A fast, robust Python library to check for offensive language in strings.
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Updated
Jun 5, 2024 - Python
A fast, robust Python library to check for offensive language in strings.
Machine Learning project - CMP2024 - Computer Engineering - Cairo University
A repository that contains practice code for ML algorithms implementation on a cancer treatment medical dataset from Kaggle (Text Processing, Response Encoding, Feature Engineering, Classification).
Wrapper on top of liblinear-tools
Performed feature selection using F-score method to filter out the important features in order to optimize the performance of Linear SVM machine learning model. The accuracy achieved was above 63%.
This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.
Statistical Pattern Recognition (classic machine learning)
Analisis Sentimen Twitter terhadap Pernikahan di Usia Muda menggunakan Metode Support Vector Machine
Implemented using Linear SVM(SVC) and Non-Linear SVM(RBF). ML ASSIGNMENT 2 => Q2
Semantic Enrichment, Data Augmentation and Deep Learning for Boosting Invoice Text Classification Performance: A Novel Natural Language Processing Strategy
Linear SVM, Quadratic SVM, SVM with RBF Kernel
The project involves building models for scoring & segregating users acccording to certain threshold criteria based on features from LinkedIn on threshold criteria-refer ReadMe.
Prediction of diabetes health indicators for machine learning class final project
FlairifyMe is a Reddit Flair Detector for r/india subreddit, that takes a post's URL as user input and predicts the flair for the post using a model generated by Logistic Regression.
Finding duplicate records using Record Linkage Comparison and BigData through Apache Spark
Amazon product review sentiment analysis using Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) multiclass as classifier models, Synthetic Minority Oversampling Technique (SMOTE) as feature oversampler, and TF-IDF vectorization as feature, Synthetic Minority Oversampling Technique (SMOTE) as oversampler, and k-fold CV.
Iris Species Classification usin various ML models.
Projet de modélisation supervisée - SCORING
K-fold cross-validation implemented from scratch to aid in analysis of the MNIST dataset
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