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Ayoade J.'s Projects

deploymlmodel-flask icon deploymlmodel-flask

This is a simple project to elaborate how to deploy a Machine Learning model using Flask API.

digit-detection-using-r- icon digit-detection-using-r-

In this project , machine learning ,deep learning tools and technique were used to Analyze the MNIST dataset .MNIST is a large database of handwritten digits that is commonly used for training various image processing systems.

disaster-response-pipelines icon disaster-response-pipelines

The project aim is to analyze disaster data from Figure Eight (https://appen.com) to build a model for an API that classifies disaster messages. the data set containing real messages that were sent during disaster events. I’m creating a machine learning pipeline to categorize these events and then send the messages to an appropriate disaster relief agency. The project also include a web app where an emergency worker can input a new message and get classification results in several categories. The web app also display visualizations of the data. The project done in three phases the first one the ETL pipeline: in this phase I have done loads the messages and categories the datasets, merges the two datasets, cleans the data, and stores it in a SQLite database. In the second, ML pipeline: in this phase I have done loads the data from the SQLite database, splits the dataset into training and test sets, builds a text processing and machine learning pipeline, trains and tunes a model, outputs results on the test set and exports the final model as a pickle file. The last one, the deployment process using Flask web application.

dp203 icon dp203

Exam DP-203: Data Engineering on Microsoft Azure Crash Course

dslectures icon dslectures

Notebooks and material for Data Science lecture (2021) at BFH.

dswss icon dswss

This repository contains all series on the data science workshops. (presentations+codes+exercise)

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