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This Repository was created to house my completed notebooks and powerpoint for the IBM Data Science Professional Certificate Capstone Project for predicting Space X Falcon 9 first stage landing success. The date as of writing this readme is 2/19/2022. I hope you enjoy the project!
The Denis O'Byrne IBM Data Science Capstone Project.pdf file is the presentation for the assignment
The notebook files were completed as part of the assignment and all data is hosted online except for the data for the dashboard project so that is provided in the repository Some files like the Plotly Dashboard and the Folium Visualizations need to be run externaly as they are not interactive on github. They should run without issue through jupyter environments on your local computer.
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IBM SpaceX Capstone Project
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In this project, I will work in SpaceX company and try to predict the Falcon 9 first stage. It’s important to know if the rockets will land successfully or not because the failure will cost the company many resources.
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- 1. Yousef Elbayoumi January 20, 2022
- 2. 2 • Executive Summary • Introduction • Methodology • Results • Conclusion • Appendix Outline
- 3. 3 • Data were collected with several ways • Machine learning models were built • Data visualizations were created Executive Summary • Summary of methodologies • Summary of all results • The optimal model was acquired • Visualizations were great for decision making
- 4. 4 Introduction • Project background and context In this project I will work in SpaceX company and try to predict the Falcon 9 first stage. It’s important to know if the rockets will land successfully or not because the failure will cost the company much resources. • Problems that need answers 1. Which factors are behind the failure of landing? 2. Will the rockets land successfully? 3. What the accuracy of a successful landing?
- 6. 6 Executive Summary • Data collection methodology: • With Rest API and Web Scrapping • Perform data wrangling • Data were transformed and one hot encoded to be apply later on the Machine Learning models • Perform exploratory data analysis (EDA) using visualization and SQL • Discovering new patterns in the data with visualization techniques such as scatter plots • Perform interactive visual analytics using Folium and Plotly Dash • Dash and Folium were used to achieve this goal • Perform predictive analysis using classification models • Classification machine learning models were built to achieve this goal Methodology
- 7. 7 Data sets were collected using the API call from several websites, I collected rocket, launchpad, payloads, and cores data from https://api.spacexdata.com/v4 website. Data Collection 1. Collecting the data with API call 2. Converting to data frame with help of JSON 3. Updating columns and rows (pre-processing) 4. Filtering the data to keep only Falcon 9 launches 5. Convert the data to csv file with name ‘dataset_part_1.csv’
- 8. Data Collection – SpaceX API 8 1. Collecting the data with API call 2. Converting to data frame with help of JSON 3. Updating columns and rows (pre-processing) 4. Filtering the data to keep only Falcon 9 launches 5. Convert the data to csv file with name ‘dataset_part_1.csv’ GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/1.%20spacex-data- collection-api
- 9. 9 Data Collection - Scraping 1. Creating the BeautifulSoup object 2. Getting column names 3. Creating the launch_dict 4. Converting to final data frame 5. Convert the data to csv file with name ‘spacex_web_scraped.csv’ GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/2.%20webscraping
- 10. 10 Data Wrangling 1. Loading the data set 2. Creating landing outcomes 3. Finding the bad outcomes 4. Presenting outcomes as 0 and 1 5. Determining the success outcome 6. Convert the data to csv file with name ‘dataset_part_2.csv’ GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/3.%20spacex-data- wrangling
- 11. 11 EDA with Data Visualization Categorial plot between Flight number and Pay load mass (kg) Bar chart between Orbit and Success rate of each orbit Scatter plot between Orbit and Flight number Line chart between Year and Success rate GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/5.%20eda-dataviz
