30 Kaggle Challenges in 30 Days

Project Overview

Welcome to my 30 Kaggle Challenges in 30 Days project! I’ve set out to solve 30 Kaggle Playground problems over the next 30 days. The goal isn’t just to hit the finish line but to dive deep into different aspects of data science and machine learning, from data wrangling to building models and feature engineering.

This project is all about challenging myself to think creatively and improve my skills by solving Kaggle playground problems. It’s not just about getting the perfect result but about learning, experimenting, and pushing myself with each new challenge.

Why This Challenge?

I’ve always been passionate about problem-solving and data-driven insights. Kaggle’s Playground Series offers a fantastic platform to take on fun, low-pressure problems while continuously growing and learning. For me, this challenge is a way to:

• Stay motivated and focused.
• Explore new techniques and approaches.
• Get creative with data.
• Learn from mistakes and iterate quickly.

Challenge Structure

Over the course of these 30 days, I’ll be diving into a variety of problems, including:

• Binary Classification
• Regression
• Time Series Forecasting
• Multiclass Classification
• Feature Engineering and Selection
• Model Tuning and Hyperparameter Optimization

Each day, I will focus on a specific Kaggle Playground problem and document my approach, insights, challenges, and results for each one.

What to Expect

• Daily Blog Posts: I’ll be sharing a blog post for each challenge, detailing the problem, my approach, and what I learned.
• Code and Notebooks: The code for each problem will be shared through GitHub and linked in each post.
• Reflection: After the 30 days, I’ll reflect on the overall experience - what worked, what didn’t, and how I grew as a data scientist.

Day 1: Binary Classification of Insurance Cross Selling

Description: Tackling the first challenge! Today’s problem is focused on binary classification for insurance cross-selling.

Notebook: Kaggle Notebook for S4E7
Code: GitHub Repository for Day 1

Day 2: Classification with an Academic Success Dataset

Description: Build classification model to predict students’ dropout and academic success. The problem is formulated as a three category classification task, with a significant class imbalance. I applied stratified K-fold cross-validation and tried multiple models to optimize performance.

Notebook: Kaggle Notebook for S4E6
Code: GitHub Repository for Day 2

Day 3: Regression with a Flood Prediction Dataset

Description: Developed a regression model to predict the probability of flooding in different regions. This challenge focused on feature analysis and automating the machine learning workflow for running multiple models efficiently. The project was enhanced by building a reusable project structure that can handle multiple model runs with a single command.

Notebook: Kaggle Notebook for S4E5
Code: GitHub Repository for Day 3

Day 4: Regression with an Abalone Dataset

Description: Build a regression model to predict the age of abalone from physical measurements. Utilize the pipelines from sklearn to streamline all preprocessing and model building.

Notebook: Kaggle Notebook for S4E4
Code: GitHub Repository for Day 4

Day 5: Steel Plate Defect Prediction

Description: Build a multiclass classifier to identify a type of steel plate. The dataset contains multiple labels corresponding to seven distinct fault types, making this problem a multi-label classification task.

Notebook: Kaggle Notebook for S4E3
Code: GitHub Repository for Day 5

Day 6: Multi-Class Prediction of Obesity Risk

Description: Predict obesity risk based on lifestyle and health factors using a multi-class classification approach, exploring several machine learning models.

Notebook: Kaggle Notebook for S4E2
Code: GitHub Repository for Day 6

Conclusion

This journey is not just about solving 30 problems, but about embracing the learning process, enjoying the challenge, and exploring different data science techniques. Feel free to follow along, and I hope to inspire others who are passionate about data science to take on similar challenges.