Automatic Fruit Classifier Using Supervised AdaBoost Machine Learning Algorithm
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Updated
May 2, 2023 - HTML
Automatic Fruit Classifier Using Supervised AdaBoost Machine Learning Algorithm
Finding Donors for Charity - 3rd project for Udacity's Machine Learning Nanodegree
Predictive Machine Learning projects
A quick reference guide for creating prediction models using R caret.
In this project i used many supervised learning algorithms available in scikit-learn, and also provided a method of evaluating, just how each model works and performs on a certain type of data.
This project combines microplastic detection through computer vision with a machine learning-based water potability prediction tool, using AdaBoost for accurate results.
Model for easy facilitation of visa processing and approvals
This dataset from "ShufersalML" captures customer order history, aiming to predict future purchases using Python. It involves interconnected files that detail customer orders over time. The goal is to build a predictive model leveraging past order patterns to anticipate which products a user is likely to include in their next order.
Classification problem using Ensemble Techniques
Investigate personnel elements influencing organizational dynamics by looking at HR analytics data using python and advanced machine learning models. Forecast employment status, estimate the period of termination, and maximize performance and satisfaction initiatives.
The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.
Based on the powerful econometrics and statistical background and rich data science resources of School of Economics (SOE) and Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, WISER CLUB is a data science mutual aid learning organization jointly organized by SOE and WISE graduate students and undergraduate students.
Implementation of the AdaBoost algorithm for UCI's Tic-Tac-Toe Endgame dataset.
6th Project for the Post Graduate Programme in Data Science and Business Analytics at the University of Texas at Austin - Model Tuning (GridSearchCV & RandomizedSearchCV)
In this project I use classification models to predict potential donors given a set of demographic factors.
Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.
In the project we built the following algorithms : Decision Tree Classifiier and Regressor, AdaBoost Classifiier , Gradient Boost Regressor
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