Machine Learning Project Environment

This is a Python environment configured for machine learning projects.

⚠️ Python 3.14 Compatibility Notice

Current Python Version: 3.14.3

Due to the recent release of Python 3.14, some machine learning libraries may not have compatible versions yet. Here's the current status:

✅ Successfully Installed

• numpy (2.4.4) - Core numerical computing
• pandas (3.0.2) - Data manipulation and analysis
• scipy (1.17.1) - Scientific computing
• matplotlib (3.10.8) - Basic plotting
• seaborn (0.13.2) - Statistical data visualization
• plotly (6.7.0) - Interactive visualizations
• jupyter (1.1.1) - Jupyter notebooks
• ipython (9.12.0) - Enhanced Python shell
• notebook (7.5.5) - Jupyter notebook interface
• joblib (1.5.3) - Lightweight pipelining

⚠️ Not Currently Compatible with Python 3.14

• scikit-learn - Requires Python < 3.13
• tensorflow - No compatible version available
• pytorch - May not have 3.14 wheels yet
• xgboost - May require compilation
• lightgbm - May require compilation

Setup Instructions

1. Activate Virtual Environment

Linux/Mac:

source venv/bin/activate

Windows:

venv\Scripts\activate

2. Install Available Dependencies

pip install -r requirements.txt

Alternative Solutions

1. Use Python 3.12 or 3.11 for full compatibility with all ML libraries
2. Install from source for libraries that don't have wheels:

   pip install --no-binary :all: scikit-learn

3. Use conda/mamba which may have better compatibility:

   conda install scikit-learn tensorflow

Project Structure

project/
├── venv/                 # Virtual environment (ignored by git)
├── requirements.txt      # Dependencies
├── .gitignore           # Git ignore rules
├── README.md            # This file
├── data/                # Data directory (create as needed)
├── notebooks/           # Jupyter notebooks
├── src/                 # Source code
└── models/              # Trained models

Usage Examples

Basic Data Analysis (Works with current setup)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_csv('data/your_data.csv')
print(df.head())

# Basic visualization
df.plot(kind='hist')
plt.show()

Best Practices

1. Always activate the virtual environment before working
2. Consider using Python 3.12 for full ML library compatibility
3. Install new packages with pip install and update requirements.txt
4. Use pip freeze > requirements.txt to update dependencies
5. Keep data in the data/ directory