A decision tree is a popular and powerful method for making predictions in data science. Decision trees also form the foundation for other popular ensemble methods such as bagging, boosting and gradient boosting. Its popularity is due to the simplicity of the technique making it easy to understand. We are...
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Using Machine Learning to Predict Fantasy Football Points
A Fantasy Football Trade Analyzer Using RNN-LSTM, ARIMA, XGBoost and Dash
NFL fantasy football is a game in which football fans take on the role of the coach or general manager of a pro football team. Participants draft their initial teams, select players to play each week and trade players in order to compete weekly during a season against other teams....
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A Marijuana Recommendation System Using TF-IDF and k-NN
A Content-based Recommender Using NLP, TF-IDF, k-NN, Pickling and Dash
A content based recommender system works with user provided data to generate recommendations for the user. Recommender systems can be used to personalize information for a user and are commonly used to recommend movies, books, restaurants, and other products and services.
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How to Use NLP to Find a Tech Job and Win a Hackathon
Using Web Scaping, NLP and Flask to Create a Tech Job Search Web App
My front-end web developer Brandon Franks and I recently won the ‘The Most Fascinating Data Science Problem Solved’ award in a recent 30 team, 30 hour Hackathon. Our winning submission was a Web app that scraped job listings from Indeed.com, processed them using Natural Language Processing (NLP) and provided a...
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How to Create an Interactive Dash Web Application
Using Dash, Heroku, XGBoost and Pickling to Create a Predictive Web App
You’ve done the data science, now you need to present the results to the world! Dash is a python framework for building web applications. Written on top of Flask, Plotly.js and React.js, Dash is well-suited for quickly building customized web applications. Once built, the web application can easily be deployed...
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