{"id":26169062,"url":"https://github.com/felixcharotte/ibm_datascience_capstone","last_synced_at":"2025-07-26T12:34:02.056Z","repository":{"id":280327504,"uuid":"941632937","full_name":"FelixCharotte/IBM_DataScience_Capstone","owner":"FelixCharotte","description":"In this project, we predicted if the SpaceX Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders.","archived":false,"fork":false,"pushed_at":"2025-03-12T21:25:07.000Z","size":21481,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-04T21:24:29.619Z","etag":null,"topics":["analysis","data-analysis","data-science","data-visualization","databases","folium","jupyter-notebook","machine-learning","machine-learning-alrgorithms","matplotlib","pandas","plotly","plotly-dash","python","scikit-learn","scipy","seaborn","sql"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/FelixCharotte.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-03-02T18:37:13.000Z","updated_at":"2025-04-02T20:14:13.000Z","dependencies_parsed_at":"2025-06-04T15:58:22.131Z","dependency_job_id":null,"html_url":"https://github.com/FelixCharotte/IBM_DataScience_Capstone","commit_stats":null,"previous_names":["felixcharotte/ibm_datascience_capstone"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/FelixCharotte/IBM_DataScience_Capstone","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixCharotte%2FIBM_DataScience_Capstone","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixCharotte%2FIBM_DataScience_Capstone/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixCharotte%2FIBM_DataScience_Capstone/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixCharotte%2FIBM_DataScience_Capstone/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/FelixCharotte","download_url":"https://codeload.github.com/FelixCharotte/IBM_DataScience_Capstone/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FelixCharotte%2FIBM_DataScience_Capstone/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267168763,"owners_count":24046707,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-26T02:00:08.937Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data-analysis","data-science","data-visualization","databases","folium","jupyter-notebook","machine-learning","machine-learning-alrgorithms","matplotlib","pandas","plotly","plotly-dash","python","scikit-learn","scipy","seaborn","sql"],"created_at":"2025-03-11T18:59:09.197Z","updated_at":"2025-07-26T12:34:02.044Z","avatar_url":"https://github.com/FelixCharotte.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IBM Applied Data Science Capstone\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"/Images/TitlePage.png\" width=\"1200\"\u003e\n\u003c/p\u003e\n\n## 📄 Summary\nThis capstone project will ultimately **predict if the Space X Falcon 9 first stage will land successfully**. \n\nThe full report can be found [here](https://github.com/FelixCharotte/IBM_DataScience_Capstone/blob/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/IBM%20Data%20Science%20Capstone%20Project%202025.pdf).\n\n### Context and Business Understanding\n- SpaceX launches Falcon 9 rockets at a cost of around $62m. This is considerably cheaper than other providers (which usually cost upwards of $165m), and much of the savings are because SpaceX can land, and then re-use the first stage of the rocket. \n\n- If we can make predictions on whether the first stage will land, we can determine the cost of a launch, and use this information to assess whether or not an alternate company should bid against SpaceX for a rocket launch.\n\n## 📑 Main Topics \nThis project follows these steps:\n1. [Data Collection](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/01.%20Data%20Collection)\n    - Making GET requests to the SpaceX REST API\n    - Web Scraping\n2. [Data Wrangling ](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/02.%20Data%20Wrangling)\n    - Using the `.fillna()` method to remove NaN values\n    - Using the `.value_counts()` method to determine the following:\n        - Number of launches on each site\n        - Number and occurrence of each orbit\n        - Number and occurrence of mission outcome per orbit type\n    - Creating a landing outcome label that shows the following:\n        - 0 when the booster did not land successfully\n        - 1 when the booster did land successfully\n3. [Exploratory Data Analysis](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/03.%20Exploratory%20Data%20Analysis)\n    - Using SQL queries to manipulate and evaluate the SpaceX dataset\n    - Using Pandas and Matplotlib to visualize relationships between variables, and determine patterns\n4. [Interactive Visual Analytics](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/04.%20Interactive%20Visual%20Analytics)\n    - Geospatial analytics using Folium\n    - Creating an interactive dashboard using Plotly Dash\n5. [Predictive Analysis (Classification)](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/05.%20Predicitve%20Analysis%20(Classification))\n    - Using Scikit-Learn to:\n        - Pre-process (standardize) the data\n        - Split the data into training and testing data using train_test_split\n        - Train different classification models\n        - Find hyperparameters using GridSearchCV\n    - Plotting confusion matrices for each classification model\n    - Assessing the accuracy of each classification model\n\n\n\n\n\n\n## 🔑 Key Skills Learned/Used \n- Using data science methodologies to define and formulate a real-world business problem\n- Using data analysis and data visualisation to load a dataset, clean it, and find out interesting insights from it\n- Interactive dashboard development with Plotly Dash\n- Interactive map development using Folium\n- Using machine learning to build a predictive model to help a business function more efficiently\n- Structuring and building a data-findings report\n\n## 🏆 Certificates \nTo verify the certificates, click the images to follow the links.\n\u003cp align=\"middle\"\u003e\n  \u003ca href=\"https://coursera.org/share/5e5a605506ebea71a5d4f1a474b50347\"\u003e\u003cimg src=\"/Images/IBM Data Science Capstone Certificate.png\" height=\"420\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://coursera.org/share/5e5a605506ebea71a5d4f1a474b50347\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/84391594/161431807-63db38f1-2203-4383-aa6e-ad8b6e42ee55.png\" height=\"420\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\nAknowledgement to DanielBarnes18\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixcharotte%2Fibm_datascience_capstone","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffelixcharotte%2Fibm_datascience_capstone","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffelixcharotte%2Fibm_datascience_capstone/lists"}