ML-For-Beginners/TROUBLESHOOTING.md

# Troubleshooting Guide

This guide helps you solve common problems when working with the Machine Learning for Beginners curriculum. If you don't find a solution here, please check our [GitHub Discussions](https://github.com/microsoft/ML-For-Beginners/discussions) or [open an issue](https://github.com/microsoft/ML-For-Beginners/issues).

## Table of Contents

- [Installation Issues](#installation-issues)
- [Jupyter Notebook Issues](#jupyter-notebook-issues)
- [Python Package Issues](#python-package-issues)
- [R Environment Issues](#r-environment-issues)
- [Quiz Application Issues](#quiz-application-issues)
- [Data and File Path Issues](#data-and-file-path-issues)
- [Common Error Messages](#common-error-messages)
- [Performance Issues](#performance-issues)
- [Environment and Configuration](#environment-and-configuration)

---

## Installation Issues

### Python Installation

**Problem**: `python: command not found`

**Solution**:
1. Install Python 3.8 or higher from [python.org](https://www.python.org/downloads/)
2. Verify installation: `python --version` or `python3 --version`
3. On macOS/Linux, you may need to use `python3` instead of `python`

**Problem**: Multiple Python versions causing conflicts

**Solution**:
```bash
# Use virtual environments to isolate projects
python -m venv ml-env

# Activate virtual environment
# On Windows:
ml-env\Scripts\activate
# On macOS/Linux:
source ml-env/bin/activate
```

### Jupyter Installation

**Problem**: `jupyter: command not found`

**Solution**:
```bash
# Install Jupyter
pip install jupyter

# Or with pip3
pip3 install jupyter

# Verify installation
jupyter --version
```

**Problem**: Jupyter won't launch in browser

**Solution**:
```bash
# Try specifying the browser
jupyter notebook --browser=chrome

# Or copy the URL with token from terminal and paste in browser manually
# Look for: http://localhost:8888/?token=...
```

### R Installation

**Problem**: R packages won't install

**Solution**:
```r
# Ensure you have the latest R version
# Install packages with dependencies
install.packages(c("tidyverse", "tidymodels", "caret"), dependencies = TRUE)

# If compilation fails, try installing binary versions
install.packages("package-name", type = "binary")
```

**Problem**: IRkernel not available in Jupyter

**Solution**:
```r
# In R console
install.packages('IRkernel')
IRkernel::installspec(user = TRUE)
```

---

## Jupyter Notebook Issues

### Kernel Issues

**Problem**: Kernel keeps dying or restarting

**Solution**:
1. Restart the kernel: `Kernel → Restart`
2. Clear output and restart: `Kernel → Restart & Clear Output`
3. Check for memory issues (see [Performance Issues](#performance-issues))
4. Try running cells individually to identify problematic code

**Problem**: Wrong Python kernel selected

**Solution**:
1. Check current kernel: `Kernel → Change Kernel`
2. Select the correct Python version
3. If kernel is missing, create it:
```bash
python -m ipykernel install --user --name=ml-env
```

**Problem**: Kernel won't start

**Solution**:
```bash
# Reinstall ipykernel
pip uninstall ipykernel
pip install ipykernel

# Register the kernel again
python -m ipykernel install --user
```

### Notebook Cell Issues

**Problem**: Cells are running but not showing output

**Solution**:
1. Check if cell is still running (look for `[*]` indicator)
2. Restart kernel and run all cells: `Kernel → Restart & Run All`
3. Check browser console for JavaScript errors (F12)

**Problem**: Can't run cells - no response when clicking "Run"

**Solution**:
1. Check if Jupyter server is still running in terminal
2. Refresh the browser page
3. Close and reopen the notebook
4. Restart Jupyter server

---

## Python Package Issues

### Import Errors

**Problem**: `ModuleNotFoundError: No module named 'sklearn'`

**Solution**:
```bash
pip install scikit-learn

# Common ML packages for this course
pip install scikit-learn pandas numpy matplotlib seaborn
```

**Problem**: `ImportError: cannot import name 'X' from 'sklearn'`

**Solution**:
```bash
# Update scikit-learn to latest version
pip install --upgrade scikit-learn

# Check version
python -c "import sklearn; print(sklearn.__version__)"
```

