Intrusion detection using SVM,KNN,RF and then creating a ensemble model by using voting classifier and showing the results in a graph. Confusion matrix for the SVM,KNN,RF are show which shows the value . IT is done Using the supervised Machine Learning algorithms

Building spam classifier using different algorithms and choosing the best one to build a streamlit application

This Project Predicts whether the Email/SMS is spam or ham by using the extensive knowledge of NLP and various ML Algorithms. Deployed on Streamlit & Herokuapp

This repository demonstrates the project dissertation topic at Kingston University (London)

A visual approach to understand the beauty of voting classifiers

Aulas do curso Dominando Data Science da Flai.

This project uses ensemble method models of decision trees, voting classifier, support vector machines, adaboost, logistic regression, dummy classifier, and bagging classifier to predict malignant or benign cells for breast cancer.

This is a Group Project focused on using The CCES Congressional polling dataset, to predict which party a Californian voter will vote for.

This repository contains a Jupyter Notebook that uses machine learning algorithms to predict whether a customer is likely to make a claim on their car insurance policy in the next 6 months.

Ensemble Learning Techniques - Breast Cancer Classification

Showcasing data science skills for a dataset provided by State Farm for a coding interview.

Kaggle Playground Series - Season 3, Episode 26 - Multi-Class Cirrhosis | EDA | MI-Score | Feature Engineering

Descriptive, predictive analysis of taxability

A simple binary email classifier that classifies emails into spam and ham and also performs topic modelling using LDA

Final project of the Data Visualization course, Ariel university.

Explore various classifier to predict which employee is likely to quit

We built a model for Film Junky Union to detect negative reviews.

A model build on RAVDESS dataset, for speech emotion recognition. 85.59% validation accuracy

This project focuses on predicting the likelihood of diabetes in individuals using ensemble machine learning models. It combines various ensemble techniques, including Random Forest, AdaBoost, Gradient Boosting, Bagging, Extra Trees, XGBoost, Voting Classifier and some others to get predictions.