Customer Churn Prediction WIth ANN model by Tensorflow

Automated Object Detection using VGG16 on an image dataset to extract features with Accuracy score of 95 %

Application of Machine Learning models to predict Company Bankruptcy

A artificial neural network regression model

Classifying one of seven different emotions on a person's face using machine learning and deep learning techniques.

Extractify is Multi-Class Bert-Classifier & ANN Linker Based JSON data extractor, Extractify API Takes Raw-Ocr Data & Classify & Maps Key-Value pairs which is Extremely useful in data extraction.

Extractify is Multi-Class Bert-Classifier & ANN Linker Based JSON data extractor, Extractify API Takes Raw-Ocr Data & Classify & Maps Key-Value pairs which is Extremely useful in data extraction.

Portfolio of machine projects completed by me for academic and self learning purposes

Preprocessing a bank's customer Data and using an Artificial Neural Network to predict the customer churn rate.

Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

ARTIFICAL INTELLIGENCE AND MACHINE LEARNING PRACTICE.

The Cell can certain features than help doctors to make decision whether you can a certain disease or not. ANN model will take long time for image classification and can only work in images concentrated in center. Here is how you do using CNN(Convolutional Neural Network). CNN is the expanded version of ANN.