This Repository contains the implementation of various Classification Algorithms on different different datasets.

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

A visual approach to understand the beauty of voting classifiers

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.

Aulas do curso Dominando Data Science da Flai.

A novel ML-based binary classifier to tell viral and non-viral long reads apart in metagenomic samples.

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

Simple Codes

Final project of the Data Visualization course, Ariel university.

Iris Species Classification usin various ML models.

Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department