There are 1 repository under knn-regression topic.
Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
Implementation of Regression Models on Navigation with IMUs.
A recommendation system based on Artificial Intelligence to predict best-fit color palettes according to user input
My exercises in the machine learning course
sklearn, tensorflow, random-forest, adaboost, decision-tress, polynomial-regression, g-boost, knn, extratrees, svr, ridge, bayesian-ridge
My solutions to projects given in the Udemy course: Python for Data Science and Machine Learning Bootcamp by Jose Portilla
I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.
Boston house price prediction.
A LibreOffice Calc extension that fills missing data using machine learning techniques
asthma-rates.com - predict asthma rates after changes in social policy - Data Science Capstone Project
Transfer Learning Image Classifier knn image tensorflow js
A k-nearest neighbors algorithm is implemented in Python from scratch to perform a classification or regression analysis.
In this program, I used the KNN model to estimate Iranian universities' entrance exam (konkur) rank, and I also developed a telegram bot so users could use it.
Assignments of the ML Course at IIT Gandhinagar
This repository contains projects related to KNN algorithm using R, Python
All data mining machine learning algorithms are basically coded by displaying solutions with Python
Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Machine Learning engine generates predictions given any dataset using regression
Machine Hack challenge to predict the flight ticket price. A detail description can be found on https://www.machinehack.com/course/predict-the-flight-ticket-price-hackathon/
In this project I have implemented 14 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.
The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue which eventually leads toward the absence of an employee and provide a proper solution to reduce the absenteeism
Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.
Data science project on Housing Prices Dataset regression analysis
Regression Machine Learning Project
This is an exoplanet classifier for the final project of the AIN212 data science course. Publicly accessible data from NASA's exoplanet archive was used in the training and testing of this classification model.
Book recommender api written in flask framework
Predictive Analysis using a Comparison of Three Machine Learning Algorithms (KNN, Random Forest, and Boosting Algorithm) to Predict Home Selling Prices.
A collection of machine learning models for predicting laptop prices
The aim of this work is to predict number of instagram likes. The text vectorization is done using TF-IDF Vectorizer.
Making cancer classification with knn module (Kaggle Expression)
Leverage machine learning techiques to predict Capital Bikesahre demand
Predict Bike🚲 Rentals with Weather! This project uses regression to predict hourly bike rentals by combining historical usage data with weather information. It helps bike-sharing companies optimize resources and improve user experience.
BBM409 Machine Learning Laboratory - Assignment 1 : KNN Classification and KNN Regression using k-Fold cross validation (OOP design for classifiers)