- 12. 12 I used SQL queries to answer the following questions: • 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 achieved • 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 the 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 EDA with SQL GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/4.%20eda-sql
- 13. 13 • folium.Marker() was used to create marks on the maps. • folium.Circle() was used to create a circles above markers on the map. • folium.Icon() was used to create an icon on the map. • folium.PolyLine() was used to create polynomial line between the points. • folium.plugins.AntPath() was used to create animated line between the points. • markerCluster() was used to simplify the maps which contain several markers with identical coordination. Build an Interactive Map with Folium GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/6.%20launch-site- location
- 14. 14 Build a Dashboard with Plotly Dash • Dash and html components were used as they are the most important thing and almost everything depends on them, such as graphs, tables, dropdowns, etc. • Pandas was used to simplifying the work by creating dataframe. • Plotly was used to plot the graphs. • Pie chart and scatter chart were used to for plotting purposes. • Rangeslider was used for payload mass range selection. • Dropdown was used for launch sites. GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/7.%20dashboard
- 15. Predictive Analysis (Classification) 1. Building the model 2. Evaluating the model 3. Finding the optimal model 15 Create column for the class Standardize the data Split the data info train and test sets Build GridSearchCV model and fit the data Find the best hyperparameters for the models Find the best model with highest accuracy Confirm the optimal model Calculating the accuracies Calculating the confusion matrixes Plot the results GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project/tree/main/8.%20machine- learning-prediction
- 16. • Exploratory data analysis results • Interactive analytics demo in screenshots • Predictive analysis results 16 Results
- 17. 18 With the increase of flight number, the success rate is increasing as well in the launch sites Flight Number vs. Launch Site
- 18. 19 Payload vs. Launch Site With the increase of Pay load Mass, the success rate is increasing as well in the launch sites
- 19. 20 Success Rate vs. Orbit Type ES-L1, GEO, HEO, and SSO have a success rate of 100% SO has a success rate of 0%
- 20. 21 Flight Number vs. Orbit Type It’s hard to tell anything here, but we can say there is no actual relationship between flight number and GTO.
- 21. 22 Payload vs. Orbit Type First thing to see is how the Pay load Mass between 2000 and 3000 is affecting ISS. Similarly, Pay load Mass between 3000 and 7000 is affecting GTO.
- 22. 23 Launch Success Yearly Trend Since the year 2013, there was a massive increase in success rate. However, it dropped little in 2018 but later it got stronger than before.
- 23. 24 All Launch Site Names We can get the unique values by using “DISTINCT”
- 24. 25 Launch Site Names Begin with 'CCA' We can get only 5 rows by using “LIMIT”
- 25. 26 Total Payload Mass We can get the sum of all values by using “SUM”
- 26. 27 Average Payload Mass by F9 v1.1 We can get the average of all values by using “AVG”
- 27. 28 First Successful Ground Landing Date We can get the first successful data by using “MIN”, because first date is same with the minimum date
- 28. 29 Successful Drone Ship Landing with Payload between 4000 and 6000 The payload mass data was taken between 4000 and 6000 only, and the landing outcome was determined to be “success drone ship”
- 29. 30 Total Number of Successful and Failure Mission Outcomes We can get the number of all the successful mission by using “COUNT” and LIKE “Success%” We can get the number of all the failure mission by using “COUNT” and LIKE “Failure%”
- 30. 31 Boosters Carried Maximum Payload We can get the maximum payload masses by using “MAX”
- 31. 32 2015 Launch Records We can get the months by using month(DATE) and in the WHERE function we assigned the year value to “2015”
- 32. 33 Rank Landing Outcomes Between 2010-06-04 and 2017-03-20 By using “ORDER” we can order the values in descending order, and with “COUNT” we can count all numbers as we did previously
- 33. 35 All Launch Sites’ Location Markers All the launches are near USA, Florida, and California
- 34. 36 Color-labeled Launch Outcomes Green means successful Red means Failure
- 35. 37 Launch Sites to its Proximities All distances from launch sites to its proximities, they weren’t far from railway tracks.
