Implementation of logistic regression using only numpy and matplotlib (no scikit-learn). Includes gradient descent, cost function visualization, and decision boundary plots with GIF animations for better understanding.

Machine Learning algorithms implemented from scratch using pure NumPy | Includes Linear/Logistic Regression, Naive Bayes, K-Means, PCA, and Decision Trees with mathematical transparency | Interactive Streamlit app for visualizing algorithm behavior in real-time.

I use various regression methods and try to predict the house prices by using them. As you can guess, there are various methods to suceed this and each method has pros and cons. I think regression is one of the most important methods because it gives us more insight about the data. When we ask why, it is easier to interpret the relation between the response and explanatory variables. I start with a very simple model and continue with more complex ones after visualizing some features and a data mining process. I try to find the best regression for this dataset. On the other hand, if you are looking for more theory and do not want to use built in functions, I recommend you to check my other kernel k-NN, Logistic Regression, k-Fold CV from Scratch.

🧠 Foundational Deep Learning Projects built from scratch using Python & NumPy. Includes Linear & Logistic Regression, Neural Networks, and MLP classification with visualizations. Ideal for beginners and ML enthusiasts!

A collection of machine learning models I’ve implemented from scratch and with libraries — including Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN), XGBoost, SVM — for learning, analysis, and visualization.

I implemented a Logistic Regression model in C++ and Python to classify whether or not a tweet has a positive or negative sentiment associated with it. My C++ code involves training Logistic Regression from scratch, while my Python code includes visualizations to better understand the process.

Created dataset from scratch, cleaned with Pandas, trained Logistic Regression & Random Forest, and visualized results using confusion matrix and feature importance. Simple end-to-end project for mental health analytics.

Logistic Regression implemented from scratch in Python using gradient descent, with metrics and visualizations

From-scratch implementation of Logistic Regression and Regularized Logistic Regression for binary classification, with clear visualizations and practical examples using Python and NumPy.

Logistic regression implemented from scratch using NumPy, with visualization of decision boundary and loss convergence.

Implementing Logistic Regression from scratch using Gradient Descent with step-by-step visualization and math explanation.

Logistic Regression implemented from scratch using NumPy with L2 regularization, gradient descent optimization, and loss visualization.

This repository implements logistic regression from scratch for heart disease risk prediction, with EDA, regularization, and decision-boundary visualizations.

A complete implementation of Logistic Regression built from scratch using Python and NumPy, with detailed visualizations and mathematical explanations.

🪄 A data science project focused on data exploration and visualization, with logistic regression implemented from scratch.

Polynomial Logistic Regression implemented from scratch using NumPy with L2 regularization and non-linear decision boundary visualization.

A university project utilizing Machine Learning algorithms (KNN, Logistic Regression) built from scratch to predict diabetes, complete with data visualizations and EDA.

A from-scratch implementation of a logistic regression classifier with data analysis and visualization tools for exploring and predicting datasets.

A from-scratch implementation of a logistic regression binary classifier, with data analysis and visualization tools to explore and interpret datasets.

🧠 Machine Learning Models from Scratch-- Educational project showcasing core Machine Learning algorithms — Linear Regression, Logistic Regression, and K-Means — implemented from scratch using Python and NumPy. Includes math explanations, visualizations, and comparisons with scikit-learn for clear model understanding.

A complete implementation of Logistic Regression from scratch to scikit-learn, including binary and multiclass (One-vs-Rest) classification with evaluation and visualization.

A complete, from-scratch implementation of logistic regression with L1/L2 regularization, evaluation metrics, and visualization — built using NumPy for educational and practical use.

**Binary classification using Logistic Regression implemented from scratch with gradient descent, validated against scikit-learn, and visualized through projected decision boundaries on placement data.**

Titanic ML project: from-scratch NumPy vs scikit-learn implementations. Features logistic regression variants, AdaBoost, bagging with comprehensive metrics & visualizations. Perfect for understanding ML algorithm mathematics & library comparisons.

Titanic Dataset Logistic Regression (from scratch) Python project demonstrating: manual implementation of logistic regression data preprocessing and visualization evaluation with accuracy, precision, recall, F1-score, and confusion matrix Technologies: Python, NumPy, pandas, matplotlib, seaborn, scikit-learn

The data is cleaned and visualized to understand it better. Then Linear and Logistic Regression models are built from scratch and with Scikit-learn. Finally, results are compared, showing Logistic Regression works best for this binary problem.

Implementation of Perceptron and Logistic Regression from scratch using SGD and Batch Gradient Descent, compared with Scikit-learn on the Breast Cancer dataset with decision boundary visualization.

Homework assignment that involves training Logistic and Linear Ridge regression models using Gradient Descent. Implementing a SVM through scikit-learn and from scratch with SMO optimization. Implementing K-Nearest Neighbours from scratch with NumPy and visualizing Kernel Ridge Regression results on non-linear data.

Logistic Regression from scratch with a Focal Loss extension for imbalanced classification. Evaluates performance across multiple datasets using advanced metrics, visualizations, and statistical tests to analyze model robustness and minority class detection.

Built a Breast Cancer Prediction System using Python & Logistic Regression. Predicted tumors as Malignant or Benign using the Breast Cancer Wisconsin dataset (569 records, 30 features). Implemented logistic regression from scratch, visualized data, and evaluated with accuracy, confusion matrix, and classification report.