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A research project of anomaly detection on dataset IoT-23
Heart disease prediction and Kidney disease prediction. The whole code is built on different Machine learning techniques and built on website using Django
We took an iris dataset and trained with different classifiers to find out their accuracy and some parameters.
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.
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.
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!
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.
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
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.
Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees
The university assignment that implements models to predict weather Pokémon is legendary or not.
This repository consists of various projects based on Machine Learning and NLP.
Application of machine learning model, on datasets, to predict desired target variables.
Detect email phising use Navie Bayes, RF, SVM, ANN and Decicion Tree. Dataset use Enron email.
Text and Sentiment analysis for Lenovo k9 product reviews from Amazon website.
The project focuses on sentiment analysis of Coronavirus tweets NLP - Text Classification kaggle dataset
Link Analysis, Naive Bayes Text Classifier, Marathi Stemmer
Labs for ECS763 Natural Language Processing
Can we get the same accuracy for Tamil as English?
Spambase dataset analysis comparing Naïve Bayes classifiers. Evaluated accuracy, confusion matrices on different splits. Explored alternatives for improved performance in ML course, uOttawa 2023.
Developed an NLP system using Gradio and Hugging Face to classify disaster tweets with both machine learning (ML) and deep learning (DL) models.
spam/ham classifier