- 36. 39 Launch Success Count KSC LC-39A has the highest success score with 41.7% CCAFS LC-40 comes next with 29.2% Finally, VAFB SLC-4E and CCAFS SLC-40 with 16.7% and 12.5% respectively
- 37. 40 Launch Site with Highest Score KSC LC-39A has the highest score with 76.9% with payload range of 2000 kg – 10000 kg, and FT booster version has the highest score
- 38. 41 Payload vs. Launch Outcome Payload 0 kg – 5000 kg (first half) Payload 6000 kg – 10000 kg (second half)
- 39. 43 Classification Accuracy Decision Tree has the highest accuracy with almost 0.89, then comes the remaining models with almost same accuracy of 0.84
- 40. 44 Confusion Matrix Sensitivity = 1.00, formula: TPR = TP / (TP + FN) Specificity = 0.50, formula: SPC = TN / (FP + TN) Precision = 0.80, formula: PPV = TP / (TP + FP) Accuracy = 0.83, formula: ACC = (TP + TN) / (P + N) F1 Score = 0.89, formula: F1 = 2TP / (2TP + FP + FN) False Positive Rate = 0.50, formula: FPR = FP / (FP + TN) False Discovery Rate = 0.20, formula: FDR = FP / (FP + TP) True Positive (TP) False Positive (FP) False Negative (FN) True Negative (TN)
- 41. 45 • We found the site with highest score which was KSC LC-39A • The payload of 0 kg to 5000 kg was more diverse than 6000 kg to 10000 kg • Decision Tree was the optimal model with accuracy of almost 0.89 • We calculated the launch sites distance to its proximities Conclusions
- 42. 46 All codes can be found on my GitHub Appendix GitHub repo: https://github.com/yousefhosam- b/IBM_SpaceX_Capstone_Project
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Project Scenario and Overview
4.7 (6,479 ratings)
120K Students Enrolled
Course 10 of 10 in the IBM Data Science Professional Certificate
This Course
Video Transcript
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.
Skills You'll Learn
Methodology, Github, Jupyter Notebook, Data Science Methodology, K-Means Clustering
- 5 stars 80.29%
- 4 stars 14.13%
- 3 stars 3.13%
- 2 stars 1.09%
- 1 star 1.34%
Mar 3, 2020
Very good capstone project. Learnt lot of insights on how to represent data through out this course.
Very good starting point for ""Data Science" field. I would definitely recommend this course.
Jun 6, 2020
This Program was well structured, it was a great combination of learning and problem solving. You also got a great chance to see how useful data can be and how easy it is to make it work for you
From the lesson
Introduction
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. In this module, you will be provided with an overview of the problem and the tools you need to complete the course.
Ph.D., Data Scientist and Developer
Joseph Santarcangelo
Ph.D., Data Scientist at IBM
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Towards Data Science
Jun 27, 2021
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Be deliberate in the problem-solving process
Ibm data science capstone project — battle of the neighborhoods, addressing a business problem using a combination of structured problem solving, data analysis & machine learning.
The cumulation of Coursera’s IBM Data Science Professional Course is a capstone project that requires course participants to identify a business problem that requires the use of location data and neighborhood clustering. The ability to analyze business problems, cut through the noise, and identify the actual issue to be addressed is an important skill to have and constantly hone. If the right questions are not identified, the effectiveness of the model results would be greatly diminished or rendered meaningless. Therefore, I had also applied several of Boston Consulting Group’s problem-solving approaches that I was recently exposed to while working on this project to enhance the framing of the problem statement.
This article provides an overview of the capstone, from the initially proposed problem statement to recommendations.
Introduction/ Business Problem
Data collection & preparation, data visualization & troubleshooting, exploratory data analysis, silhouette method, evaluation & recommendations, other tools.
Context: A client has approached the Consultation firm to advise on the business strategies and execution roadmap on setting up restaurants in Kyoto. The initial business problem question is “Should the Client set up a restaurant chain in Kyoto, and where?”
Rather than diving into the problem and apply machine learning, I deliberated on the business problem and outlined the following considerations which may not be apparent:
a) The Client is targeting to set up restaurant presence in Kyoto
b) They are not certain of the market saturation nor potential locations in Kyoto to act on.
