
ai-data-science-team
An AI-powered data science team of agents to help you perform common data science tasks 10X faster.
Stars: 1671

The AI Data Science Team of Copilots is an AI-powered data science team that uses agents to help users perform common data science tasks 10X faster. It includes agents specializing in data cleaning, preparation, feature engineering, modeling, and interpretation of business problems. The project is a work in progress with new data science agents to be released soon. Disclaimer: This project is for educational purposes only and not intended to replace a company's data science team. No warranties or guarantees are provided, and the creator assumes no liability for financial loss.
README:
An AI-powered data science team of agents to help you perform common data science tasks 10X faster.
Please ⭐ us on GitHub (it takes 2 seconds and means a lot).
Beta - This Python library is under active development. There may be breaking changes that occur until release of 0.1.0.
The AI Data Science Team of Copilots includes Agents that specialize data cleaning, preparation, feature engineering, modeling (machine learning), and interpretation of various business problems like:
- Churn Modeling
- Employee Attrition
- Lead Scoring
- Insurance Risk
- Credit Card Risk
- And more
- Your AI Data Science Team (🪖 An Army Of Agents)
- Want To Become A Full-Stack Generative AI Data Scientist?
- ⭐️ Star History
Want to have your own customized enterprise-grade AI Data Science Team and domain-specific AI-powered Apps?
Send inquiries here: https://www.business-science.io/contact.html
If you're an aspiring data scientist who wants to learn how to build AI Agents and AI Apps for your company that performs Data Science, Business Intelligence, Churn Modeling, Time Series Forecasting, and more, then I'd love to help you.
Register for my next Generative AI for Data Scientists workshop here.
This project is a work in progress. New data science agents will be released soon.
🔥 Open Pandas AI Data Analyst: Load an Excel or CSV file and ask it questions. Get data and charts back.
🔥 SQL Database Agent: Connects any SQL Database, generates SQL queries from natural language, and returns data as a downloadable table.
🔥 Exploratory Data Copilot: An AI-powered data science app that performs automated exploratory data analysis (EDA) with EDA Reporting, Missing Data Analysis, Correlation Analysis, and more.
🔥 Pandas Data Analyst Agent: Combines the ability to wrangle, transform, and analyze data with an optional data visualization agent that can create interactive plots.
- NEW Exploratory Data Copilot: An AI-powered data science app that performs automated exploratory data analysis (EDA) with EDA Reporting, Missing Data Analysis, Correlation Analysis, and more. See Application
- SQL Database Agent App: Connects any SQL Database, generates SQL queries from natural language, and returns data as a downloadable table. See Application
- Data Wrangling Agent: Merges, Joins, Preps and Wrangles data into a format that is ready for data analysis. See Example
- Data Visualization Agent: Creates visualizations to help you understand your data. Returns JSON serializable plotly visualizations. See Example
- 🔥 Data Cleaning Agent: Performs Data Preparation steps including handling missing values, outliers, and data type conversions. See Example
- Feature Engineering Agent: Converts the prepared data into ML-ready data. Adds features to increase predictive accuracy of ML models. See Example
- 🔥 SQL Database Agent: Connects to SQL databases to pull data into the data science environment. Creates pipelines to automate data extraction. Performs Joins, Aggregations, and other SQL Query operations. See Example
- 🔥 Data Loader Tools Agent: Loads data from various sources including CSV, Excel, Parquet, and Pickle files. See Example
- 🔥 H2O Machine Learning Agent: Builds and logs 100's of high-performance machine learning models. See Example
- 🔥 MLflow Tools Agent (MLOps): This agent has 11+ tools for managing models, ML projects, and making production ML predictions with MLflow. See Example
- 🔥🔥 EDA Tools Agent: Performs automated exploratory data analysis (EDA) with EDA Reporting, Missing Data Analysis, Correlation Analysis, and more. See Example
- 🔥🔥 Pandas Data Analyst Agent: Combines the ability to wrangle, transform, and analyze data with an optional data visualization agent that can create interactive plots. See Example
- 🔥🔥 SQL Data Analyst Agent: Connects to SQL databases to pull data into the data science environment. Creates pipelines to automate data extraction. Performs Joins, Aggregations, and other SQL Query operations. Includes a Data Visualization Agent that creates visualizations to help you understand your data. See Example
- Data Analyst: Analyzes data structure, creates exploratory visualizations, and performs correlation analysis to identify relationships.
- Interpretability Agent: Performs Interpretable ML to explain why the model returned predictions including which features were the most important to the model.
- Supervisor: Forms task list. Moderates sub-agents. Returns completed assignment.
This project is for educational purposes only.
- It is not intended to replace your company's data science team
- No warranties or guarantees provided
- Creator assumes no liability for financial loss
- Consult an experienced Generative AI Data Scientist for building your own custom AI Data Science Team
- If you want a custom enterprise-grade AI Data Science Team, send inquiries here.
By using this software, you agree to use it solely for learning purposes.
You can install via PyPI (note that this is a beta version and breaking changes may occur until 0.1.0):
pip install ai-data-science-team
Or, if you want the latest version from GitHub:
pip install git+https://github.com/business-science/ai-data-science-team.git --upgrade
# Import libraries
from langchain_openai import ChatOpenAI
import pandas as pd
import h2o
import os
from ai_data_science_team.ml_agents import H2OMLAgent
# Load the data
df = pd.read_csv("data/churn_data.csv")
df
# Initialize the language model
os.environ['OPENAI_API_KEY'] = "YOUR_OPENAI_API_KEY"
llm = ChatOpenAI(model=MODEL)
llm
# Initialize the H2O ML Agent
ml_agent = H2OMLAgent(
model=llm,
log=True,
log_path="logs/",
model_directory="h2o_models/",
enable_mlflow=True, # Use this if you wish to log models to MLflow
)
ml_agent
# Run the agent
ml_agent.invoke_agent(
data_raw=df.drop(columns=["customerID"]),
user_instructions="Please do classification on 'Churn'. Use a max runtime of 30 seconds.",
target_variable="Churn"
)
# Retrieve and display the leaderboard of models
ml_agent.get_leaderboard()
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
This project is licensed under the MIT License. See LICENSE file for details.
I teach Generative AI Data Science to help you build AI-powered data science apps. Register for my next Generative AI for Data Scientists workshop here.
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