H2o-Numpy-Pandas

H2O + NumPy + Pandas is an open-source AI/ML and data analytics stack used for numerical computing, data processing, and scalable machine learning workflows. It combines H2O’s machine learning platform with NumPy’s array computing capabilities and Pandas’ powerful data analysis features.

The solution supports common data science workflows including data loading, cleaning, transformation, numerical analysis, model training, and machine learning experimentation. It is ideal for AI/ML projects, data analytics, predictive modeling, and Python-based research environments on Azure Ubuntu.

Version Details:

• H2O Version: 3.46.0.10
• NumPy Version: 2.4.4
• Pandas Version: 3.0.3
• Platform: Ubuntu 26.04 on Azure

Features of H2O + NumPy + Pandas:

• Open-source machine learning and data analytics environment.
• H2O Flow web interface for machine learning workflows.
• NumPy support for high-performance numerical and array computing.
• Pandas support for data analysis, cleaning, and transformation.
• Python-based environment suitable for AI/ML development and testing.
• Supports scalable model training, data frames, and analytics workflows.

Usage instructions for H2O + NumPy + Pandas
$ sudo su
$ cd /opt/h2o-numpy-pandas
$ source venv/bin/activate
$ python start_h2o.py

Check Installed Versions:

cd /opt/h2o-numpy-pandas
source venv/bin/activate

python - <<'PY'
import h2o, numpy, pandas
print("H2O Version:", h2o.__version__)
print("NumPy Version:", numpy.__version__)
print("Pandas Version:", pandas.__version__)
PY

Credentials: No default username or password is configured unless authentication is enabled separately.

Access H2O Flow:
Open your browser and navigate to:
http://your-server-ip:54321

Disclaimer: H2O, NumPy, and Pandas are provided “as is” under their applicable open-source licenses. Users are responsible for proper configuration, secure access control, data protection, and validation of machine learning results. This solution is best suited for AI/ML, numerical computing, data analysis, and development or testing environments.