Problem Sheet 3 for Emerging Technologies.

My understandings of the tensorflow on the expedition to learn it!

Our objective is try to classify the class variable (species) with help of length and width of sepal, petals given.

Sklearn model serving with Flask api

Loaded the iris dataset in Python using a Pandas data frame.Performed a PCA using Scikit Decomposition component.Plotted the Principal Components to recreate the scatterplot for each flower type

A simple model to classify the iris dataset with keras.

Data Visualisation using Iris Dataset

Prediction using Unsupervised Machine Learning

A beginner's models for the classification of Iris ,A study based jupyter notebook

Data analytics of palmerpenguins dataset using python language.

This project is a user Interface (Tkinter), that's a demonstration of the skills I acquired in the CS50AI course offered by HarvardX, with a focus on TensorFlow technology. It uses a database to describe the length and width of three species of iris (Iris Setosa, Iris Virginica and Iris Versicolor) which are found mainly in the United States

A simple project to train and evaluate a multilayer perceptron on the Iris Sepecies data using TensorFlow, SciKeras, and Scikit-Learn.

Implemented Decision Tree on iris dataset

A simple linear perceptron capable of determining the type of iris between versicolor and setosa by analyzing its sepal and petal length

This project classifies Iris flowers into three species—Setosa, Versicolor, and Virginica—using machine learning algorithms. It analyzes petal and sepal measurements to predict the species with high accuracy.

An end-to-end data analysis and machine learning project covering data collection, database setup, comprehensive analysis, dashboarding, and model building using the Iris dataset.

pandas, sklearn, seaborn, matplotlib

A basic implementation of the Random Forest Classifier from Scratch and using Seaborn to find important features.

Classification of iris species