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Investigated factors affecting likelihood of donations being made using real census data. Developed naive classifier to compare testing results. Trained & tested several supervised machine learning models on preprocessed census data to predict donation likelihood. Selected best model based on accuracy, modified F-score metric, & algo efficiency.
This repository consists of all the projects carried out by me during the period of Data scientist Nanodegree
Finding out the donors who earn more than $50K annually using Machine Learning
Data Science project using Census Income dataset. (Kaggle Competition)
A ML notebook that explores census data to determine whom is more likely to donate.
Apply supervised machine learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause
Build an algorithm to best identify potential donors of CharityML
Udacity机器学习进阶,为CharityML寻找捐献者
Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause
Udacity Machine Learning Engineer Nanodegree Supervised Learning Project: Finding Donors for CharityML
The Project for Udacity Machine Learning nanodegree - the supervised learning section - for a charity non-profit organization to detect the most possible donor individuals based on salary or annual income.
My submission for udacity nanodegree finding-donors project
Charity donor prediction
Machine Learning Engineer Nanodegree -Supervised Learning -Project: Finding Donors for CharityML
Finding donors using supervised learning
Project-1 of Udacity's Introduction to Machine Learning with TensorFlow Nanodegree. "Finding Donors for CharityML" is a Supervised Learning Project with Scikit-learn that aims to build a model that accurately predicts whether an individual earns more than $50,000
Udacity Data Science Nanodegree program, supervised learning, finding donors project
Supervised Learning: Finding Donors for CharityML
Udacity Nano degree program 1st project
Created AdaBoost Model to predict whether the income is <=50K or >50K of an individual. In order to find potential donors.
Machine Learning Nanodegree Project Udacity. Investigated factors affecting likelihood of donations being made using real census data. Developed naive classifier to compare testing results. Trained & tested several supervised machine learning models on preprocessed census data to predict donation likelihood. Selected best model based on accuracy...
Machine Learning Engineer Nanodegree, Supervised Learning, Finding Donors for CharityML
First project of Data science nanodegree (Supervised learning)
Udacity Machine Learning Engineer Nanodegree, Supervised learning project (Oct 2018)
In this project I use classification models to predict potential donors given a set of demographic factors.
Finding Donor for CharityML - Machine Learning Nanodegree from Udacity
Employing several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census to construct a model that accurately predicts whether an individual makes more than $50,000, This sort of task can arise in a non-profit setting, where organizations survive on donations.
Finding Donors for CharityML using supervised learners.
ML Project done in Udacity Course
Find Donors for CharityML for Udacity Machine Learning Engineer Nanodegree
Finding Charity Donors -- Udacity MLND
Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.
Machine Learning Nanodegree Project Udacity. Investigated factors affecting likelihood of donations being made using real census data. Developed naive classifier to compare testing results. Trained & tested several supervised machine learning models on preprocessed census data to predict donation likelihood. Selected best model based on accuracy, modified F-score metric, & algo efficiency.
Created AdaBoost Model to predict the whether the income is <=50K or >50K of an individual. In order to find potential donors.