linearsvm

用sklearn的linearSVC和SVC做简单数据的预测对比

Identify which questions asked on QUORA are duplicated on question that have already been asked. Used performance metric as log loss and trained the model using XGBOOST that brought my log loss to 0.3157.

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

The major aim of this project is to predict which of the customers will have their loan paid or not. Therefore, this is a supervised classification problem to be trained with different algorithms.

Sentiment Analysis system is a NLP-ML system,which was a supervised learning system.

Machine Learning Linear SVM and Deep Learning LSTM model to classify customer complaints into different categories

Implementation of Linear Support Vector Machine Algorithm

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

Suggest the tags based on the content that was there in the question posted on Stack Overflow. Used performance metric as micro F1 score and trained models by using One VS Rest Logistic Regression and Linear SVM

Đồ án vấn đáp môn Máy học Phân lớp ảnh chữ số viết tay bằng SVM

📧🤖 SmartMail — ML-Based Email Spam Detection & Automated Triage uses Python, TF-IDF, and machine learning to classify messages as spam or ham. 📊 Compares three ml algorithms, achieving 98.26% accuracy with Linear SVM, and automatically routes emails into Inbox, Review, or Spam based on calibrated spam probabilities. 🚀

Machine Learning Projects from Coursework

A beginner-friendly Machine Learning project that analyzes customer feedback and predicts sentiment (Positive, Neutral, Negative) using TF-IDF and classical ML models.

Built a fake news classification system using NLP and ML on a Kaggle dataset of 40,000+ articles. Preprocessed text, applied TF-IDF, and trained Naive Bayes, Logistic Regression, and Linear SVM. Achieved 99.3% accuracy with Linear SVM. Includes evaluation and visualizations.

This project predicts TripAdvisor hotel review ratings (1–5 stars) using Natural Language Processing and Machine Learning. After preprocessing the review text and applying TF-IDF / CountVectorizer, models such as Logistic Regression, Linear SVM, and Naive Bayes were trained to classify ratings. The dataset contains 20,491 reviews.

Binary classification problem to predict whether a pair of questions are duplicates or not.

Data Mining Algorithms and Implementation of libraries.

The comparison between different embeddings (TF-IDF, USE, and TF-IDF + USE) and various classifiers provides valuable insights into the performance of different techniques for sentiment classification.

Enterprise Knowledge Platform using Retrieval-Augmented Generation (RAG), Machine Learning, LangChain, ChromaDB, and local LLMs.