This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

Project for IBM Data Engineering & Python course on ML & AI -- Created predictions for noise of an airfoil based on various physical features

Independent Project - Kaggle Dataset-- I worked on the California Housing dataset, performing data cleaning and preparation; exploratory data analysis; feature engineering; regression model buildings; model evaluation.

Independent Project - Kaggle Competition -- I worked on the Data Science London data set for the Data Science London + Scikit-learn competition.

Project for University of Michigan Applied Data Science Specialization -- Predicted viewer engagement based on features related to video metrics; evaluated a large set of classifiers under different scoring metrics to select the "optimal" one.

Final Project for IBM Data Science Professional Certificate -- Applied all skills and methods utilized in the series of courses for this certification to predict the success of SpaceX landings; issued full report to stakeholders

Project for University of Michigan Applied Data Science Specialization -- Performed NLP in order to build features of email messages; trained various classification models to help predict if a message was spam.

Project for IBM Data Science course on Machine Learning -- Trained ML models for classification, evaluating based on a variety of metrics

Project for University of Michigan Applied Data Science Specialization -- Analyzed network nodes and edges, developing custom features based on various scoring metrics; used features to train classifier model to predict node attribute (employee salary type) and future edges (employee connections)

Independent Project - Kaggle Dataset-- I worked with the News Category Dataset, which provided a headline and description, etc. in .json format; used NLTK for NLP, tokenizing, lemmatizing, and finding part-of-speech; trained and tuned parameters on classifier models to predict news category based on headline text.

Project for IBM Data Science course on ML Models & Analysis -- Read in large dataset of home sales and utilized polynomial linear regression analysis to make predictions of future home sales prices

Independent Project - Kaggle Dataset-- I worked on the Superstore Sales Dataset, performing (as Part 1) data cleaning and preparation and exploratory data analysis. The main task was to make predictions for future sales based on time-series analysis, which is found in Part 2.

Independent Project - Kaggle Competition -- I worked on the obesity classification data set as part of a Kaggle Competition of the same name, scoring (for accuracy) above 0.9