Reframing the problem statement as “What can be done to determine the Client’s business positioning and potential restaurant locations in Kyoto?” With the reframed problem statement, several top-level business drivers viz. Business Strategies, Operations, and Profitability are brought into focus as illustrated below.
Regarding Business Strategies, K-Means clustering will be applied to the relevant restaurants’ geospatial data to cluster restaurants and uncover insights such as restaurant themes and suitable locations.
Two data sources were identified for use. These are:
1) List of Kyoto wards and their respective geo coordinates . The wards list can be retrieved from the following webpage , whereas the corresponding coordinates can be retrieved using the geopy library.
2) Restaurants in each neighborhood of Kyoto . The data can be retrieved using the Foursquare API and specifying the particular category of interest.
The list of Kyoto wards was scraped using the pandas read_html() method.
The pandas get_levels() method is then used to collapse the headers.
The data is then furnished with the geographical coordinates retrieved using the geopy library. A user-agent is specified per Nominatum’s terms of service; it is also to avoid one’s IP address from being blocked from accessing the service.
Visualization of the Kyoto wards is done using the folium library.
It was found that ‘Kita-Ku’ and ‘Minami-Ku of Kyoto are not plotted on the Kyoto Map. One hypothesis their coordinates could be referring to other cities’ wards. To begin, I might check the returned addresses, find the correct coordinates. Lastly, I will replace the corrected coordinates in the data frame.
The returned addresses confirm the hypothesis that the retrieved coordinates are off. Adding the city name “Kyoto” to the ward names enables the correct coordinates to be acquired. The code snippet for this task is reproduced below.
The updated map verifies the corrected coordinates with all eleven wards plotted on the map.
Foursquare API is then utilized to retrieve the data on restaurants in each of these wards. Specifying the criteria to return 100 restaurants within a radius of 500 meters, the result is visualized below. No surprise on Ramen and Japanese restaurants being the most common types of restaurants.
Compared with the other wards, Higashiyama-Ku, Ukyō-Ku, Nishikyō-Ku & Sakyō-Ku have a higher density of restaurants.
Noting the characteristics (i.e. restaurant types by proportion for each ward), K-Means clustering will be applied to cluster these entities and uncover potential insights such as viable restaurant themes and suitable restaurant locations. These insights could enhance the formulation of business strategies.
For K-Means clustering, the optimum number of k first needs to be established. This is a step that could potentially be overlooked. Several methods are available, such as the Elbow method and the Silhouette method. These methods are briefly discussed below.
Elbow Method. The Within-Cluster-Sum of Squared Errors (WSS) is calculated for different values of k. Select the k value for which WSS first starts to decrease (i.e. ‘elbow’). However, if the dataset is not well clustered (i.e. overlapping clusters), the elbow may not be distinct.
Silhouette Method. The Silhouette method measures the similarity of a point to its own cluster, compared to other clusters. The range of the Silhouette coefficients is between +1 and -1. A positive coefficient tending close to +1 indicates the particular point is assigned in the ideal cluster. It also implies point is as practically distanced from the neighboring clusters as possible. A coefficient of zero indicates that the particular point is on or very close to the decision boundary between two neighboring clusters. A negative coefficient indicates that the point has been assigned to the wrong cluster.
The Silhouette method is selected for determining the optimum k value.
The K-Means clustering is then implemented with init=’k-means++’, and random_state=42 for reproducibility of results. The resulting clusters are then plotted.
Examining each cluster based on venue categories, the following observations are derived.
Ramen restaurants are predominantly prevalent in cluster 1. This is closely followed by restaurants offering Asian-styled cuisines such as Chinese, Yoshoku, or Sushi dishes.
Japanese restaurants are predominantly prevalent in cluster 2. This is closely followed by a mix of either Chinese or Ramen or Donburi restaurants.
Udon restaurants are predominantly prevalent in cluster 3, followed by Donburi restaurants.
Exploring the Neighbourhoods in Kyoto, Higashiyama-Ku, Ukyō-Ku and Nishikyō-Ku & Sakyō-Ku have a higher density of restaurants (more than 10 in its area). The higher restaurant density could imply these areas as being more popular with visitors with more tourist attractions in their vicinity.
