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 l…

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.

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.

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.

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.

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

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

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

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!

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

❤️ Predict heart disease risk using classic machine learning techniques with this Jupyter notebook project, featuring data exploration and model building.

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.

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.

spam/ham classifier

Link Analysis, Naive Bayes Text Classifier, Marathi Stemmer