Performance comparison of classification algorithms
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Updated
Jun 29, 2019 - Python
Performance comparison of classification algorithms
Data warehouse and analytics project to predict bike theft prediction from TPS data
We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.
Scoring model for financial company - all files
Routines to perform cross-validation and nested cross-validation using data transformations
demonstrate different models such as Variational Autoencoders and GANs in a variety of datasets, including tabular, text and image data, including the generation of synthetic data for comparison of their effectiveness in all models for each kind of dataset
Awarded 1st position in 'Google-Krenicki Hackathon' for cloud based ML framework to predict US household income for real-estate brokerage firm; using Google Colab and AWS S3
Scripts developed for the paper "Understanding when SMOTE works", developed in the "Knowledge Extraction and Machine Learning" (ECAC) class.
Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.
A telemarketing model to predict campaign subscriptions in a portuguese bank institution.
Fraud Machine Learning Pipelining for experimenting with SMOTE
Continuing with telemarketing model to predict campaign subscriptions in a portuguese bank institution. For this project I have evaluated the performance of four resampling techniques and selected the best one to implement the logistic model.
Text classification with scikit-learn, used to make predictions for Kaggle Spooky Author Identification competition
Malicious URL detector built with deep exploration on feature engineering.
Model with Dimensionality Reduction with performing SMOTE and Tuning will get comparable results comparing with off-the-shelf models in Sentiment Analysis of Citations (Athar 2011).
Implementation of novel oversampling algorithms.
Synthetic Minority Over-sampling Technique Implementation
Data Science Case Study
A minority oversampling method for imbalance data set
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