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Ayoade J.'s Projects

building-a-trained-model icon building-a-trained-model

This module will cover the key stages involved in building a comprehensive program. It also explains how to build and save a model such that you get the same results every time it is run and call a saved model to use it for predictions on unseen data.

ca-------statistical-analysis icon ca-------statistical-analysis

This repository focused on statistical analysis and exploration used on various data sets for personal and professional projects. :chart_with_upwards_trend:

car_insurance_prediction icon car_insurance_prediction

Training and deploying an ML model to predict whether or not an individual is likely to purchase car insurance. Creating a Flask API to serve the persisted trained model in a Docker container via Google Cloud Run, in order to make a prediction on a sample from the test set.

carpooling-website icon carpooling-website

Simple php based carpool website which matches user based on their destination, route.

charity_ml icon charity_ml

Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.

competitive-programming-codes icon competitive-programming-codes

GitHub Repository for storing the Coding files during various contests on Competetive Programming Websites. Currently Coding in PYTHON3

cooking-ai icon cooking-ai

A model that generates random recipes. Trained with 50+ Epicurious recipes.

covid-19-research-project icon covid-19-research-project

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared a dataset of open sourced research papers. This data-set is a resource of over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19, SARS- CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up and extract insight from this growing body work. The goal of this project is to use NLP and other machine learning algorithms learned in this course to develop a tool that can text-mine this database of research articles to gain useful insights about COVID-19 and how we might be able to tackle the outbreak, contain the spread and flatten the curve. The overarching insights that can be acquired from this dataset are numerous and which aspect of the problem you decide to tackle is up to you. For example you may choose to use this dataset to better understand the transmission, incubation and symptoms of COVID-19, look to gain insights around which therapeutics and vaccines may hold promise and warrant further investigation, or you may wish to investigate the risk factors that make COVID- 19 particularly deadly in some patients. The underlying goal of this project is to gain insights from this dataset to better inform how our healthcare system, government, industries can tackle this growing problem.

crop_yield icon crop_yield

A ML model to predict Production of crop yields , on the basis of pattern recognition on the Crop_production dataset.

data-analytics-programming icon data-analytics-programming

Regression model, Unsupervised (K-means) and Supervised (KNN )Machine learning algorithms have been implemented in Python and R.

data-science-21-1.0 icon data-science-21-1.0

This project is designed for helping enthusiasts get a hang of Data Science using Python. It breaks down the useful functions in packages like Numpy, Pandas, Matplotlib, Seaborn and Machine Learning Algorithms. There will be a project at the end of training.

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