applied data science capstone presentation spacex

Winning Space Race with Data Science

November 1, 2022

This is the presentation of the capstone project in the IBM Data Science Professional Certificate .

Note that this presentation is much more detailed and technical than regular high-level and abstracted presentations for executive teams.

I assume the role of a Data Scientist working for a startup intending to compete with SpaceX , and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting results to stakeholders.

In this capstone, we will predict if the Falcon 9 first stage will land successfully, SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; 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. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

Executive Summary

Introduction

Methodology

Data collection API notebook

Web scraping notebook

Data wrangling notebook

EDA with Visualization notebook

EDA with SQL notebook

Launch Sites Locations Analysis with Folium notebook

Interactive Dashboard with Ploty Dash

Machine Learning Prediction notebook

Insights Drawn from EDA

Launch Sites Proximities Analysis

Build a Dashboard with Plotly Dash

Predictive Analysis (Classification)

Conclusions

For notebooks, datasets and scripts, follow this GitHub repository link: Applied Data Science Capstone

IBM Data Science Capstone Project Space X

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Created on February 15, 2022

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Winning Space Race with Data science

Executive Summary

Methodology

Introduction

executive summary

• Exploratory Data Analysis results
• Interactive analytics in screenshots
• Predictive Analytics results

Summary of all results

• Data Collection through API
• Data Collection with Web Scraping
• Data Wrangling
• Exploratory Data Analysis with SQL
• Exploratory Data Analysis with Data Visualization
• Interactive Visual Analytics with Folium
• Machine Learning Prediction

Summary of methodologies

introduction

• What factors determine if rockets will land successfully?
• Which features are the most correlatedto determine the success rate of a successful landing.
• What conditions does SpaceX have to achieve to get the best results and ensure the best rocket success landing rate.

Problems you want to find answers

SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; 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. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch. In this capstone, we will predict if the Falcon 9 first stage will land successfully using the machine learning pipeline created.

Project background and context

• SpaceX Rest API.
• Web Scraping from Wikipedia.
• One Hot Encoding applied on categorical features (Transforming data for Machine Learning)
• Scatter and bar graphs to show patterns between data.
• How to build, tune and evaluate classification models.

Create dataframe from dictionnary

Apply list to dictionnary

Filter columns then export csv

Clean data, check and fill missing values

Convert into dataframe using .json_normalize()

Decode the response content as a Json using .json()

Request to get SpaceX API

Data collection is the process of gathering data to provide the information that's needed to answer questions, analyze business performance or other outcomes, and predict future trends or actions to take.

Data collection - SpaceX api

Create a dictionnary

Extract column name one by one

Convert dictionnary to dataframe then export csv

Appending data to keys

Find all tables

Create a BeautifulSoup object

Request the Falcon9 Launch HTML page

Data collection - web scraping

Export dataframe to csv

Create a landing outcome label from Outcome column

Calculate the number and occurence of mission outcome per orbit type

Calculate the number and occurrence of each orbit

We perform Exploratory Data Analysis (EDA) and determine Training LabelsWe convert those outcomes into Training Labels with :- 1 means the booster successfully landed - 0 means it was unsuccessful

Calculate the number of launches on each site

Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis.

Data wrangling

Line graphs work well in showing trends chronologically.Moreover, we can visualize data changes at a glance.

Most audiences understand how to read a bar graph and can grasp the information.From this graph, we can easily interpret which orbit have the highest sucess rate

We use a scatter plot to determine whether or not two variables have a relationship or correlation.

Launch Success Yearly Trend

• Success rate and Orbit type
• FlightNumber and PayloadMass
• Flight Number and Launch Site
• Payload and Launch Site
• FlightNumber and Orbit type
• Payload and Orbit type

Scatter Graphs

Exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics by using simple tools from statistics, simple plotting tools.

