Learn keras with two simple examples
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Updated
May 12, 2018 - Python
Learn keras with two simple examples
Pima Indians Dataset classification using Tensorflow Linear Classifier and DNN Classifier.
This repo related to the analysis of pima-indian-diabetes dataset
Machine Learning Exercise: Using Logistic Regression, Naive Bayes and Random Forest to classify people with and without diabetes based on Pima Indian data from Kaggle
MLP in Keras
Pima-Indian Diabetes Classification
Diabetes Prediction Using Machine Learning Algorithms: Random Forest Classifier, Linear SVM and Logistic Regression in Indian PIMA Diabetes Dataset.
To explore the given dataset for all basic statistics such as the distributions, correlations, outliers, missing values, etc.
A multi-layer perceptron which predicts whether an individual is susceptible to diabetes
Predict diabetes onset for women of Pima Indian Heritage (age 21+) using data from the National Institute of Diabetes and Digestive and Kidney Diseases.
Diabetes Prediction using Decision Tree Algorithm - Machine Learning Project - Pima Indians Diabetes Database - Jupyter Notebook - Python
This is a flask based app for Diabetes Prediction, which provide the website as well as API feature. In this used 'Pima Indian Diabetes Dataset' to build the machine learning model.
Using keras specify-compile-fit- predict workflow on this binary classification problem to investigate if i'll get better predictions.
Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
Implement a feedforward neural network for Pima Indians onset of diabetes dataset using TensorFlow & Keras
Pima Diabetes Outcome
We run the dataset of Pima indians through different learnt Machine Learning techniques using R and then interpreting the results in terms of our research questions and purpose. From this, we were able to deduce the best algorithm as well as the most influential variables for the onset of diabetes with proper mathematical reasoning provided.
A RESTful API using Flask and XGBoost to predict diabetes in Pima Indians based on various diagnostic measurements. Includes training, saving the best model, and testing the API using Python requests.
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