Lab 1: Getting Started with Data Mining in Python — NumPy Foundations
Kickstarting my data mining journey with Python: setting up the environment, exploring NumPy arrays, and understanding the foundations of multidimensional data.
Read more →Welcome to My Learning Journey 🚀
A portfolio-style space where I share projects, experiments, and lessons across Data Mining, Kaggle, Math, SQL, Theory, and Generative AI.
⛏️ Data mining
Lab 2: Evaluating Classification Models — Confusion Matrices, Errors, and ROC Curves
Learning how to measure model performance: from true/false positives to confusion matrices, error types, and ROC curves — with hands-on examples in Python.
Read more →Lab 3: From Perceptrons to Neural Networks — Building Smarter Classifiers
Experimenting with perceptrons, vectorization, and multi-layer neural networks — from toy datasets to digit recognition and universal approximation.
Read more →Lab 4: Estimating Probabilities — Maximum Likelihood vs Maximum A Posteriori
Exploring parameter estimation through coin flips: learning how MLE and MAP differ, and why Bayesian priors make estimates more robust.
Read more →Lab 5: Sampling Distributions and Handling Imbalanced Data with SMOTE
From probability distributions to SMOTE: exploring sampling techniques and applying them to balance datasets for better machine learning performance.
Read more →Lab 6: Logistic Regression for Text Classification — From Odds Ratios to TF-IDF
Applying logistic regression to classify newsgroup posts: exploring odds ratios, feature importance, and TF-IDF text representations.
Read more →Lab 7: Clustering with Hierarchical Methods — From Toy Data to Music Genres
Exploring unsupervised learning through hierarchical clustering, dendrograms, and a music genre classification dataset.
Read more →Lab 8: Dimensionality Reduction with PCA and ICA
Reducing complexity with PCA and uncovering hidden signals with ICA — applying dimensionality reduction techniques to the Iris dataset and beyond.
Read more →All posts are listed above for Data mining.
Total posts: 8📊 Kaggle
Heart Disease Prediction Using Logistic Regression vs Random Forest
In this blog post, I analyze the UCI Heart Disease dataset to predict whether a patient has heart disease using two machine learning models: Logistic Regression and Random Forest. This project builds on my...
Read more →Titanic Dataset: Logistic Regression vs Decision Tree
Logistic Regression Model: Interpretation & Explanation
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