EDA with Data Visualization

EDA with SQL (Structured Query Language)

We were able to load SpaceX dataset into the corresponding table in a Db2 database directly on Jupyter notebook. We performed EDA with SQL queries to gather information from the data :

• Display the names of the unique launch sites in the space mission
• Display 5 records where launch sites begin with the string 'CCA'
• Display the total payload mass carried by boosters launched by NASA (CRS)
• Display average payload mass carried by booster version F9 v1.1
• List the date when the first successful landing outcome in ground pad was acheived.
• List the names of the boosters which have success in drone ship and have payload mass greater than 4000 but less than 6000
• List the total number of successful and failure mission outcomes
• List the names of the booster_versions which have carried the maximum payload mass. Use a subquery
• List the failed landing_outcomes in drone ship, their booster versions, and launch site names for in year 2015
• Rank the count of landing outcomes (such as Failure (drone ship) or Success (ground pad)) between the date 2010-06-04 and 2017-03-20, in descending order

It is the standard language to interact with databases. SQL is the most important tool, a data analyst uses to manipulate and gain insights from the data.

Folium makes it easy to visualize data that has been manipulated in Python on an interactive leaflet map which makes it an excellent tool for plotting maps.

Build an Interactive Map with Folium

• We marked all launch sites on a map and added map objects such as :
• folium.Marker()
• folium.Circle()
• We assigned the feature "launch_outcome" to easily visualize marker colors on the map based on the class value :
• 1 (Success) = Green
• 0 (Failure) = Red
• We marked the success/failed launches for each site on the map with folium.Icon() :
• The color-labeled marker clusters, able us to easily identify which launch sites have relatively high success rates.
• We calculated the distances between a launch site to its proximities thanks to MousePosition to get coordinate for a mouse over a point on the map.
• Then we answered some questions :
• Are launch sites in close proximity to railways, highways and coastline ?
• Do launch sites keep certain distance away from cities ?

- Add a Scatter Plot to show the relationship with Outcome and Payload Mass for the different Booster Version.- Scatter plot is used to determine whether or not two variables have a relationship or correlation.

Scatter Plot :

- Add a Range Slider to Select Payload Mass (Kg) in order to find if variable is correlated to mission outcome.

Range Slider :

- Add a Launch Site Dropdown Input Component to show the total launches by all sites or a certain site.- Generally used to display numeric values, Pie chart is easy to understand thanks to its different portions and color codings.

Pie Chart :

Dash is a python framework created by plotly for creating interactive web applications.

Build a Dashboard with Plotly Dash

• Select the model with the best accuracy score

Predictive analytics is the use of various statistical and machine learning algorithms to predict the likelihood of future outcomes based on historical data. The goal is to suggest a course of action or strategy to make decisions from immediate to long term to provide the best assessment of what will happen in the future.

Predictive Analysis (Classification)

• Use the accuracy metric for our model
• Improve our model by using feature engineering and algorithm tuning to find the best hyperparameters for each type of algorithms
• Plot a Confusion Matrix

2. Evaluating Model

3. Find the best performing Classification Model

• Load our dataset using NumPy and Pandas
• Transform our data
• Split our data into train and test set
• Check how many test samples have been created
• Select the different machine algorithms to be trained
• Set hyperparameters and algorithms to the object GridSearchCV
• Fit the the data into the GridSearchCV to find the best parameters
• Train our dataset

1. Building Models

• Exploratory data analysis results
• Interactive analytics demo in screenshots
• Predictive analysis results

Flight Number vs. Launch Site

Payload vs. Launch Site

Success Rate vs. Orbit Type

Flight Number vs. Orbit Type

Payload vs. Orbit Type

EDA WITH visualization

As we can see from this scatter plot, the more the number of flights increases, the more the success rate increases

For example with CCAFS SLC 40 LaunchSite, the greater the playload mass, the greater the success rate for the rocket to land.At this stage, we still cannot determine if there is a correlation between these two variables.

From the barplot, we can see that 'ES-L1', 'GEO', 'HEO', 'SSO' had the most success rate.

We can clearly observe for 'LEO' Orbit that sucess is related to the number of flights.Unlike 'GTO' where there seems to be no correlation.

