The Iris Flower Classification project is a popular data science project that involves the classification of Iris flowers based on their various attributes. The dataset used in this project contains measurements of four features: sepal length, sepal width, petal length, and petal width, for three different species of Iris flowers: setosa, versicolor, and virginica. The goal of the project is to build a machine learning model that can accurately classify an Iris flower based on its feature measurements. This involves training the model with the provided dataset, which includes labeled examples of Iris flowers, and then using the trained model to predict the species of new, unseen Iris flowers.
The Car Price Prediction data science project involves building a model to predict the price of used cars based on various features and attributes. The dataset used in this project typically includes information such as the car's brand, model, year of manufacture, mileage, fuel type, transmission type, and other relevant factors. The goal of this project is to develop a machine learning model that can accurately estimate the price of a car based on its features. This involves training the model using historical data with labeled car prices and then using the trained model to predict the price of new or unseen cars.