This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
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Updated
Nov 22, 2017 - Python
This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
Predict Air Grade
Creating a Model to predict if a user is going to buy the product or not based on a set of data
MINIST dataset is a database of handwritten digits , classified with 3 models (K nearest neighbor, support vector machine, Artificial Neural Network)
Implementation of K-Nearest Neighbour Algorithm on iris dataset from scratch.
3 ML methods from scratch to classify urban sounds
Python implementation of different clustering algorithms.
This repository showcases my foundational knowledge in machine learning concepts, based from the Machine Learning series by TechWithTim. It serves as my requirement for aspiring members of the PUP Hygears Programming Team, demonstrating my ability to apply theoretical knowledge to practical implementations.
Using k-nearest neighbors, and infinite-lookback ngrams with LLMs
A processing of a simplified video game dataset using k-nearest neighbors, and using Synthetic Data Vault and Regression/Classification models to verify synthetic model accuracy
Starting with Machine Learning (On-Hold)
This project aims to develop an AI/ML model to predict loan repayment failure using a historical dataset of borrowers, their features, and loan characteristics. The problem is significant as it can help financial institutions identify potential defaulters and take preventive measures to minimize losses.
This project was developed for the CSC-481: Artificial Intelligence class at Southern Connecticut State University. The purpose of this assignment was to use the K-Nearest Neighbor classifier, as well as Decision Tree classifier, to create AI models that could identify the gender of an individual from the provided face dataset.
This repository contains a machine learning project that applies the K-Nearest Neighbors (KNN) classification algorithm to predict car safety ratings. The project uses a dataset of cars, with features such as buying price, maintenance cost, number of doors, persons, lug boot size, and safety.
MNIST classification challenge held as a part of Machine Learning course at TU Munich.
Predicts gender of the voice signal
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