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.

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

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.

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

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

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

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.

Machine Learning Projects from Coursework

Implemented Perceptron, Logistic Regression, Linear SVM, and SGD with proper scaling and regularization. Compared decision boundaries, training dynamics, and coefficient-based interpretability, and outlined strategies for imbalance and hyperparameter tuning in fast, production-friendly models.

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

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.

Data Mining Algorithms and Implementation of libraries.

Developed an app to predict whether food reviews are positive or negative using linear SVM on amazon fine food review dataset and deployed it on localhost

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.