GraphPart, a data partitioning method for ML on biological sequences

This library allows reading and converting bounding box annotations in many popular formats

📁 Repo for python_splitter Python package. This package can split Images into Train, Test, Validation folders automatically by shuffling media/images for machine learning.

This is an algorithm for evenly partitioning.

This repository contains introductory notebook for logistic regression

To create a Decision Tree classifier and visualize it graphically, the purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

Trained and evaluated two supervised machine learning models using original and resampled data to identify 'healthy loan' and 'high risk loan' applicants from financial disclosures.

This project is designed to extract sales data from a PostgreSQL database, process it, and use a Random Forest model to predict sales quantities. It also visualizes real and predicted sales for better understanding.

Code in which an initial approach to decision trees and bagging will be made, and an attempt will be made to ensure that the model can be trained with any dataset coming from Kaggle (for this, we will again use the 'connect with Kaggle' project).

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

This creates an AWS Chatbot to give users their investment portfolio based on their risk tolerance level i.e. conservative, moderate, or aggressive. With the use of machine learning, the tool will be created to different portfolios based off that.

Learning Project ML - Diabetes Prediction

Time series analysis on Yen Futures with ACF, PACF, ADF tests and seasonal decomposition to detect stationary trends. Screen for robust regression models on rolling train-test windows.

A time slicer for training and testing temporally correlated Machine Learning models.

Linear regression models are used to predict football player attacking stats based on attributes like finishing and passing, with the model trained, evaluated, and applied for predictions. Multiple features improve accuracy, and performance is assessed using metrics like MSE and R-squared.

Module 13 - I am creating a binary classification model using a deep neural network by preprocessing data for a neural network model , using the model-fit-predict pattern to compile and evaluate a binary classification model , and optimize the model.

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