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)

Application of Machine Learning models to predict Company Bankruptcy

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

Browser-based digit OCR demo using canvas input and a Python neural network backend.

Client-Retention-Insight is a churn classification project to predict whether the customer will Exited or not using the ANN implementation by tensorflow. We have also performed regression on the EstimatedSalary feature and done the HyperparameterTunning to find the best possible parameters for the model.

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.

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.

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

MiniProject môn AI-Intro SOICT, HUST

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.

ARTIFICAL INTELLIGENCE AND MACHINE LEARNING PRACTICE.

📊 Analyze customer churn in telecom using Python. Discover insights to improve retention and make data-driven decisions for better business outcomes.

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

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

A machine learning-based crop recommendation system that predicts the most crop to cultivate based on environmental and soil parameters. This project compares multiple classification algorithms, including SVM, Random Forest, Decision Tree, KNN, Naive Bayes, Logistic Regression, and ANN, to determine the optimal model for accurate crop prediction.

Histopathology image classification to predict molecular subtypes. Includes datasets, EDA and preprocessing notebooks, patch extraction, baseline models, and training utilities for quick experiments and reproducible evaluation. Project for the Artificial Neural Networks and Deep Learning course at Politecnico di Milano (PoliMi) (2025/2026).

This project explores the use of machine learning to predict weather patterns and extreme climate events in Europe using historical data from 18 weather stations. Models like KNN, Decision Tree, and ANN are evaluated to identify the best approach for future forecasting.

Deep Neural Netowrk (TCN + BiLSTM with Attention) model on multivariate time-series classification. Project for the Artificial Neural Networks and Deep Learning course at Politecnico di Milano (PoliMi) (2025/2026).

A artificial neural network regression model

Customer Churn Prediction WIth ANN model by Tensorflow