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This repository consists of the homework assigments as part of BUAN 6341 - Applied Machine Learning. The solutions in this repository are to be used strictly as reference purposes only. Copying or submitting the same solutions found in this repository violates the UTD honor code
Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Numpy, Pandas, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the features. Implemented Algorithms using Scikit-Learn to increase the R2 score.
Contains KNN regression, Linear regression, KNN classification and Decision trees (using gini index, entropy & misclassification rate), all implemented from scratch.
Function which takes training data and their labels, and the size of the neighborhood (K), and then it returns the regressed values for the test data points.
This is a competition for UT Dallas MIS/BUAN students. The purpose of this competition is to evaluate pre-processing steps and use pipelines. The competition is based on a subset of data from Kaggle Competition: https://www.kaggle.com/shree1992/housedata
In the second edition of this Competition featuring data from Woogles.io, participants are challenged to predict the ratings of players based on Scrabble gameplay. The evaluation algorithm is RMSE.
Análise de classificação utilizando o algoritmo k-NN no conjunto de dados Iris e analise de regressão utilizando o algoritmo k-NN no conjunto de dados do setembro amarelo
This repository contains code for predicting stock prices using various machine learning models. The models implemented include Linear Regression, SVM Regression, KNN Regression, Kernel Ridge Regression, and Ridge Regression.
The K-Nearest Neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used for both classification and regression tasks.