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)
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
Predict NYC taxi travel times (Kaggle competition)
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.…
asthma-rates.com - predict asthma rates after changes in social policy - Data Science Capstone Project
A LibreOffice Calc extension that fills missing data using machine learning techniques
Boston house price prediction.
Assignments of the ML Course at IIT Gandhinagar
A k-nearest neighbors algorithm is implemented in Python from scratch to perform a classification or regression analysis.
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.
Transfer Learning Image Classifier knn image tensorflow js
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.
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/
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