### Version Conflicts

**Problem**: Package version incompatibility errors

**Solution**:
```bash
# Create a new virtual environment
python -m venv fresh-env
source fresh-env/bin/activate  # or fresh-env\Scripts\activate on Windows

# Install packages fresh
pip install jupyter scikit-learn pandas numpy matplotlib seaborn

# If specific version needed
pip install scikit-learn==1.3.0
```

**Problem**: `pip install` fails with permission errors

**Solution**:
```bash
# Install for current user only
pip install --user package-name

# Or use virtual environment (recommended)
python -m venv venv
source venv/bin/activate
pip install package-name
```

### Data Loading Issues

**Problem**: `FileNotFoundError` when loading CSV files

**Solution**:
```python
import os
# Check current working directory
print(os.getcwd())

# Use relative paths from notebook location
df = pd.read_csv('../../data/filename.csv')

# Or use absolute paths
df = pd.read_csv('/full/path/to/data/filename.csv')
```

---

## R Environment Issues

### Package Installation

**Problem**: Package installation fails with compilation errors

**Solution**:
```r
# Install binary version (Windows/macOS)
install.packages("package-name", type = "binary")

# Update R to latest version if packages require it
# Check R version
R.version.string

# Install system dependencies (Linux)
# For Ubuntu/Debian, in terminal:
# sudo apt-get install r-base-dev
```

**Problem**: `tidyverse` won't install

**Solution**:
```r
# Install dependencies first
install.packages(c("rlang", "vctrs", "pillar"))

# Then install tidyverse
install.packages("tidyverse")

# Or install components individually
install.packages(c("dplyr", "ggplot2", "tidyr", "readr"))
```

### RMarkdown Issues

**Problem**: RMarkdown won't render

**Solution**:
```r
# Install/update rmarkdown
install.packages("rmarkdown")

# Install pandoc if needed
install.packages("pandoc")

# For PDF output, install tinytex
install.packages("tinytex")
tinytex::install_tinytex()
```

---

## Quiz Application Issues

### Build and Installation

**Problem**: `npm install` fails

**Solution**:
```bash
# Clear npm cache
npm cache clean --force

# Remove node_modules and package-lock.json
rm -rf node_modules package-lock.json

# Reinstall
npm install

# If still fails, try with legacy peer deps
npm install --legacy-peer-deps
```

**Problem**: Port 8080 already in use

**Solution**:
```bash
# Use different port
npm run serve -- --port 8081

# Or find and kill process using port 8080
# On Linux/macOS:
lsof -ti:8080 | xargs kill -9

# On Windows:
netstat -ano | findstr :8080
taskkill /PID <PID> /F
```

### Build Errors

**Problem**: `npm run build` fails

**Solution**:
```bash
# Check Node.js version (should be 14+)
node --version

# Update Node.js if needed
# Then clean install
rm -rf node_modules package-lock.json
npm install
npm run build
```

**Problem**: Linting errors preventing build

**Solution**:
```bash
# Fix auto-fixable issues
npm run lint -- --fix

# Or temporarily disable linting in build
# (not recommended for production)
```

---

## Data and File Path Issues

### Path Problems

**Problem**: Data files not found when running notebooks

**Solution**:
1. **Always run notebooks from their containing directory**
   ```bash
   cd /path/to/lesson/folder
   jupyter notebook
   ```

2. **Check relative paths in code**
   ```python
   # Correct path from notebook location
   df = pd.read_csv('../data/filename.csv')
   
   # Not from your terminal location
   ```

3. **Use absolute paths if needed**
   ```python
   import os
   base_path = os.path.dirname(os.path.abspath(__file__))
   data_path = os.path.join(base_path, 'data', 'filename.csv')
   ```

### Missing Data Files

**Problem**: Dataset files are missing

**Solution**:
1. Check if data should be in the repository - most datasets are included
2. Some lessons may require downloading data - check lesson README
3. Ensure you've pulled the latest changes:
   ```bash
   git pull origin main
   ```

---

## Common Error Messages

### Memory Errors

**Error**: `MemoryError` or kernel dies when processing data

**Solution**:
```python
# Load data in chunks
for chunk in pd.read_csv('large_file.csv', chunksize=10000):
    process(chunk)

# Or read only needed columns
df = pd.read_csv('file.csv', usecols=['col1', 'col2'])

# Free memory when done
del large_dataframe
import gc
gc.collect()
```

