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)
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Updated
Apr 9, 2019 - Python
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)
A recommendation system based on Artificial Intelligence to predict best-fit color palettes according to user input
Implementation of Regression Models on Navigation with IMUs.
Predict NYC taxi travel times (Kaggle competition)
My exercises in the machine learning course
My solutions to projects given in the Udemy course: Python for Data Science and Machine Learning Bootcamp by Jose Portilla
sklearn, tensorflow, random-forest, adaboost, decision-tress, polynomial-regression, g-boost, knn, extratrees, svr, ridge, bayesian-ridge
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.…
A LibreOffice Calc extension that fills missing data using machine learning techniques
Boston house price prediction.
Assignments of the ML Course at IIT Gandhinagar
asthma-rates.com - predict asthma rates after changes in social policy - Data Science Capstone Project
This repository contains projects related to KNN algorithm using R, Python
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.
A k-nearest neighbors algorithm is implemented in Python from scratch to perform a classification or regression analysis.
Transfer Learning Image Classifier knn image tensorflow js
Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.
Machine Learning engine generates predictions given any dataset using regression
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.
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