With heavy payloads, the successful landing are more for 'PO', 'ISS' and 'LEO' orbits.Unlike 'GTO' which have a negative impact.

From this line chart, we can observe that the success rate keep increasing since 2013 till 2020.

All Launch Site Names

Launch Site Names Begin with 'CCA'

Total Payload Mass

Average Payload Mass by F9 v1.1

First Successful Ground Landing Date

Successful Drone Ship Landing with Payload between 4000 and 6000

Total Number of Successful and Failure Mission Outcomes

Boosters Carried Maximum Payload

2015 Launch Records

• Rank Landing Outcomes Between 2010-06-04 and 2017-03-20

EDA WITH SQL

We used the keyword DISTINCT to show unique values in LaunchSite column from SpaceXTbl dataset

➜ Display first 5 records where column Launch_Site values must start with 'CCA'

SELECT * FROM = Query all (*) rows and columns from SpaceXTbl dataset WHERE ... LIKE ... = Condition which will only query rows from Launch_Siteusing pattern matching %LIMIT = Return first n_rows matching the SELECT criteria.

➜ Display the total payload mass carried by boosters launched by NASA (CRS)

SELECT SUM( ) = Return the total of the column payload_mass__kg_AS = rename the name of columnWHERE ... = ... = Condition which will only query rows from Customer columns with 'NASA (CRS)' values

➜ Display average payload mass carried by booster version F9 v1.1

SELECT AVG( ) = Return the average of the column payload_mass__kg_WHERE ... = ... = Condition which will only query rows from Booster_version column with 'F9 v1.1' values

➜ Display the date when the first successful landing outcome in ground pad was acheived.

SELECT MIN( ) = Return the average of the column payload_mass__kg_ WHERE ... = ... = Condition which will only query rows from Launding__outcome column with 'Success (ground pad)' values

➜ Display the names of the boosters which have success in drone ship and have payload mass greater than 4000 but less than 6000

SELECT = Query only data from booster_version column WHERE ... = ... = Condition which will only query rows from Launding__outcome column with 'Success (drone ship)' valuesAND = Requires that additional conditions are true

➜ Display the total number of successful and failure mission outcomes

SUM(CASE WHEN ... THEN ... ELSE ... END) = Case statement is used to get both success and failure instead of multiple COUNT()

➜ Display the names of the booster_versions which have carried the maximum payload mass.

Here, we used a subquery for :WHERE ... = (SELECT MAX( ) FROM ...) = return the maximum value of payload_mass__kg_ column

➜ Display the failed landing_outcomes in drone ship, their booster versions, and launch site names for in year 2015

MONTHNAME(...) = return the month from the column DateWHERE ...AND ... = requires all conditions to be true. Here, we only select Failure (drone ship) from 2015

All Launch Sites on folium map

Color labels for each site on the map

Launch Sites distances from railways / highways / cities / coastlines

LAUNCHSITES PROXIMITIES analysis

➜ Launch sites are close proximity to the coast for safety reasons

Green Markers = Successful LaunchesRed Markers = Failure Launches➜ KSC LC-39A launch site has the most probability of success

➜ Do launch sites keep certain distance away from cities?Launch sites are the farthest from cities and dense areas to protect the population from them.

➜ Are launch sites in close proximity to coastline ?Launch sites are close to coastline for multiple logical reasons : - As we saw on previous notebooks, the launch success rate may depend on many factors such as the location and proximities of a launch site, i.e., the initial position of rocket trajectories. - Do the lauches over the ocean to cancel any time in case of problems.- Prevent human and material repercussions in case of failure.

➜ Are launch sites in close proximity to highways ?Launch sites still close to highways for the same reasons for railways. But since the highways are also frequented by the population, they must keep a safe distance to avoid any injuries.

Closest_Coastline

Closest_City

Closest_Highlway

Closest_Railway

➜ Are launch sites in close proximity to railways ?Launch sites are nearest from railways in order to transport and receive more easily materials or cargos. But also, to minimize the distance for the employees : thus saving time, money and effort.

