Making Prediction from fivethirtyeight 2016 Election Poll Data
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Updated
Sep 14, 2023
Making Prediction from fivethirtyeight 2016 Election Poll Data
Collection of notebooks performing analysis of data and visualisations of outputs for a research paper on evaluation of GHG emissions in Burmese reservoirs
This is a Credit Analysis project developed by Felipe Solares da Silva and is part of his professional portfolio.
Given financial information of a person, this determines, based on past data (through boosted decision trees), whether or not to approve their loan request.
This is the repository for my R project on modeling historical weather data in Santa Barbara.
Codes for reproducing the results of arXiv:2207.04157
A sigmoid SVM classifier for predicting whether an online post is conspiracy or philosophical theory
This is the repository for Jay Shreedhar, Varun Thakur, Tara Gopinath, and Yueyang Pan's final project on diabetes risk classification for DSCI 550 - Data Science At Scale at USC. Check the individual contributor branches for reports on each classification model.
A R script that runs Boosted Regression Trees(BRT) on epochs of land use datasets with random points to model land use changes and predict and determine the main drivers of change
Machine learning prediction project, R studio, 2019.
Following Project is for predicting the list of creditworthy customers for a bank.
A few classifiers - ML (level basic); scikit-learn
These are my notes for the interview prep workshop I led on Random Forests
Multivariate selection of VLQ events
machine learning algorithms realization
Bicycle Rental Demand Forecasting: Using Advanced Techniques with Microsoft Azure Machine Learning
All the code used for my MSc Thesis: Search for Dark Matter using Machine Learning in dilepton and missing energy events with the ATLAS detector at the LHC, A tentative model independent approach
Research based testing of Boosted Tree Classifier for Predicting Disease from Symptoms
Future Ready Talent Project Submission.Using Azure ML Studio to predict the income of individuals, based on their age, race, education, residence city, etc. Used the adult census dataset
Implementation of decision trees for binary categorical data using numpy. Includes regular decision trees, random forest, and boosted trees.
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