futuregoose,Gustaf Bodén,github

This GitHub is my data science playground. Think of it as a messy workbench overflowing with cool experiments (mostly Jupyter notebooks) I built while learning and expanding my workflow.

Each project follows a clear path: wrangling data, cleaning it up, analyzing extensively to understand the problem (think model prep!), and building production-ready machine learning models – both classification and regression.

Some projects prioritize education, skipping steps to highlight the core concepts. It's a win-win – solidifies my knowledge and lets others learn from my tinkering.

Feel free to hit me up on LinkedIn to share feedback, knowledge, and thoughts – eager to learn from others!

Project Breakdown

Repository	Project Type	Description
Ecommerce Purchase Prediction	End-to-End	Advanced predictive analytics for e-commerce conversion—featuring EBM, LightGBM models, SMOTE sampling, and a tailored interpret ML dashboard for stakeholder insights.
Analyzing Crime Data	End-to-End	In this ML Engineering lab, we clean and explore Chicago crime data, culminating in an XGBoost model fine-tuned with Hyperopt and Recursive Feature Elimination, yielding 89% precision.
Logistic Regression: Part 2	Fundamentals (Educational)	Applying logistic regression and random forest to optimize revenue. Thorough data preprocessing, insightful EDA, and comprehensive model evaluation. Builds upon prior knowledge in logistic regression fundamentals.
Logistic Regression: Part 1	Fundamentals (Educational)	Delve into model mathematics, error analysis, and performance. Focus on statistical fundamentals with statsmodels for understanding and analyzing logistic regression. Linear regression background advised.
Linear Regression: Part 2	Fundamentals (Educational)	Exploring Supervised ML, this study of California's housing employs EDA and OLS evaluation. Techniques like Polynomial Transformation, Ridge and Lasso Regularization, and Quantile Regression are used with scikit-learn for in-depth insights.
Linear Regression: Part 1	Fundamentals (Educational)	This project serves as an entry point into machine learning, focusing on building and evaluating basic linear regression models. By applying feature selection and understanding model performance, we offer a foundational approach to predictive modeling, blending algorithms with statistical insights for accuracy and interpretability.
Predicting Insurance Charges	End-to-End	Using Random Forest and XGBoost for regression to predict health insurance charges based on patient data. Features EDA, preprocessing, and in-depth insights.
Decoding Titanic	End-to-End	Comprehensive Titanic survival prediction using machine learning models like Logistic Regression and ensemble techniques for classification. Includes EDA, feature engineering, and model interpretation insights. Achieved 83.5% accuracy.
Bike Store Analysis	Business Intelligence	Analyzing a European bicycle retail business to enhance growth and profitability. Features in-depth EDA, business performance analysis, and strategic insights based on comprehensive sales data.

Outside of work, I keep my coding skills sharp by engaging with coding challenges on Codewars:

Gustaf Bodén's Projects

advanced_techniques_linear_regression

Exploring Supervised ML, this study of California's housing employs EDA and OLS evaluation. Techniques like Polynomial Transformation, Ridge and Lasso Regularization, and Quantile Regression are used with scikit-learn for in-depth insights.

advanced_techniques_logistic_regression

Applying logistic regression and random forest to optimize revenue. Thorough data preprocessing, insightful EDA, and comprehensive model evaluation. Builds upon prior knowledge in logistic regression fundamentals.

analyzing_crime_data

In this Machine Learning Engineering lab, we traverse from detailed data cleaning to deep exploratory analysis, extracting nuanced insights into Chicago crime data. The capstone is a polished XGBoost model, fine-tuned via step-wise Hyperopt and honed through Recursive Feature Elimination, achieving an commendable 89% precision rate.

bike-store-analysis

Analyzing a European bicycle retail business to enhance growth and profitability. Features in-depth EDA, business performance analysis, and strategic insights based on comprehensive sales data.

decoding_titanic

Comprehensive Titanic survival prediction using machine learning models like Logistic Regression and ensemble techniques for classification. Includes EDA, feature engineering, and model interpretation insights. Achieved 83.5% accuracy.

ecommerce_purchase_prediction

Advanced predictive analytics for e-commerce conversion—featuring EBM, LightGBM models, SMOTE sampling, and a tailored interpret ML dashboard for stakeholder insights.

fundamentals_logistic_regression

Explore model mathematics, error analysis, and performance. Focus on statistical fundamentals with statsmodels for understanding and analyzing logistic regression. Linear regression background advised.

fundamentals_ols_linear_regression

This project serves as an entry point into machine learning, focusing on building and evaluating basic linear regression models. By applying feature selection and understanding model performance, we offer a foundational approach to predictive modeling, blending algorithms with statistical insights for accuracy and interpretability.

predicting_insurance_charges

Using Random Forest and XGBoost for regression to predict health insurance charges based on patient data. Features EDA, preprocessing, and in-depth insights.

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