Daily Tasks Efficiently Managed by ChatGPT for Data Specialists
ChatGPT, a cutting-edge AI model, is revolutionising the data science landscape by automating routine tasks, allowing professionals to focus more on analysis and decision-making. In this article, we explore five tasks that ChatGPT can handle in a data project, including data cleaning, exploratory data analysis (EDA), data transformation, model building, and model evaluation and reporting.
A practical example of ChatGPT's capabilities is demonstrated through a data project analysing failed ride orders from Gett. This project involves using ChatGPT to clean and preprocess the dataset, handle missing values, and prepare the data for analysis.
With ChatGPT's assistance, exploratory analysis is conducted to identify patterns in why rides failed, such as location and timing. The findings are visualised through various trends, including failure rates by time of day or region.
Furthermore, predictive models are built with ChatGPT's help to predict when a ride may fail based on input features. To make the analysis accessible, an interactive Streamlit dashboard is created, integrating the entire workflow from cleaning to modelling and visualisation with a single click.
The Streamlit app, built with the Gemini CLI, covers all steps from basic EDA to applying machine learning models. Each step in the app is displayed in a different tab, providing a seamless user experience.
The next step in the project is to prepare the dataset for machine learning, which involves encoding categorical variables, scaling numerical features, and returning a clean DataFrame ready for modelling. Machine learning evaluation metrics like accuracy, precision, recall, and F1-score are being reported.
The Gemini CLI, an open-source agent, can be used for routine data science tasks beyond ChatGPT, including data cleaning, exploration, and dashboard automation. It provides a command-line interface and is available at no cost.
The data science report by Anaconda states that data scientists spend nearly 60% of their time on cleaning and organizing data. By automating these time-consuming tasks, ChatGPT and the Gemini CLI are helping data scientists save valuable time and resources.
In conclusion, this practical implementation showcases how ChatGPT and the Gemini CLI can streamline the full cycle of a data science project, particularly focusing on routine but essential steps in data handling and preliminary model deployment.
- ChatGPT, an advanced AI model, is streamlining the data science landscape by automating tasks, enabling professionals to delve deeper into insights.
- In the Gett ride order data project, ChatGPT assists in cleaning and preprocessing datasets and handling missing values for better analysis.
- Through exploratory analysis with ChatGPT, trends such as failure rates by time of day or region in ride orders are uncovered.
- Predictive models are crafted with ChatGPT's help, using various inputs to predict the likelihood of ride failures.
- An interactive Streamlit dashboard built with ChatGPT and the Gemini CLI demonstrates the entire workflow, from data cleaning to model visualization, with a single click.
- The Streamlit app, developed with the Gemini CLI, spans tasks from basic exploratory data analysis (EDA) to machine learning model application.
- The Gemini CLI, an open-source tool, offers additional benefits beyond ChatGPT's capabilities, including data cleaning, exploration, and dashboard automation.
- Anaconda's data science report reveals that data scientists dedicate nearly 60% of their time to cleaning and organizing data, tasks which ChatGPT and the Gemini CLI automate to save time and resources.
- This practical application highlights how ChatGPT and the Gemini CLI can simplify the entire data science project life cycle, concentrating on essential, yet routine steps like data handling and initial model deployment.
