Intro to Machine Learning – Kaggle Course

This repository contains my work and notebooks from the Kaggle Intro to Machine Learning course.
It covers the fundamental concepts of building, evaluating, and improving machine learning models.

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/anjanakri/Intro-to-ML.git

Open the notebooks using Jupyter Notebook or Kaggle Notebooks.

🛠 Requirements

-Python 3.x -Pandas -scikit-learn -Jupyter Notebook (or run on Kaggle)

Install dependencies:

pip install pandas scikit-learn notebook

📌 About the Course

The Kaggle Intro to Machine Learning course is designed for beginners to understand: -How to handle data. -How to train and validate models. -How to make predictions and improve performance.

📂 Notebooks Overview

1. Explore Your Data

   • Viewing the first few rows of data.
   • Summary statistics with .describe().
   • Identifying data types and spotting potential issues.

2. Creating, Reading, and Writing

   • Loading data into Pandas DataFrames.
   • Reading CSV files and writing processed data back to disk.
   • Handling file paths and exploring datasets.

3. Your First Machine Learning Model

   • Building a simple decision tree model.
   • Training the model and making predictions.
   • Measuring model accuracy.

4. Model Validation

   • Splitting data into training and validation sets.
   • Understanding validation scores.
   • Avoiding data leakage.

5. Underfitting and Overfitting

   • Recognizing signs of underfitting and overfitting.
   • Using max_leaf_nodes to control model complexity.
   • Striking the balance for better generalization.

6. Random Forest

   • Introduction to the Random Forest algorithm.
   • Comparing Random Forest with Decision Trees.
   • Observing improvements in accuracy.

7. Final Competition Submission (Not in this repo)

   • This notebook contains my final submission for the course’s competition.
   • You can view it directly on my Kaggle profile: My Kaggle Profile.