ML model for stock trend prediction using Python

This is an algorithm for evenly partitioning.

Codes and templates for ML algorithms created, modified and optimized in Python and R.

An Artificial Neural Network with weight decay created using python using the Numpy library which can read handwritten digits. Uses K-Folds cross validation for training the Neural Network.

Linear Regression Feature Selection and Trainer

Collaborative filtering, word Embedding dense vector representation - NeuralNetwork regression model and K-Fold Evaluation.

PCA applied on images and Naive Bayes Classifier to classify them. Validation, cross validation and grid search with multi class SVM

Artificial intelligence model that detects intrusions in a network by analyzing TCP / IP packets by machine learning algorithms

Data sampling library

A Python script that implements Machine Learning Algorithm to predict if a female is affected by Breast Cancer after considering a certain set of features. The credit of the Dataset goes to UCI Repository of ML.

Used Python Scikit-Learn to analyze Austin car crash data from 2018 to 2020 and created an interactive dashboard using a Random Forest Classifier algorithm to calculate a driver score from user features.

Progetto di Intelligenza artificiale sugli alberi di decisione con valori mancanti.

These are all of my machine learning codes.You can find every code about machine learning.

My very first hands on experiment with CV

GroupSplit is a module to help split datasets into train and test sets for data science and machine learning projects.

Image classification with DeiT model, including data preprocessing, k-fold CV, early stopping and model saving.

The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.

Data sampling library

Machine Learning Algorithms implemented using Numpy and Scipy

Experimenting ML Algorithms, feature selection, cross validation and feature transformation on self-annotated custom Eskişehir real estate dataset. - 2021 - Yildiz Technical University