Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Final Year project based upon Network Intrusion Detection System

This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.

Iris classification with Python Scikit-learn :blossom:

Machine learning library for classification tasks

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

Machine learning library for classification tasks

Gaussian Discriminant Analysis introduction and Python implementation from scratch

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

Probabilistic graphical models home works (MVA - ENS Cachan)

Machine learning library for classification tasks

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

Comitê de Classificadores | Projeto N1

To Detect Sepsis Disease using six Classifiers on clinical data

Recognize users of mobile devices from accelerometer data ( Accelerometer Biometric Competition on kaggle)

No description provided.

R | Classification Project

Developed a predictive analytics system to identify student dropout risk using ML models (Random Forest, Logistic Regression, AdaBoost, LDA, QDA) with GridSearchCV tuning. Built interactive dashboards with Streamlit and Tableau for early intervention insights and data-driven decision-making.

Machine Learning Methods apply on Breast Cancer Wisconsin (Original) Data Set.

ML project for the internship at Technocolabs company

A simple 1-dimensional Gaussian Naïve Bayes Classifier.

Built an ML-based HR analytics solution to predict employee turnover. Used SMOTEENN and models like Random Forest, SVM, Logistic Regression, KNN, among others. Optimized with GridSearchCV and RandomizedSearchCV. Visualized insights in Tableau to support retention strategies.

The code for Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

Code for ICASSP paper. FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm

No description provided.

R packages Implementing linear models for classification

Bunch of exercises computed during the Machine Learning for Finance course.

Classification methods applied to an imbalanced big dataset

Developed a machine learning model to predict customer churn in the telecom industry. The project involved EDA, handling class imbalance with SMOTEENN, and applying 10 classification algorithms, such as Random Forest, SVM, Logistic Regression, KNN, and Decision Tree. Model performance was optimized using GridSearchCV and RandomizedSearchCV.