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

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

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.

Learns using the input from the testing_data how to compute the output

Implementação do algoritmo KNN utilizando numpy para classificação no conhecido dataset iris.

Explore the Iris flower dataset using numerous machine learning methods and tools

Built and implemented the Decision Tree successfully with split on the basis of information gain from scratch