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The ability to predict prices and features affecting the appraisal of property can be a powerful tool in such a cash intensive market for a lessor. Additionally, a predictor that forecasts the number of reviews a specific listing will get may be helpful in examining elements that affect a property's popularity.
In this project, I will be implementing Principal Component Analysis (PCA) from scratch on an ecological footprint consummation database for countries and a three-dimensional scale using a movie database. The goal of this project is to gain a deeper understanding of PCA and to demonstrate its capabilities in exploring complex datasets.
Explore facial recognition through an advanced Python implementation featuring Linear Discriminant Analysis (LDA). This repository provides a comprehensive resource, including algorithmic steps, specific ROI code and thorough testing segments, offering professionals a robust framework for mastering and applying LDA in real-world scenarios.
The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil. The objective here is to predict for each new individual, whether he is going to be absent for more than 3 hours or no (3 hours is the median for the absenteeism hours).
Uses K-Means unsupervised machine learning algorithm and Principal Component Analysis to cluster cryptocurrencies based on performance in selected periods.
In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.