This repository has been created for Udacity Data Scientist Nanodegree Program - Data Engineering Part - Disaster Response Pipeline Project.
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Updated
Jul 18, 2020 - HTML
This repository has been created for Udacity Data Scientist Nanodegree Program - Data Engineering Part - Disaster Response Pipeline Project.
Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.
This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.
Developed a ETL pipeline, a ML training pipeline and a Flask web app that can classify disaster-related messages input by a user.
White and Red Wine classification using logistic regression
Classifying real messages that were sent during disaster events so that they can be sent to an appropriate disaster relief agency.
Pipeline for working with irregular search spaces in Platypus-Opt genetic optimisation
Pipeline for augmenting sparse data for genetic optimisation
Develop a machine learning model that can predict whether people have diabetes when their characteristics are specified
Anomaly Detection Pipeline with Isolation Forest model and Kedro framework
Clean and extract features from a large, scrapped data set from the Palestinian market. Deploying ML Pipeline
This is the first project of the ML-Ops Dicoding class. This project serves to classify whether a title is included in the clickbait category or not.
This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of trained machine learning pipeline.
Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.
Framework3 is a super-simple and robust ML Pipeline for tabular and image competition. The purpose of this is to make the process not too abstract, so that the user can have full control over it.
This repository contains a Machine Learning (ML) pipeline which predicts the response to messages in disaster situations. An ETL pipeline is also developed and everything is deployed with a web app based in Flask.
Example machine learning pipeline with MLflow and Hydra
The code snippet cleans and analyzes a hotel bookings dataset, handling missing values, dropping unnecessary columns, and creating new features. It visualizes the data using various plots and performs feature encoding and selection. It then trains machine learning models to predict hotel booking cancellations.
Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"
42 school project. Process EEG datas by cleaning, extracting, creating a ML pipeline implementing a dimensionality reduction algorithm before finding the right classifier and handling a real time data-stream with sklearn.
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