Finding donors using supervised learning
-
Updated
Sep 29, 2019 - Jupyter Notebook
Finding donors using supervised learning
Spring 2021 - Automation of Scientific Research - course project
Boston Crime Analisys test.
Gradient Boosting Classifier on the Titanic dataset
I applied supervised machine learning models (Decision Trees, Gradient Boosting, Support Vector Machines) on data collected for the US census to help CharityML (a fictitious charity organization) identify/predict people most likely to donate to their cause.
Predicting mammalian taxonomic order based on ecological, geographic, and life-history traits
Fake News Detection Engine using Natural Language Processing and Machine Learning
Detect Fraudulent Credit Card transactions using different Machine Learning models
This project uses machine learning to predict whether a loan applicant will repay their loan. The project uses a dataset of historical loan data from PeerLoanKart, a peer-to-peer lending platform.
Finding Donors for CharityML using Gradient Boosting Classifier, Ada Boost Classifier and Logistic Regression
Udacity DataScience nanodegree classification problem
Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.
The objective of this project is to develop a machine learning model that can predict the risk of Cardiovascular diseases (CVDs) in individuals based on their health data.
Open source gradient boosting library
This project uses machine learning to predict whether a loan applicant will repay their loan. The project uses a dataset of historical loan data from PeerLoanKart, a peer-to-peer lending platform.
Repository for SLC projects
This project uses machine learning to classify breast cancer tumors as malignant or benign using the Breast Cancer Wisconsin (Diagnostic) Dataset.
This project applies Gradient Boosting to predict the outcome of Kickstarter campaigns and uses K-means Clustering to uncover project trends, providing deeper insights into their distinct features.
Predicting Annual Income from Census Data: A Binary Classification Analysis using various ML Techniques
Add a description, image, and links to the gradient-boosting-classifier topic page so that developers can more easily learn about it.
To associate your repository with the gradient-boosting-classifier topic, visit your repo's landing page and select "manage topics."