MMBazel / Predicting-Kickstarter-Campaign-Outcomes-Using-NLP-Feature-Engineering

Turning raw kickstarter text data => Campaign predictions using SpaCy, Scikit-learn, SQLAlchemy, SQLite3 & XGBoost Classifier (feat eng = Bag-of-Words, Tfdvectorizer)

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πŸ•΅οΈβ€β™€οΈ Classifying Kickstarter Campaigns Utilizing NLP Feature Engineering Techniques πŸ“š

πŸ‘‹ Hi!

My name is Mikiko Bazeley and this is my second capstone project: πŸ”¬ Classifying Kickstarter Campaigns Utilizing NLP Feature Engineering Techniques. πŸ“–

From Oct 2018 to April 2019 I completed a number of projects, including this, as part of the Springboard Data Science Track. 🧠

For this project I incorporated NLP feature engineering techniques (Bag-of-Words, N-Grams and TFID-vectorizer) & the SpaCy πŸ‘©πŸ»β€πŸš€ package to use both quantitative & text data to predict outcomes of Kickstarter campaigns πŸ’‘.

To find out more about this project, check out the attached presentation below!

β˜‘οΈ Jupyter notebook for project: LINK

β˜‘οΈ Final write up: LINK

β˜‘οΈ Slides presentation: LINK

For more information about my Springboard work: πŸ“ All of the documentation, code, and notes can be found here, as well as links to other resources I found helpful for successfully completing the program.

πŸ’¬ For questions or comments, please feel free to reach out on LinkedIn.

⚠️ If you find my repo useful, let me know OR β˜• consider buying me a coffee! https://www.buymeacoffee.com/mmbazel β˜•.


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Turning raw kickstarter text data => Campaign predictions using SpaCy, Scikit-learn, SQLAlchemy, SQLite3 & XGBoost Classifier (feat eng = Bag-of-Words, Tfdvectorizer)


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