Contains 5th Semester AIML Lab programs

A collection of data science concepts, datasets, industry-applications, walk-through's & notes. See ISSUES tab for in-depth studies by topics.

A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)

Naïve Bayes Algorithm is implemented from scratch in order to classify spam and not spam emails.

Aims to build and test classification models to predict salaries from the text contained in the job description.

End-to-end implementation and deployment of Machine Learning Restaurant Reviews Sentiment Analysis using python, flask, gunicorn, scikit-Learn, nltk, etc. on the Heroku web application platform.

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.

Code templates for data prep and different ML algorithms in Python.

Predicting whether an SMS (text message) is spam using natural language processing (NLP), naive Bayes classifier and cross validation (in Python)

Native_Bayes_Sentimientos Básico

This is a simple python program to train a classification model using decision tree, random forest and Naive Bayes algorithms

The aim of the iris flower classification is to predict flowers based on their specific features.

Sentiment analysis with spark

WEB AND SOCIAL MEDIA ANALYSIS

Models to detect real pictures from fake photoshoped pictures

Implementation of natural language processing, supervised and unsupervised machine learning methods for classifying events around the US to automate a travel start-up's recommendation pipeline. Includes interactive command line tool.

Interactive Streamlit Visualisation for a Naive Bayes Classfier