🤖 Introduction: Shaping Data for Learning Systems
By this stage in the pipeline, the dataset is already:
- Unified from multiple sources
- Processed and standardised for consistency
However, machine learning and AI systems require more than clean data — they require informative data.
This notebook represents Part 3, where the dataset is prepared and enriched with ML- and AI-relevant structure, making it suitable for feature engineering, embeddings, and modelling.
This is where data becomes learnable.
🎯 Purpose: Making Data Useful for ML & AI
The goal of this step is to:
- Prepare the dataset specifically for ML/AI workflows
- Add or refine fields that improve signal quality
- Align data formats with downstream modelling needs
- Reduce ambiguity in features and labels
This step bridges data engineering and machine learning.
🧠 How It Works: ML-Oriented Dataset Preparation
At a high level, this notebook performs the following:
- Load the processed and standardised dataset
- Identify fields relevant for ML and AI tasks
- Refine or derive features from existing data
- Remove noise or non-informative columns
- Ensure the dataset structure supports learning workflows
Every transformation is driven by model readiness, not just cleanliness.
🧩 The Technical Part: Preparing Features for Learning
A simplified example of preparation logic might look like this:
df["duration_hours"] = df["duration_minutes"] / 60
df["is_long_task"] = df["duration_hours"] > 2
Other preparation techniques demonstrated include:
- 🧮 Deriving numeric features
- 🏷 Creating categorical or boolean indicators
- 🧠 Aligning feature naming for clarity
- 📐 Selecting ML-relevant columns only
These steps reduce friction in later ML stages.
💡 Key Takeaways: ML Preparation Is Intentional
This notebook reinforces several important ideas:
- 🤖 ML datasets require deliberate feature thinking
- 🧠 Not all clean data is useful data
- 🔁 Preparation improves model performance downstream
- 🛠 Feature readiness is as important as model choice
Well-prepared datasets simplify everything that follows.
🏁 Conclusion: Ready for the Next ML Steps
Preparing and Enriching the Dataset for ML & AI (Part 3) marks a clear transition point:
The dataset is no longer just correct — it is now useful for learning systems.
With this foundation, the pipeline can confidently proceed to:
- Handling missing values
- Generating embeddings
- Model training and validation
This notebook sets the stage for true ML work.
🔗 Link to Notebook
Notebook link: Coming Soon