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Course 10 - IBM Capstone Project

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CAP 1 - Data Collection Using Spacex API.ipynb
CAP 1 - Data Collection Using Spacex API.ipynb
 
 
CAP 2 - Data Collection Using Webscraping.ipynb
CAP 2 - Data Collection Using Webscraping.ipynb
 
 
CAP 3 - Data Wrangling.ipynb
CAP 3 - Data Wrangling.ipynb
 
 
CAP 4 - EDA using SQL.ipynb
CAP 4 - EDA using SQL.ipynb
 
 
CAP 5 - EDA using Matplotlib & Seaborn.ipynb
CAP 5 - EDA using Matplotlib & Seaborn.ipynb
 
 
CAP 6 - Data Visualization using Folium.ipynb
CAP 6 - Data Visualization using Folium.ipynb
 
 
CAP 7 - Machine Learning Models.ipynb
CAP 7 - Machine Learning Models.ipynb
 
 
README.md
README.md
 
 
applied-ds-capstone-project-presentation.pdf
applied-ds-capstone-project-presentation.pdf
 
 
spacex_dash_app.py
spacex_dash_app.py
 
 
spacex_launch_dash.csv
spacex_launch_dash.csv
 
 
spacex_launch_geo.csv
spacex_launch_geo.csv
 
 
test.py
test.py
 
 

README.md

🚀 Applied Data Science Capstone

This Capstone Project is the final and 10th module of this course in IBM Data Science Professional Certificate specialization, and it actually summarizes all materials that have been learnt during this specialization.

📄 Project Background

SpaceX is the most successful commercial company of space age, making space travel affordable. The company advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars. At the heart of SpaceX’s spate of successes is the Falcon 9, which has brought down the cost of reaching space and become a springboard. While other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore, if we can determine if the first stage will land, we can determine the cost of a launch. Based on public information and machine learning models, we are going to predict if SpaceX will reuse the first stage.

📄 Questions to be answered

  • How do variables such as payload mass, launch site, number of flights, and orbits affect the success of the first stage landing?
  • Does the rate of successful landings increase over the years?
  • What is the best algorithm that can be used for binary classification in this case?

📄 Methodology

1. Data collection methodology

  • Using SpaceX Rest API
  • Using Web Scrapping from Wikipedia

2. Performed data wrangling

  • Filtering the data
  • Dealing with missing values
  • Tranforming the data and adding new columns
  • Using One Hot Encoding to prepare the data to a binary classification

3. Performed exploratory data analysis (EDA) using visualization and SQL

4. Performed interactive visual analytics using Folium and Plotly Dash

5. Performed predictive analysis using classification models

  • Selected required features, split the data, build models, tuned and performed evaluation to ensure an optimized model

6. Constructed a brief presentation report