Success Count for all Launch Sites with pie chart

• Pie chart with highest success ratio
• Folium Map Screenshot 3

build a dashoboard with plotly dash

Has we saw on Folium Map part, KSL LC-391 had the most successeful launches from Launch Sites.

KSL LC-39A had 76.9% of success rate while getting 23.1% of failure rate

pie chart with highest success ratio

(with different Payload selected in Range slider) PART I

Payload range(Kg) between 0 to 10 000 Kg

Payload range(Kg) between 0 to 5600 Kg

Low weighted Payload Mass (Kg) have HIGHER success rate than Heavy Payload Mass (Kg)

scatter plot of Payload vs launch outcome for all sites

Booster Version Company with Highest success rate

Payload range(Kg) with highest success rate

(with different Payload selected in Range slider) PART II

Payload range(Kg) with lowest success rate

Payload range with lowest success rate is between 362 Kg and 475kg.Most payload mass with highest success rate is between 1952 Kg and 5300kg.FT is the Booster Version with highest launch success rate.

Classification Accuracy

• Confusion Matrix

predictive analysis (classification)

We trained 4 models different models.Decision Tree has the highest classification accuracy with 0.90 (while during test set, it got 0.83, the lowest score)

Same Confusion Matrix for KNN, Decision Tree and Logistic Regression

False Negative

Decision Tree got a higher result thanks to his TP of 3 against TP of 5 for the other models.On the other hand, it calculates more than FN (3 against 1).

Confusion matrix

• More the number of flights increases, more the success rate increases at a launch site.
• Orbits 'ES-L1', 'GEO', 'HEO', 'SSO' had the most success rate.
• Success rate keep increasing since 2013 till 2020.
• KSC LC-39A had the most successful launches from all sites.
• Low payloads mass (Kg) perform better than the heavier payloads.
• Most payload mass with highest success rate is between 1952 Kg and 5300kg.
• FT is the Booster Version with highest launch success rate.
• The Decision Tree classifier is the best machine learning algorithm for this project with provided dataset.

Conclusions

Interactive Plotly :https://plotly.com/python-api-reference/Dash Plotly :https://dash.plotly.comDashBoarding Tools :https://pyviz.org/dashboarding/

Applied DataScience Capstone Project

Hey Everyone

In the final Course of the IBM Professional Data Science Certificate I had the chance to do an Applied Data Science Capstone Project with all of the knowledge that I gathered throughout this wonderful course. Attached Below are the Powerpoint Presentation, Final Report and Notebook of the Project. Here is the link to my Github .

Capstone Project Final Full Report by Robert on Scribd

PowerPoint Presentation

Capston Project Final FULL PPT PDF by Robert on Scribd

U.S. News & World Report Education takes an unbiased approach to our recommendations. When you use our links to buy products, we may earn a commission but that in no way affects our editorial independence.

Applied Data Science Capstone

About this course.

This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets. In this course you will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders. You will be tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch. In this course, there will not be much new learning, instead you’ll focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your data science and machine learning portfolio to showcase to employers.

Add a Verified Certificate for $38 USD

What Else Should I Know?

Data Science

Statistics and Actuarial Science

Master of science in data science, earn your ms in data science.

The Master of Science program in data science requires 30 semester hours of graduate credit. It aims to train the next generation of data scientists with the analytical and technical skills to explore, formulate, and solve complex data-driven problems in science, industry, business, and government. The program focuses on the theory, methodology, application, and ethics for working with and learning from data. Students will acquire the abilities to develop and implement new or special purpose analysis and visualization tools, and a fundamental understanding of how to quantify uncertainty in data-driven decision-making. 

