Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

Spam Email Detector using Naive Bayes, C4.5 Decision Tree, and K Nearest Neighbor algorithms. We found Naive Bayes to be the most accurate at classifying spam.

Analytics Professional Project Work

Essential NLP & ML, short & fast pure Python code

Comparing logistic regression, decision tree, random forest, k-nearest neighbors, and SVMs in regard to binary prediction performance metrics.

This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.

This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

This project classifies images from the Flower102 dataset using k-means clustering followed by K-Nearest Neighbors (KNN) classification. It optimizes KNN parameters to achieve high accuracy, with the best results obtained using 7 clusters and 5 nearest neighbors.

Predict whether a visitor to an e-commerce website will generate revenue by making a purchase during their session based on features such as the number of pages visited, session duration, type of visitor, operating system, browser, and other relevant attributes.

Rcpp bindings for the approximate nearest neighbors library hnswlib

Nearest Neighbor Search with Neighborhood Graph and Tree for High-dimensional Data

Using ML to classify SMS messages into spam and ham

An analysis of the 2023 Chicago Mayoral election.

A song recommendation system using the kNN algorithm and lyrics embeddings to suggest similar songs based on user input.

Exploring the intersection of supervised machine learning algorithms and weather data to drive ClimateWins forward. (CF student project)

Simple implementation of the K-Nearest Neighbour algorithm

Multiple models for binary classification and checking the accuracy with each model.

A project to scrape features about spotify tracks and a website for recommendations from 7mil+ tracks using a custom optimised k-nearest-neigbours algorithm

An exploratory data analysis and machine learning project using the Iris dataset to classify flower species with a K-Nearest Neighbors classifier. It includes data visualization, feature scaling, model training, and evaluation with 100% accuracy on the test set.

k-Nearest Neighbors Algorithm with p-adic Distance