Python implementation of naive bayes from scratch and using scikit-learn

This project detects spam messages in SMS, including those written in regional languages typed in English. It uses an extended SMS dataset and applies the Monte Carlo method with various supervised learning algorithms to improve spam detection.

Breast Cancer Data Analysis: Analyzes and classifies breast cancer data using a Naive Bayes classifier with preprocessing, label encoding, and k-fold cross-validation. Cars Dataset Analysis: Explores a cars dataset with data loading, statistics, and visualizations, including price distribution and correlation heatmap. Hayes-Roth Classification: C

Navie Base Classifier for classifiing imdb comments

Classifying emails into custom user labels

Machine learning project to predict fetal heart health outcomes from CTG exam data.

Group project for the 4 assignments of IS661-Text-Analytics at the University of Mannheim

Disease Prediction Model using SVM, GaussianNB and Random Forest Classifiers.

Sentiment analysis on the IMDb dataset through a custom multivariate Bernoulli Naive Bayes implementation and a rudimentary BiGRU RNN.

Componente central de processamento de linguagem natural, voltado para aplicações de aprendizado de máquina.

Culled from the UCI Machine Learning Repository, the Dry Bean Dataset (licensed under CC BY 4.0) provides valuable insights into bean classification and is a valuable resource for machine learning enthusiasts.

Analyzing tweets from Twitter and classifying them into trolls using Natural Language Processing

This Repository contains Classifer Program in a Python File as well as Jupyter Notebook to Recognize Spam Emails.

Machine Learning, EDA, Classification tasks, Regression tasks for customer churn

Machine Learning Algorithms Practicals in Python with Datasets

In this study we seek to predict employee attrition with KNN clustering and Naive Bayes, and to predict employee salary using multiple linear regression

This repository contains the coding projects that I completed for the Masters program in Data Analytics at Western Governors University.

The Student Success Predictor employs Naive Bayes to assess the likelihood of students achieving scores above 90, integrating study hours and personal factors. This model aids educators in identifying key elements influencing academic excellence, facilitating targeted interventions for enhanced student success. Contributions Welcome!!

This repository contains the codebase and resources for a machine learning-based project aimed at predicting loan eligibility for individuals. The project utilizes various algorithms and data preprocessing techniques to build predictive models that assess the likelihood of an applicant being eligible for a loan based on historical data.

Machine Learning projects