Built a image classification model that classifies images into one of the 43 classes from the German Traffic sign dataset.

This repository contains the implementation of a recurrent neural network (LSTM from keras library) with the purpose of forecasting target time series, given the targets historical records and covariates. The project uses a toy data set, while focusing on the data transformation tasks (pandas dataframes to 3D numpy arrays required by recurrent n…

XSS Attack Machine Learning detection

A fusion between Python and legend, a name that suggests that the code is both modern and mythical. 3 months of work, with tons of errors to establish the calculations necessary for the superposition, I want to publish my work and improve it and share it under Apache 2.0 License. Designed to work with NBminer!

This is an end-to-end animal face classification model with Keras, KerasTuner, Mlflow, SQLite, Streamlit, and FastAPI which can classify animal faces as either cat, dog or wildlife

binary classifier that utilizes advanced machine learning algorithms and neural networks to accurately predict the success rate of funding applicants

Personal GitHub to host and shares my academic mini-projects related to my master degree.

Detecting Pneumonia with Convolutional Neural Networks

A configurable Python framework for comparing time series forecasting models (SARIMA, Prophet, RNN, LSTM) with evaluation & visualization.

The case study is about India's Largest Marketplace for Intra-City Logistics. This dataset has the required data to train a regression model that will do the delivery time estimation, based on all those features.

Portfolio of Deep Learning projects

DeepNet tuned with Kerastuner added with ensemble of LGBM, HistGBM

This is simple example for using Keras Tuner for selecting the number of hidden layers and hidden neurons.

This repository contains simple usage examples for basic machine learning libraries. These notebooks are tested in Colab.

Google SVHN Prediction using Keras Tuned CNN model and Transfer Learning

End to End implementation for a Flask App in Google Kubernetes Engine. The Notebook has EDA, model selection and training for a unclean structured text data. DNN and Combination of PCA and RandomForest is used for classification.

To predict customer churn rate using Artificial Neural Network using TensorFlow and Keras implemented in Python.