/failed launches for each site on the map
   • Calculating the distances between a launch site to its proximities
7. Machine Learning Prediction: Performing exploratory data analysis and determine training labels;
   • Creating a column for the class
   • Standardization the data
   • Spliting into training data and test data
   • Finding best Hyperparameter for SVM, Classification Trees and Logistic Regression
     • To find the method performs best using test data
8. Dashboard - SpaceX: Real-time dashboard creation using all collected and processed datasets with Python libraries dash and plotly.