🤖 Introduction: Before ML Comes Data
Machine learning doesn’t start with models.
It starts with data preparation.
This notebook is Part 1 of preparing a dataset for machine learning. It focuses on joining multiple CSV files, cleaning inconsistencies, and producing a structured dataset that can later be used for feature engineering and model training.
This is where most ML work actually happens.
🎯 Purpose: Making Data ML-Ready
The goal of this notebook is to demonstrate how to:
- Combine multiple CSV sources into a single dataset
- Resolve schema differences between files
- Clean and standardise raw values
- Produce a consistent, analysis-ready table
- Lay the groundwork for machine learning workflows
This is data engineering in service of ML.
🧠 How It Works: Dataset Assembly Pipeline
At a high level, the notebook follows this pipeline:
- Load multiple CSV files
- Inspect columns and overlaps
- Align schemas and column names
- Join datasets using shared keys
- Clean and normalise values
- Export a consolidated dataset
This mirrors real-world ML preprocessing pipelines.
🧩 The Technical Part: Joining CSV Files
A simplified example of the core logic looks like this:
df1 = pd.read_csv("data_part_1.csv")
df2 = pd.read_csv("data_part_2.csv")
merged_df = df1.merge(df2, on="id", how="inner")
Across the notebook, techniques such as the following are applied:
- 📂 Reading multiple CSV files
- 🔗 Joining datasets with
merge - 🧼 Cleaning inconsistent fields
- 📐 Selecting and reordering columns
- 🧠 Ensuring data integrity post-join
Two versions of the notebook (v1 and v2) show iterative improvement, reflecting real development workflows.
💡 Key Takeaways: ML Is Won Before Training
This notebook reinforces several critical ML truths:
- 📊 Models are only as good as the data
- 🧼 Cleaning and consistency matter more than algorithms
- 🧱 Structured datasets enable downstream success
- 🔁 Iteration is part of data preparation
Most ML failures begin with poor data prep — this notebook avoids that trap.
🏁 Conclusion: The First ML Milestone
Preparing Dataset for ML – Part 1 represents an important shift:
You’re no longer just analysing data —
you’re engineering datasets for learning systems.
With this foundation, the next natural steps are:
- Feature engineering
- Encoding categorical variables
- Train/test splitting
- Model training and evaluation
This notebook clearly signals ML readiness, not just interest.
🔗 Link to Notebook
Notebook link: Coming Soon