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decisiontreeclassifier-fromscratch

A Decision Tree Classifier built from scartch in python 3 using the supervised learning methodology. The example here uses the iris data set, but you can load any dataset and it will run for that, just need to change the loading code. To run the program you just have to run the python file. ( Python DecisionTree_fromScratch.py )

document-classification

This is an implementation of document classification using four algorithms: Multinomial Naive Bayes, Support Vector Machines, Random Forests and Logistic Regression.

erp-system

Enterprise Resource Planning (ERP)

image-clustering

This is a simple image clustering algorithm which uses KMeans for clustering and performs 3 types of vectorization using vgg16, vgg19 and resnet50 using the weights from ImageNet

job-website-template-with-next-js-

lda

Topic modeling with latent Dirichlet allocation using Gibbs sampling

linearregression-onevariable-fromscratch

A linear regression in one variable written from scratch in python 3.

logistic-regression-from-scratch

simple logistic regression using the Stochastic gradient optimizer.

neural-network-from-scratch

a simple neural network from scratch in python. this neural network has 3 layers.

openai-pong-reinforcement-learning

The AI will train itself on the game of pong by playing against the computer Bot using reinforcement learning.

react-flask-es

This is a simple CRUD Application with search and pagination functionality as well. The Project is written in: React.JS, Python3-Flask (with ElasticSearch).

regression_classifier_complete

This is a complete regression and classification machine learning model that will automate everything and the user will only have to input the training and testing data.

simple-speech-to-text

this is a simple speech to text program using the speech_recognition module and the google speech to text API

superlaw-marketing-website

Andromeda Nextjs Light is a multipurpose NextJs SAAS Template designed to showcase any SAAS product or solution.

various_lstm_programs

Tuning of the following parameters: 1) Batch Size 2) Number of Epochs 3) Numer of Nerons 4) Sequence Length 5) Optimizer