Full Setup Guide¶

Everything you need installed to run the code in this module.

Python Version¶

We recommend Python 3.10+. Check your version:

python --version

Virtual Environment¶

Always use a virtual environment to isolate your project dependencies:

# Create the environment
python -m venv venv

# Activate it
# Windows (PowerShell):
venv\Scripts\Activate.ps1

# Windows (CMD):
venv\Scripts\activate

# Mac/Linux:
source venv/bin/activate

Install Dependencies¶

pip install pandas numpy scikit-learn seaborn matplotlib statsmodels xgboost lightgbm shap lime optuna prophet joblib imbalanced-learn gower kmodes pmdarima

Core Libraries¶

Library	Purpose
`pandas`	Data manipulation and analysis
`numpy`	Numerical computing
`scikit-learn`	ML algorithms, preprocessing, evaluation, model selection
`seaborn` / `matplotlib`	Data visualisation
`statsmodels`	Statistical models and time series analysis
`xgboost` / `lightgbm`	Gradient boosting frameworks
`shap` / `lime`	Model explainability
`optuna`	Bayesian hyperparameter optimisation
`prophet`	Automated time series forecasting
`joblib`	Saving and loading trained models
`imbalanced-learn`	SMOTE and other resampling techniques

Verify Installation¶

import sklearn
import pandas as pd
import seaborn as sns

print(f"scikit-learn: {sklearn.__version__}")
print(f"pandas: {pd.__version__}")
print(f"seaborn: {sns.__version__}")
print("All core libraries installed successfully.")

KSB Mapping¶

KSB	Description	How This Addresses It
K1	Context of Data Science	Understanding where ML sits within the broader discipline
S3	Programming languages and tools	Setting up the development environment and dependencies
B6	Commitment to keeping up to date	Engaging with current ML resources and research