This repository contains two classification projects: credit card default prediction and mobile price classification along with streamlit deployment . supervised-learning machine-learning-classification streamlit-application

Built classifiers using logistic regression and decision trees to classify product reviews and used machine learning techniques such as boosting, precision and recall, and stochastic gradient descent for optimization in Python - agrawal-priyank/machine-learning-classification

nlp text-classification tensorflow classification convolutional-neural-networks sentence-classification fasttext attention-mechanism multi-label memory-networks multi-class textcnn textrnn ... Postgres with GPUs for ML/AI apps. postgres machine-learning sql ai clustering ml regression embeddings artificial-intelligence forecasting classification ann approximate-nearest-neighbor-search knn rag vector-database llm

A final assignment of the course- Machine Learning for Python on Coursera. This notebook gives a good example of using ML framework to realize classification tasks. - patrick013/Classification-Algorithms-with-Python

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks - rasbt/pattern_classification

Build and evaluate various machine learning classification models using Python. - Ansu-John/Classification-Models

This repository contains implementations of seven major classification techniques in both Python and R. Each model is presented in its own folder with a dedicated dataset, clean code, and usage instructions.

Code used for "Machine Learning with Python" workshop at Cambridge Coding Academy: http://cambridgecoding.com/workshop/machine-learning/cambridge ... Module 3: Visualisation of distribution and correlation of feature data from UCI's breast cancer data set · Module 4: Experimenting with k-nearest neighbours classifier on the breast cancer data set, and looking at validation metrics

Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient) ... Focused on advancing credit card fraud detection, this project employs machine learning algorithms, ...

random-forest exploratory-data-analysis classification logistic-regression outlier-detection decision-trees classification-algorithm lead-generation lead-scoring classification-models lead-scoring-case-study ... This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

Project made for Advanced Methods in Machine Learning subject at MINI PW - mateuszdorobek/Machine-Learning-Classification

In this repo, I share my practices in building Machine Learning algorithms for binary and multiclass classfication problems using scikit-learn. ... Figure: Binary classification task: (Left panel) Confusion matrix (middle panel) ROC curve (Right ...

classifier machine-learning random-forest statistical-learning xgboost lightgbm gradient-boosting-machine ensemble-learning cart decision-tree tree-ensemble decision-tree-classifier gradient-boosting classification-trees decision-tree-learning ...

A very simple Random Forest Classifier implementation in python. machine-learning sklearn python3 classifier-model classifier-ensembles machine-learing-algorithms

Codes from Coursera's course Machine Learning - classification, part of machine learning specialization offered by University of Washington - sidsriv/Ml-classification

This project is to test classification algorithms wrote from scratch in python using only numpy. Algorithms wrote in this project: KNN, Logistic Regression and Naive Bayes classifier. machine-learning classification breast-cancer-wisconsin ...

The objectives of this project are to explore how well machine learning algorithms perform when given the task to identify the poor in a given population, and to provide a resource of machine learning techniques for researchers, data scientists, and statisticians around the world. We use a selection of categorical variables from household survey data from Indonesia and Malawi to predict the poverty status of households – a binary class with labels “Poor” and “Non-poor”. Various “out-of-the-box” classification algorithms (no regression algorithms) are used: logistic regression, linear discriminant analysis, k-nearest neighbors, decision trees, random forests, naïve Bayes, support vector machine, extreme gradient boosting, multilayer perceptron, and deep learning.

Binary Classification using Machine Learning. Contribute to abhigyan2311/Binary-Classification-using-ML development by creating an account on GitHub.

machine-learning deep-learning scikit-learn jupyter-notebook cnn kaggle brain cnn-model kaggle-dataset classification-model cnn-classification brain-tumor-classification brain-tumor-detection classification-machine-learning

Contribute to Amitha353/Machine-Learning-Classification development by creating an account on GitHub.