Kaggle-Notebooks

This repository is for my public notebooks on Kaggle.

My Kaggle Profile → Erkan Hatipoglu | Kaggle

The explanation for each notebook is below:

1 - getting-started-with-titanic.ipynb

This is my first notebook on Kaggle. I was a newbie, and the notebook had so many mistakes. I keep it since it is my first notebook.

Original Notebook on Kaggle → Getting Started with Titanic

Data → Titanic - Machine Learning from Disaster

2 - intermediate-machine-learning-course-helper.ipynb

This notebook is inspired by Intermediate Machine Learning Course by Alexis Cook at Kaggle Learn.

It includes solutions for handling missing values, dealing with categorical variables, using Scikit-learn library pipelines, cross-validation and XGBoost on Ames Housing dataset.

Original Notebook on Kaggle → Intermediate Machine Learning Course Helper

Data → Housing Prices Competition for Kaggle Learn Users

3 - housing-prices-pipeline-starter-code.ipynb

This is a Scikit-learn pipeline tutorial on Ames Housing dataset. It includes preprocessing, validation, cross-validation, prediction, saving, and submission to the competition steps.

Original Notebook on Kaggle → Housing Prices: Pipeline Starter Code

Data → Housing Prices Competition for Kaggle Learn Users

4 - introduction-to-sklearn-pipelines-with-titanic.ipynb

A comprehensive tutorial about Scikit-learn library pipelines. The notebook includes:

• Pipelines,
• Estimators,
• fit and transform mechanism,
• Custom Transformers,
• Column Transformers,
• Feature Unions,
• Implementing a Scikit-learn pipeline from start to end with tabular data.

Original Notebook on Kaggle → Introduction to Sklearn Pipelines with Titanic

Data → Titanic - Machine Learning from Disaster

5 - housing-prices-gridsearchcv-example.ipynb

A tutorial on using GridSearchCV with Scikit-learn library pipelines. It is forked from the housing-prices-pipeline-starter-code.ipynb and GridSearchCV part is added.

Original Notebook on Kaggle → Housing Prices: GridSearchCV Example

Data → Housing Prices Competition for Kaggle Learn Users

6 - titanic-on-function-transformers-and-pipelines.ipynb

A tutorial on using function transformers with Scikit-learn library pipelines.

Original Notebook on Kaggle → Titanic: On Function Transformers and Pipelines

Data → Titanic - Machine Learning from Disaster

7 - heart-failure-prediction-with-xgboost.ipynb

This is a notebook for predicting cardiovascular death events using Heart failure clinical records.

The notebook includes:

• Scikit-learn library Pipelines,
• Validation with early_stopping_rounds,
• Grid Search,
• Cross-validation,
• confusion matrix,
• accuracy, precision, recall, f1_score from scratch.

Original Notebook on Kaggle → Heart Failure Prediction with XGBoost

Data → Heart Failure Prediction

8 - house-prices-using-pipelines.ipynb

This is a notebook similar to housing-prices-pipeline-starter-code.ipynb (#3). It is submitted to the GettingStarted Prediction Competition House Prices - Advanced Regression Techniques.

Original Notebook on Kaggle → House Prices: Using Pipelines

Data → House Prices - Advanced Regression Techniques

9 - univariate-linear-regression-from-scratch.ipynb

This tutorial notebook explains basic machine learning concepts such as univariate linear regression, the hypothesis, the cost function, and gradient descent.

Original Notebook on Kaggle → Univariate Linear Regression From Scratch

Data → Linear Regression

10 - multivariate-linear-regression-from-scratch.ipynb

This tutorial notebook explains how to implement the hypothesis, cost function, and gradient descent algorithms in Python with a vectorization method for a multivariate Linear Regression task.

Original Notebook on Kaggle → Multivariate Linear Regression From Scratch

Data → Graduate Admission 2

11 - linear-regression-using-the-normal-equation.ipynb

This is my current project!

Original Notebook on Kaggle → Linear Regression Using the Normal Equation

Data → Graduate Admission 2