The coursework includes six core courses covering the fundamentals of data science including probability and statistics; data storage, access, and management; and data visualization, exploration, modeling, analysis, and uncertainty quantification. Students will acquire hands-on experience in solving real-world problems, communication skills and data ethics via a required capstone project. Students choose three electives (9 semester hours) from a wide variety of courses on specialized data science topics offered by statistics, biostatistics, computer science, and business analytics to enhance their skill set, based on their interests and career goals.

Requirements and program planning

Academic requirements.

Students who apply to our MS program should have two semesters of calculus or two semesters of engineering calculus.

The MS with a major in data science requires the following coursework.

Sample schedule for MS students in data science

Year 1 fall semester.

• STAT:3120 Probability and Statistics
• STAT:3200 Applied Linear Regression
• STAT:4540 Statistical/Machine Learning
• STAT:5400 Statistical Computing

Year 1 spring semester

• DATA:4750 Probabilistic Statistical Learning
• STAT:4580  Data Visualization and Data Technologies
• Two electives (or one, if two are taken in year 2 fall)

Year 2 fall semester

• DATA:5890 MS Data Science Practicum*
• One elective (or two)

*Students may substitute DATA:5890 by an appropriate internship/summer work experience, with pre-approval by the course instructor.

Probability and Statistics (STAT:3120, 4 semester hours)

Basic concepts of probability, statistical models, discrete and continuous random variables and their distributions, expectations, conditional expectations, estimation of parameters, testing statistical hypotheses.

Applied Linear Regression (STAT:3200, 3 semester hours)

Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA; hands-on data analysis with computer software.

Statistical Learning (STAT:4540, 3 semester hours)

Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering; methods will be applied to real data using appropriate software; supervised learning topics include linear and nonlinear (e.g., logistic) regression, linear discriminant analysis, cross-validation, bootstrapping, model selection, and regularization methods (e.g., ridge and lasso); generalized additive and spline models, tree-based methods, random forests and boosting, and support-vector machines; unsupervised learning topics include principal components and clustering. Requirements: an introductory statistics course and a regression course. Recommendations: prior exposure to programming and/or software, such as R, SAS, and Matlab. 

Data Visualization and Data Technologies (STAT:4580, 3 semester hours)

Introduces common techniques for visualizing univariate and multivariate data, data summaries, and modeling results. Students will learn how to create and interpret these visualizations, and to assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking.  Data technologies for obtaining and preparing data for visualization and further analysis will also be discussed. Students will also learn how to present their results in written reports and to use version control to manage their work.

Computing in Statistics (STAT:5400, 3 semester hours)

Python, R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlo studies, bootstrap, etc.). 

Probabilistic Statistical Learning (DATA:4750, 3 semester hours)

This course focuses on essential machine learning and statistics ideas that are critical in analyzing modern complex and large data. Selected topics are covered in supervised learning: linear models, deep neural networks, and non-parametric models. Besides supervised learning, essential topics from non-linear dimension reduction, clustering, and recommender systems are part of the course.

Master’s second-year core courses (DATA:5890 MS Data Science Practicum–1 course totaling 2 semester hours)

Each student will be supervised by a faculty member to complete a project that solves a real‐world problem using knowledge gained from the core courses. Students are required to submit a written report and give an oral presentation of their projects; the written report must include the background and significance of the problem, analysis method, presentation and interpretation of the results including tables and visualization, discussion, and references, plus appendices comprising technical details and documentation of computer code used in the analysis. A capstone committee consisting of three faculty members will evaluate the capstone projects and assign the final grades (S or U), with inputs from the supervising faculty members.

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SpaceX promotes Falcon 9 rocket launches on their site at $62 million, while other providers charge over $165 million due to SpaceX's ability to reuse the first stage. Therefore, accurately predicting a successful landing of the first stage allows estimating the launch cost. This insight is valuable to competitors seeking rocket launch contracts as it enables them to understand the cost advantage achieved by SpaceX through reusability. By knowing whether the first stage will land successfully, alternate companies can determine their competitive pricing strategies and improve their chances of securing contracts in the rocket launch market, potentially disrupting SpaceX's dominant position.

• Jupyter Notebook 99.8%
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