Classification Models
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Updated
May 24, 2020 - Jupyter Notebook
Classification Models
Implement an algorithm that can classify handwritten digits, based on MNIST database.
analyzing data, performing visualization, and training five different machine learning models, including two ensemble models
Detecting Fake Job Postings - Data Visualization, TF-IDF, XGBoost, SVC
Predicting the likelihood of candidates joining the company or not
Helps to work with projects repositories and SVC operations.
ML algorithms in Python
Driver codes in C for RTOS development on STM32F411VETx
SVM
Performed Sentiment Analysis on the Twitter Us Airline dataset
Analysis of Support Vector Machine
ECE NTUA Neural Networks
Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.
Machine Learning - Classification
Utilizing machine learning models including logistic regression, random forest, gradient boosting, and neural networks to identify fraudulent credit card transactions. Dataset, consisting of PCA-transformed features and unbalanced classes, required precision-recall metrics for accurate evaluation. Developed in Python using TensorFlow and scikit.
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