A research project of anomaly detection on dataset IoT-23

The cancer like lung, prostrate, and colorectal cancers contribute up to 45% of cancer deaths. So it is very important to detect or predict before it reaches to serious stages. If cancer predicted in its early stages, then it helps to save the lives. Statistical methods are generally used for classification of risks of cancer i.e. high risk or low risk. Sometime it becomes difficult to handle the complex interactions of highdimensional data. Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.I am using algorithms like Naive Bayes, decision tree

Heart disease prediction and Kidney disease prediction. The whole code is built on different Machine learning techniques and built on website using Django

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

We took an iris dataset and trained with different classifiers to find out their accuracy and some parameters.

This project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.

Welcome to the "SMS Spam Detector" project! This machine learning model identifies whether a given SMS is spam or not, providing a valuable tool for spam detection and filtering.

A Flask based production level web app which uses Naive Bayes classifier to predict given SMS is spam or ham. Also contains jupyter notebook with basic data exploration and ml modelling.

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

The objective is to analyze voter behavior based on demographic and opinion-based variables and build a classification model that can predict which party a voter will vote for. This model is used to simulate an exit poll.

Indian English News (2023) Analysis and Classification: Categorize news articles with class labels like entertainment, social, sports, national, etc. Achieved 83% accuracy. Interactively predict categories from headlines. Contributions welcome!

A Model Built Using Kaggle Dataset & Machine Learning Classification Algorithms such as Logistics Regression,K-NN, Naive Bayes, SVM, Decision Tree & Random forest which Predicts chances of heart disease in a person.

Movie genre classification in NLP using multinomial navie bayes classification and linear support vector classification.

Application of machine learning model, on datasets, to predict desired target variables.

Multi-class classification of news articles using NLP techniques, TF-IDF, and Naive Bayes

Detect email phising use Navie Bayes, RF, SVM, ANN and Decicion Tree. Dataset use Enron email.

Spambase dataset analysis comparing Naïve Bayes classifiers. Evaluated accuracy, confusion matrices on different splits. Explored alternatives for improved performance in ML course, uOttawa 2023.

This repository consists of various projects based on Machine Learning and NLP.

No description provided.

spam/ham classifier

Link Analysis, Naive Bayes Text Classifier, Marathi Stemmer

The university assignment that implements models to predict weather Pokémon is legendary or not.

A machine learning-based fake news detection system that classifies news articles as "FAKE" or "REAL" using Naive Bayes and Support Vector Machine (SVM) models. The project features a text preprocessing pipeline, model evaluation, and prediction capabilities, demonstrating practical accuracy and efficiency for real-world news verification.

The project focuses on sentiment analysis of Coronavirus tweets NLP - Text Classification kaggle dataset

This project performs Sentiment Analysis on a small IMDb dataset (positive, negative, neutral reviews). It uses a simple Naive Bayes model with text preprocessing and vectorization.

Text and Sentiment analysis for Lenovo k9 product reviews from Amazon website.

NTI final Trianing project

📱 Detect spam SMS in real-time using machine learning with multiple models for effective filtering in cellular networks.

Labs for ECS763 Natural Language Processing

Can we get the same accuracy for Tamil as English?