Welcome to the grand culmination of the IBM Data Science Professional Certificate series - the Applied Data Science Capstone. This pivotal 10th course represents not just the final challenge but a comprehensive synthesis of the vast array of knowledge and skills acquired throughout this esteemed specialization. Participants will engage in a project that encapsulates the entire learning journey, applying theoretical insights to real-world data science challenges.
At the forefront of the commercial space era, SpaceX has redefined the economics of space travel, making it more accessible and cost-effective than ever before. The Falcon 9 rocket, a marvel of engineering and cost efficiency, embodies this revolution. Advertised on SpaceX's website for $62 million, it starkly contrasts with the offerings of other providers, whose launch costs soar above $165 million. A significant portion of these savings stems from SpaceX's innovative reuse of the Falcon 9's first stage. Our project aims to leverage publicly available data and sophisticated machine learning algorithms to predict the likelihood of the first stage's reuse, thus estimating the overall cost of a launch.
This project is driven by a series of probing questions designed to unravel the complexities of space launch economics and the technicalities of reusable rocket technology:
- How do critical variables such as payload mass, launch site, flight frequency, and orbital trajectories influence the success rate of the Falcon 9's first stage landing?
- Is there a discernible trend in the success rate of landings over time, indicating improvements in technology or operational methodologies?
- Among the plethora of binary classification algorithms, which one emerges as the most effective in predicting the reuse of the rocket's first stage?
1. Data Acquisition Strategy
- Harnessing the power of the SpaceX Rest API for real-time launch data.
- Employing web scraping techniques to extract historical launch data from Wikipedia.
2. Data Wrangling Endeavors
- Employing rigorous data filtering techniques to refine the dataset.
- Addressing and rectifying issues related to missing data values.
- Implementing One Hot Encoding strategies to transform categorical data into a format suitable for binary classification analysis.
3. Exploratory Data Analysis (EDA) Tactics
- Utilizing advanced visualization tools and SQL queries to uncover patterns and insights from the data.
4. Interactive Visual Analytics
- Leveraging Folium for geospatial data representation and Plotly Dash for creating dynamic, interactive data visualizations.
5. Predictive Analytical Modeling
- Constructing and fine-tuning classification models to predict the likelihood of the first stage's reuse with optimal accuracy. This process involves meticulous model selection, parameter tuning, and performance evaluation to ensure the highest predictive efficacy.
This capstone project not only signifies the culmination of the IBM Data Science Professional Certificate but also serves as a testament to the practical application of data science in solving complex, real-world problems. Through this endeavor, participants will showcase their ability to navigate the full spectrum of data science methodologies, from data collection and preprocessing to predictive modeling and insight generation.
Email: dilshad.geologist@gmail.com Dilshad Raza