### Convergence Warnings

**Warning**: `ConvergenceWarning: Maximum number of iterations reached`

**Solution**:
```python
from sklearn.linear_model import LogisticRegression

# Increase max iterations
model = LogisticRegression(max_iter=1000)

# Or scale your features first
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
```

### Plotting Issues

**Problem**: Plots not showing in Jupyter

**Solution**:
```python
# Enable inline plotting
%matplotlib inline

# Import pyplot
import matplotlib.pyplot as plt

# Show plot explicitly
plt.plot(data)
plt.show()
```

**Problem**: Seaborn plots look different or throw errors

**Solution**:
```python
import warnings
warnings.filterwarnings('ignore', category=UserWarning)

# Update to compatible version
# pip install --upgrade seaborn matplotlib
```

### Unicode/Encoding Errors

**Problem**: `UnicodeDecodeError` when reading files

**Solution**:
```python
# Specify encoding explicitly
df = pd.read_csv('file.csv', encoding='utf-8')

# Or try different encoding
df = pd.read_csv('file.csv', encoding='latin-1')

# For errors='ignore' to skip problematic characters
df = pd.read_csv('file.csv', encoding='utf-8', errors='ignore')
```

---

## Performance Issues

### Slow Notebook Execution

**Problem**: Notebooks are very slow to run

**Solution**:
1. **Restart kernel to free memory**: `Kernel → Restart`
2. **Close unused notebooks** to free resources
3. **Use smaller data samples for testing**:
   ```python
   # Work with subset during development
   df_sample = df.sample(n=1000)
   ```
4. **Profile your code** to find bottlenecks:
   ```python
   %time operation()  # Time single operation
   %timeit operation()  # Time with multiple runs
   ```

### High Memory Usage

**Problem**: System running out of memory

**Solution**:
```python
# Check memory usage
df.info(memory_usage='deep')

# Optimize data types
df['column'] = df['column'].astype('int32')  # Instead of int64

# Drop unnecessary columns
df = df[['col1', 'col2']]  # Keep only needed columns

# Process in batches
for batch in np.array_split(df, 10):
    process(batch)
```

---

## Environment and Configuration

### Virtual Environment Issues

**Problem**: Virtual environment not activating

**Solution**:
```bash
# Windows
python -m venv venv
venv\Scripts\activate.bat

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

# Check if activated (should show venv name in prompt)
which python  # Should point to venv python
```

**Problem**: Packages installed but not found in notebook

**Solution**:
```bash
# Ensure notebook uses the correct kernel
# Install ipykernel in your venv
pip install ipykernel
python -m ipykernel install --user --name=ml-env --display-name="Python (ml-env)"

# In Jupyter: Kernel → Change Kernel → Python (ml-env)
```

### Git Issues

**Problem**: Can't pull latest changes - merge conflicts

**Solution**:
```bash
# Stash your changes
git stash

# Pull latest
git pull origin main

# Reapply your changes
git stash pop

# If conflicts, resolve manually or:
git checkout --theirs path/to/file  # Take remote version
git checkout --ours path/to/file    # Keep your version
```

### VS Code Integration

**Problem**: Jupyter notebooks won't open in VS Code

**Solution**:
1. Install Python extension in VS Code
2. Install Jupyter extension in VS Code
3. Select correct Python interpreter: `Ctrl+Shift+P` → "Python: Select Interpreter"
4. Restart VS Code

---

## Additional Resources

- **GitHub Discussions**: [Ask questions and share solutions](https://github.com/microsoft/ML-For-Beginners/discussions)
- **Microsoft Learn**: [ML for Beginners modules](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
- **Video Tutorials**: [YouTube Playlist](https://aka.ms/ml-beginners-videos)
- **Issue Tracker**: [Report bugs](https://github.com/microsoft/ML-For-Beginners/issues)

---

## Still Having Issues?

If you've tried the solutions above and still experiencing problems:

1. **Search existing issues**: [GitHub Issues](https://github.com/microsoft/ML-For-Beginners/issues)
2. **Check discussions**: [GitHub Discussions](https://github.com/microsoft/ML-For-Beginners/discussions)
3. **Open a new issue**: Include:
   - Your operating system and version
   - Python/R version
   - Error message (full traceback)
   - Steps to reproduce the problem
   - What you've already tried

We're here to help! 🚀