For example, the ward of Higashiyama-Ku features many historical sights such as the entertainment district of Gion in front of Yasaka Shrine, Ninenzaka, Sannenzaka, and Kiyomizu Temple (designated as World Heritage site). Ukyō-Ku is also home to many famous sites such as Tenryū-Ji, and Arashiyama, a hill famed for its maple leaves.
The Preliminary recommended locations are Higashiyama-Ku and Ukyō-Ku for market entry. Higashiyama-Ku is assigned to cluster 2; a restaurant offering Japanese cuisine could have a higher chance of success with the visitors. Ukyō-Ku is assigned to cluster 1; a restaurant offering Ramen could have a higher chance of success with the visitors. Regardless of the above recommendations, the other fundamentals of F&B service such as quality food & services and strict hygiene practices are not to be overlooked.
For this capstone project, I also tried out the readme.so for the creation and editing of the readme file. The tool provides several predefined templates for editing. Users only need to select the desired templates, make the necessary edits and upload the generated readme file to Github.
I would say the capstone project is an enriching experience with the introduction of an API for data collection and being able to work on geospatial data. While it could have been easy to state a business problem statement and apply a machine-learning technique, the capstone presented a good opportunity to apply BCG’s business problem-solving approaches. It had helped enhance the framing of the problem statement. Without applying these approaches, the capstone could have been more difficult without a clear direction and purpose for machine learning application.
The code for the capstone can be accessed here .
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SpaceX Rocket Delivers Aggie Capstone Project To International Space Station
The SpaceX Falcon 9 rocket with the Dragon cargo module lifted off from the Florida Space Launch Complex 40 at Cape Canaveral Air Force Station on May 4. The Commercial Resupply Services mission (CRS-17) launched over 5,550 pounds of NASA cargo into orbit and successfully delivered it to the International Space Station (ISS) on May 6.
Included in this cargo was a Texas A&M University undergraduate capstone team’s final project, an experiment facility known as Hermes, which will be used to conduct experiments on asteroid particles in space.
The team, made up of Multidisciplinary Engineering Technology (MXET) mechatronics students Luis Orozco (embedded software), Dustin Tish (hardware systems) and Jeremy Coffelt (Python software, security and communications), celebrated the culmination of over a year’s work as they watched their project be launched into space.
The students took over the Hermes prototype that was developed by an electronic systems engineering technology capstone team and moved their design to the three production systems that underwent testing, validation and acceptance by T STAR, NASA engineers and scientists at the Johnson Space Center.
The first of its kind, Hermes is a Class-1E experiment facility that will host and support four microgravity experiment tubes that will be changed out every three to six months by an ISS mission specialist through 2024. The first set of four Hermes experiments will investigate microgravity effects on regolith (moon dust) simulants.
“This is the type of project that clearly demands the broad-based experiential education available to students pursuing the new MXET mechatronics focus area at Texas A&M,” stated Matthew Leonard, president of T STAR.
Hermes is a self-contained system that is responsible for the monitoring and control of all aspects of the facility. The system is composed of seven embedded intelligence devices. Six of them use a real-time operating system environment to monitor and control particular aspects of the facility or an individual experiment. One of the devices is a BeagleBone Black, which is the overall system and communications manager providing near real-time data and control capabilities for the scientists and engineers on earth. Hermes provides lighting, imagery and vacuum resources to each experiment while transferring all data collected to both the solid-state storage that is part of the experiment cassette and the ISS network drive to enable downloading on Earth. In addition, Hermes supports the addition of an external accelerometer that provides microgravity data to the scientists.
The public-private-academic model of applied research developed by T STAR has made the design and development of Hermes an unprecedented success. Using a capstone project to undertake the initial prototype design before moving that project to an applied research effort for product delivery is a unique approach to developing space-worthy systems within budget and schedule requirements.
