A demo of vertical federated learning on simple datasets
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Updated
Nov 21, 2020 - Python
A demo of vertical federated learning on simple datasets
This program consists of clean and polished Graphical User Interface (GUI) that interacts with 8 Machine Learning models and data visualization tools through the use of different Python libraries. The user can interact with the GUI through selecting which model to run on the testing data on, which then takes them to a screen displaying the predi…
Applying Machine Learning Algorithms to the Kaggle "Titanic Survival Prediction Problem".
Testing different ML models on famous Titanic dataset from kaggle. (100% accuracy)
My implementation for Logistic Regression and applying it to different data sets.
Simple Flask App with Logistic Regression model to predict Titanic survivor ⛰💥🚢
This is a machine learning model that predicts whether you would have survived the Titanic incident on 15 April 1912
Introduction to AI and ML Course Project @NanyangTechnologicalUniversity
Intro to machine learning using kaggle's titanic passenger list of survivors
Machine learning project for predicting the deaths on Titanic dataset using logistic regression model.
Uncover the secrets of deploying ML models in production with this tutorial. Leveraging the Titanic dataset, learn the ins and outs of transitioning from research to production. Master modularization, code standards, and scalability for successful machine learning deployments.
Predicting survival rates of the Titanic with a multi-layer perceptron neural network.
An algorithm that determines whether or not you're likely to have survived the Titanic (to an accuracy of 77%).
Data Science Internship at CodSoft
A binary classifier, using logistic regression, to predict whether a person, given its information, would have survived to the Titanic’s disaster.
Used to predict whether a passenger can survive or not by training dataset using logistic regression in python
Parameter tuning and testing to find the best model which works best on the data to bring out the best of data.
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