Solve complex real-life problems with the simplicity of Keras
-
Updated
Apr 18, 2019 - Jupyter Notebook
Solve complex real-life problems with the simplicity of Keras
Android app for vehicle tracking & prediction with Jetpack Compose. Features data collection, visualization, & ML integration for training & predictions.
Built various machine learning models for banks to develop effective credit rating
Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;
The project tries to develop & compare 3 different Machine Learning methods that could better predict in employee attrition.
This project creates a binary classifier that is capable of predicting whether applicants will be successful if funded by the Alphabet Soup Charity Program
Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.
Add a description, image, and links to the model-accuracy topic page so that developers can more easily learn about it.
To associate your repository with the model-accuracy topic, visit your repo's landing page and select "manage topics."