Finding donors using supervised learning
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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.
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.
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.
Predicting whether a customer will carry out a transaction or not for Santander group
Supervised learning project using Gradient Boosting Classifier
The Office of Foreign Labor Certification is facing a dramatic increase in work visa applications, but is hampered by a sluggish review system. It needs to improve the process by developing a way to quickly, accurately identify applications likely to be accepted or rejected so their processing may be prioritized.
The model should predict whether is it going to rain the next day coming or it isn't. The models that have been deployed were TensorFlow Sequential, Random Forest Classifier and GradientBoostingClassifier. The best model on both training and test set was achieved with Gradient Boosting Classifier with 95.2% and 85.5% accuracy on the train and test.
TOP13% solution for the Titanic-Kaggle competition using a Gradient Boosting Classifier. Moreover, implementation of a Streamlit App to play with the models.
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