Python code implementation for Iris dataset classification using Chat GPT

Hello and welcome to this short video where we will demonstrate how to use Python code and ChatGPT to perform classification on the Iris dataset.

The Iris dataset is a popular dataset used in machine learning and consists of 150 instances with four features: sepal length, sepal width, petal length, and petal width. Our goal is to classify the instances into three different classes based on their features.

To perform this classification, we will be using ChatGPT, a large language model trained by OpenAI that can be used for a variety of natural language processing tasks.

First, we will import the necessary libraries such as pandas, scikit-learn, and transformers. Then, we will load the Iris dataset using pandas and split it into training and testing sets.

Next, we will use transformers to fine-tune ChatGPT for classification on the Iris dataset. We will use the pre-trained model for language modeling and then fine-tune it on the training set. After training, we will evaluate the model on the testing set.

Finally, we will use the model to make predictions on new instances by providing their features as input. The model will output the class that the new instance belongs to.

With ChatGPT, we can perform classification on the Iris dataset using natural language processing techniques, providing a unique and powerful way to analyze and classify data. Thank you for watching this video, and we hope you find this demonstration helpful.

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