An image classification model using K Nearest Neighbours algorithm is implemented to differentiate between cats and dogs in an input image. The .yml file contains the trained model details created with CV Studio and a dataset of cat/dog images from IBM Object Storage.

Octree/Quadtree/N-dimensional linear tree

This project is an application for classifying the quality of coconuts using the K Nearest Neighbors algorithm. It is built with Streamlit for easy deployment.

Data Science - K-Nearest Neighbors (KNN) Work

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

Self Work Coding Files related to Data Science

Intro to k-nearest neighbors (KNN) with cuML.

Predicting the champion of the 2023 Cricket World Cup through the implementation of the Random Forest algorithm.

The purpose of this project is to promote understanding -- my own and others' -- of fundamental data science and machine learning concepts and tools. It currently consists of one notebook that classifies fruit types based on weight, volume, and image data.

This model predicts whether the survivors of the Titanic survived or not. In this file, different classification models are compared and predictions are done from the model(s) having highest accuracy. Here, 'training_data.csv' is used for training and testing the models and 'testing data.csv' is used for predictions. These data sets are from Kaggle

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository

Using Machine Learning to rank a list of customers most likely to buy a Car Insurance for a cross-sell campaign.

Flight_Price Prediction using Machine Learning.(Regression Use Case)

Created a Python program for K Nearest Neighbor Algorithm implementation from scratch. Determined the Euclidean distance between the data points to classify a new data point as per the maximum number of nearest neighbors. Implemented the algorithm on sklearn’s IRIS dataset which achieved an accuracy of 95.56%.

In this repository I am gonna show the main and most popular non-supervised clustering algorithms with short explanations.

Machine learning and Deep learning project

Esse pequeno projeto tem como objetivo fazer testes de acurácia com rede neural com apenas um neurônio sem classificadores e com 2 (dois) classificadores, sendo eles KNN e 1R.

Using machine learning algorithms to predict SpaceX Falcon 9's First Stage Landing Sucess

Project 1 for Introduction to Machine Learning course.