Building a Unified Dataset from Multiple CSV Trackers (Part 1)

🤖 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.

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🎯 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.

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🧠 How It Works: Dataset Assembly Pipeline

At a high level, the notebook follows this pipeline:

1. Load multiple CSV files
2. Inspect columns and overlaps
3. Align schemas and column names
4. Join datasets using shared keys
5. Clean and normalise values
6. Export a consolidated dataset

This mirrors real-world ML preprocessing pipelines.

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🧩 The Technical Part: Joining CSV Files

A simplified example of the core logic looks like this:

import pandas as pd

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.

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💡 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.

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🏁 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.

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🔗 Link to Notebook

Notebook link: Coming Soon