Hyacinth Ampadu (@JoAmps)
  • Stars
    star
    13
  • Global Rank 834,067 (Top 29 %)
  • Followers 18
  • Following 20
  • Registered over 4 years ago
  • Most used languages
    Python
    22.2 %
  • Location 🇬🇭 Ghana
  • Country Total Rank 508
  • Country Ranking
    Python
    217

Top repositories

1

Lead_conversion_prediction_for_a_mobile_app_company

Predicting if a lead would convert on an app, deployed using fastapi, with streamlit as frontend via CI/CD using GitHub Actions, and containerised using Docker
Jupyter Notebook
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2

Classical-Machine-Learning-Folder

Building complex Machine learning models to make predictions on data
Jupyter Notebook
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3

JoAmps-Udacity-Machine-learning-Devops-Engineer-Nanodegree-MLOPS-

My projects in the Udacity MLOPS nano degree course
Jupyter Notebook
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4

PowerSystemsBot

An interactive Q & A bot that helps you quickly find answers and information on all programs during electrical engineering masters studies in KNUST
Python
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5

Churn-prediction-in-a-vehicle-insurance-company-in-Ghana

Predicting customer churn in a vehicle insurance company in Ghana, deployed on Heroku, via CI/CD using GitHub Actions and set up monitoring to detect model and concept drifts
Jupyter Notebook
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6

Forecasting-average-weekly-prices-of-the-Parallel-Alpha-nft

Forecast next week prices of the parallel alpha nft, using data from an api, stored in postgresql database and forecasted using 3 time series models with results deployed to a Tableau dashboard
Jupyter Notebook
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7

KidFriendlySocial

A safe social media webapp using NLP models(BERT and GPT3) for bad language detection and content recommendations, deployed using docker and Kubernetes on digital ocean
Python
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8

AWS_ML_architecture_for_cost_prediction_of_sporting_equipment

Build and Train a model on AWS sagemaker, and automate the various data and machine learning tasks using AWS step functions via AWS lambda and deploy to a webapp
Jupyter Notebook
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9

JoAmps

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10

Tensorflow-for-Computer-vision-Folder

Building and Training models to enable computers to interpret and understand the visual world using Tensorflow
Jupyter Notebook
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