Udacity 2018 Machine Learning Nanodegree Capstone project

This is the final project for the Udacity Machine Learning Nanodegree: Predicting article retweets and likes based on the title using Machine Learning

Stock Price Prediction using Regressions with Fast Fourier Transform (FFT) - Machine Learning Nanodegree capstone project (2017)

Peer-Graded Assignment: Capstone Project Notebook | Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.

My machine learning capstone projects that predicts rental rates and property values in San Francisco using two datasets (Airbnb and SF Assessor data). I incorporate publicly available geospatial data to improve the accuracy of the models.

My Graduate Capstone Project - This is a Product Recommendation System for a Local Wholesaler in India, using Python and Machine Learning

This repository contains the project files for the Capstone Project - Cardiac Arrhythmia ML as part of Udacity's Machine Learning Nanodegree.

The source code of machine learning model's API of Travens smart guide in order to complete Bangkit Capstone Project

Predicting Global Supply Chain Outcomes for Essential HIV Medicines using Machine Learning Techniques. Capstone original project for Udacity Machine Learning Engineer Nanodegree.

Implemetation of ML models for predticting used car prices

Capstone Project: Using machine learning to predict the probability of default of credit card clients.

🍏 Capstone Project of MSIB Dicoding 2022 Cycle 3. We plan to build a machine learning model to predict fresh fruit. That way, users are expected to be able to easily separate between fresh and rotten fruit.

Capstone Project for Udacity Machine Learning Nanodegree

GitHub Repositories of Bangkit Academy 2021 Capstone Project from 3 learning path. Machine Learning, Mobile development, Cloud Computing

Galvanize capstone project for classifying human activity of daily living using uci machine learning smartphones data set.

Insurance Claim Prediction using Machine Learning - Udacity Nanodegree Capstone Project

Capstone Project for the Machine Learning Nanodegree. Used Deep Q Learning to create an agent that trade stocks.

*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.

Source codes and datasets used for the undergraduate capstone project entitled "Machine Learning Algorithms for the Detection of GPS Spoofing in Intelligent Transportation Systems"

Capstone Project SIB Dicoding Batch 3 2022. Flask x Replit Website with Machine Learning Features (Multiclass Classification & CNN)

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

This is a capstone project associated with MLOps Zoomcamp. The end goal of the project is to build an end-to-end machine learning project containing feature engineering, training, validation, tracking, modeel deployment, hosting, and general engineering best practices aimed at making house price predictions.

Capstone project for Udacity's Machine Learning for Engineers

This is the Capstone Project of Udacity Machine Learning Nanodegree.

A Machine Learning project for retail data analytics as part of the Machine Learning Engineering Nanodegree Capstone Project from Udacity

WQU capstone project - short term currency trading strategy utilizing machine learning

Capstone project for my Udacity Machine Learning Nanodegree

The project aims to profile stocks with similar weekly percentage returns using K-Means Clustering. The project calculates realized volatility for each stock and predicts realized volatility for each stock using classical volatility models and machine learning models and comparing their performance. This is a capstone project for CIVE 7100 Time Series and Geospatial Data Sciences.

My capstone project explores machine learning, hardware, and web development to create a smart home system for monitoring the health of homebound patients suffering from sleep apnea. The system includes data collection through sensors, embedded ML (TinyML) to analyze data, and web development for creating a medical dashboard.

Udacity Machine Learning Engineer Nanodegree - Capstone Project