1st Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
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Updated
Dec 23, 2019 - Python
1st Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
This repository contains an ML project that was approached with a business mindset from the beginning to the end. It addresses the problem of regression.
deciphering car VIN codes using regular expressions
Regression and Classification task with sklearn.
Regression & Classification Machine Learning Models predict the weather
BME499 course materials. I developed the neural networks / deep learning section of this course.
peramalan jumlah penduduk kec.kandangserang kab. pekalongan dengan metode regresi linier. data training jumlah penduduk dengan gender wanita pada tahun 2011-2021
Esercizi e piccoli Progetti di applicazione all'Intelligenza Artificiale utilizzando GraphLab Create e Python
Deep Learning Projects Using Keras & Scikit-Learn
Solving exercises of Introduction to Statistical Learning with Python
An exploratory data analysis is performed and a regression model is used to predict house values. The prediction performance is optimized after tuning the model hyper-parameters to minimize bias/variance errors.
Analyzing a huge dataset taken by the Department of Education, utilzing both SAS, and MS Excel (https://catalog.data.gov/dataset/college-scorecard)
Analyze A/B Test Results - Udacity Data Analyst Nanodegree Project
This repo contains all my implementations of Machine Learning Models.
Predictive Modeling Part 1: Home Prices in Philadelphia
Cancer Classification Using Gene Expression Data with the use of different Regression ML based models.
Historical weather data from Basel is used to predict the amount of Short wave radiation on a hourly basis. The data covers 1863 days from 07/2013 to 07/2018. The model is tested on data from 07/2012 to 07/2013.
A machine learning project for predicting laptop prices using Gradient Boosting and Random Forest regressors.
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