notebooks Google Colab

This is practice notebook for Naive Bayes Classification on Iris Data Set.

In this notebook we'll see how to use KNN to classify the IRIS Flowers.

This notebook focuses on the classification of Iris Species by its Sepal Length, Sepal Width, Petal Length and Petal Width.

This project focuses on classifying Iris flowers into different species using machine learning techniques. The classification model is built using Jupyter Notebook and deployed using Streamlit.

A machine learning project for classifying iris flowers into three species using sepal and petal characteristics. The repository includes EDA, advanced data visualization, and model evaluation, achieving 100% accuracy. Explore data, models, and visualizations in the notebook, images, and data folders.

This repository contains two machine learning projects focused on different aspects of predictive modeling. Each project is implemented using linear regression and logistic regression and includes Jupyter notebook code for easy understanding and reproduction.

This repository contains four machine learning projects developed during college, covering email spam detection, emotion classification in Urdu, voice classification, and iris recognition. Each project includes data preprocessing, feature extraction, model training, and evaluation. Explore the Jupyter notebooks for detailed implementations.

This is a web application that classifies iris flowers based on their sepal length, sepal width, petal length, and petal width. The app is built using Streamlit and the classification model is trained using Jupyter notebook.

This is my first task by "Oasis Infobyte". They provided me with an excellent opportunity to learn and demonstrate my abilities. This is a basic logistic Regression to classify the iris flower data. . Task-1 Used - Python (Jupyter Notebook)