T STAR has implemented this work model in two other Texas A&M capstone projects, Helikite and Cardinal.
Helikite has produced a working prototype capable of measuring small fluctuations in magnetic fields. This system will be tested in late June when it is attached to an ultralight aircraft and then towed by a ground-based vehicle. The system will allow NASA scientists to evaluate whether or not it could one day be used on Mars.
The Cardinal project implements a new approach to collecting asteroid particles at high altitudes. It is intended to be an experimental facility attached to a high-altitude weather balloon. Once launched, it will monitor several environmental parameters including barometric pressure. When a preset altitude is reached, the Cardinal will automatically open its collection chamber and begin rotating the dust collection arm at a fixed rate. Once the system determines that the balloon is in position, the rotating arm will be stowed and the chamber closed. This new concept of collecting particles will provide a longer mission time with less contamination from the collection system itself.
From the Hermes project to Helikite and Cardinal, Aggie students are changing the future of space experimentation and exploration.
This article originally appeared on the College of Engineering website.
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GitHub - chuksoo/IBM-Data-Science-Capstone-SpaceX: In this project, we predicted if the Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders. chuksoo / IBM-Data-Science-Capstone-SpaceX main 1 branch 0 tags Code chuksoo Update README.md
IBM-Data-Science-Capstone-Project Hello, This Repository was created to house my completed notebooks and powerpoint for the IBM Data Science Professional Certificate Capstone Project for predicting Space X Falcon 9 first stage landing success. The date as of writing this readme is 2/19/2022. I hope you enjoy the project!
Ibm data science capstone project-SpaceX launch analysis Jan. 04, 2022 • 2 likes • 8,192 views Download Now Download to read offline Data & Analytics 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
IBM SpaceX Capstone Project Feb. 25, 2022 • 2 likes • 1,526 views Download Now Download to read offline Software In this project, I will work in SpaceX company and try to predict the Falcon 9 first stage. It's important to know if the rockets will land successfully or not because the failure will cost the company many resources. YousefElbayomi
(PDF) IBM Data Science Capstone Project -Space X IBM Data Science Capstone Project -Space X July 2021 Authors: Ashlin Darius Govindasamy University of South Africa Abstract I...
This project is part of the IBM Data Science Certification program The commercial space age is here; several companies such as Virgin Galactic, Blue Origin, and SpaceX are making space travel affordable for everyone. Among all of them, SpaceX perhaps stands out as the most successful.
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.
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.
Originally I have shared it to my GitHub repository IBM-Applied-Data-Science-Capstone SpaceX Falcon 9 first stage Landing Prediction ¶ Lab 1: Collecting the data ¶ Estimated time needed: 45 minutes In this capstone, we will predict if the Falcon 9 first stage will land successfully.
IBM Data Science Capstone Project - Space X. Ashlin Darius Govindasamy Outline • Executive Summary • Introduction • Methodology • Results • Conclusion • Appendix. 2 Executive Summary • Summary of methodologies - Data collection - Data wrangling - EDA with data visualization - EDA with SQL - Building an interactive map with Folium - Building a Dashboard with Plotly Dash ...
IBM Db2 - Db2 is a family of data management products, including database servers, developed by IBM Jupyter Notebooks - Open-source web application that allows data scientists to create and share documents that integrate live code, equations, computational output, visualizations, and other multimedia resources.
IBM Data Science Capstone Project — Battle of the Neighborhoods | by ShengJun | Towards Data Science 500 Apologies, but something went wrong on our end. Refresh the page, check Medium 's site status, or find something interesting to read. ShengJun 136 Followers Engineer | Analyst | Matcha & Coffee lover | Iaidoka Follow More from Medium Zach Quinn
The SpaceX Falcon 9 rocket with the Dragon cargo module lifted off from the Florida Space Launch Complex 40 at Cape Canaveral Air Force Station on May 4. The Commercial Resupply Services mission (CRS-17) launched over 5,550 pounds of NASA cargo into orbit and successfully delivered it to the International Space Station (ISS) on May 6.