My Journey Through the Kaggle Google 5-Day Intensive ML Sprint

Source: Dev.to

This is a submission for the Google AI Agents Writing Challenge: Learning Reflections OR Capstone Showcase

My Learning Journey / Project Overview

Over the past week, I completed the Kaggle × Google 5‑Day Intensive Program — a fast‑paced, hands‑on sprint that helped me dive into Python for Data Science, Machine Learning basics, and Kaggle‑style workflows. Below, I’m sharing the full structure of the course, how I experienced each day, what I built, and the skills I gained. If you’re starting out in ML or thinking of trying Kaggle, this might help you decide if this path is for you.

Key Concepts / Technical Deep Dive

• Python fundamentals (lists, dictionaries, loops, functions)
• Data cleaning and exploratory data analysis with Pandas
• Baseline machine‑learning models using Scikit‑Learn (Linear Regression, Decision Trees, Random Forests)
• Feature engineering, encoding, scaling, and hyper‑parameter tuning
• End‑to‑end ML pipeline construction and Kaggle submission workflow

Reflections & Takeaways

• Kaggle Notebooks are beginner‑friendly; live code execution makes experimentation straightforward.
• Clean, well‑explored data is the foundation for good ML results.
• Baseline models can deliver surprisingly decent performance with minimal tuning.
• Feature engineering and proper validation often improve performance more than switching to a more complex model.
• Going from zero to a full submission in 5 days is possible and hugely motivating—it turns theory into a tangible outcome.

Course Structure & My Daily Experience

Day 1 — Getting Started: Python Basics + Kaggle Environment

• Introduction to the Kaggle environment: Notebooks, datasets, competitions.
• Brushed up on Python essentials — lists, dictionaries, loops, conditionals, functions.
• First hands‑on task: loaded a dataset using Pandas and performed basic exploration (head, shape, info).

Takeaway: Kaggle Notebooks are beginner‑friendly, and running code live makes experimentation very straightforward.

Day 2 — Data Cleaning & Exploratory Data Analysis (EDA)

• Learned data cleaning: handling missing values, removing duplicates, filtering outliers.
• Explored data using Pandas: .describe(), grouping, filtering, summary statistics.
• Performed preliminary visualization to observe data distributions and relationships.

Takeaway: Investing time in clean, well‑explored data is critical—it lays the foundation for good ML results.

Day 3 — First Machine Learning Models (Baseline)

• Understood the ML workflow: splitting data into training and test sets, fitting models, evaluating performance.
• Built baseline models using Scikit‑Learn:
  • Linear Regression (for regression tasks)
  • Decision Trees
  • Random Forests
• Ran a quick mini‑competition/prediction task on a real dataset.

Takeaway: Even baseline models — with minimal tuning — can deliver surprisingly decent results on real‑world data.

Day 4 — Enhancing Models: Feature Engineering & Hyperparameter Tuning

• Practiced feature engineering: generating new features, encoding categorical variables, scaling when required.
• Applied hyperparameter tuning and cross‑validation strategies to improve model performance.
• Learned about the importance of model interpretation and avoiding overfitting.

Takeaway: Often, smarter features and better validation improve performance more than choosing a more complex model.

Day 5 — Final Project: End‑to‑End Pipeline + Submission

• Built a complete ML pipeline: Data loading → cleaning → exploration → feature engineering → model training → evaluation → prediction.
• Generated submission.csv and submitted to a real competition on Kaggle.
• Witnessed the model’s score and placement on the leaderboard — first “real” ML submission.

Takeaway: Going from zero to a full submission in 5 days is possible — and hugely motivating. It turns theory into a tangible outcome.