This app demonstrates custom-built Neural Networks implemented from scratch in Python using only NumPy.

This repository contains the final project for the Elements of Artificial Intelligence course at AGH University of Krakow (2026). Please find more information in README file.

End-to-end ML project predicting house prices using Python, scikit-learn, and Flask

Regression analysis on the California Housing dataset using scikit-learn

A repository for creating and maintaining a comprehensive directory of community resources in Alameda County, including shelters, food banks, healthcare services, and more.

Third Assignment in 'Practical topics in Machine Learning' course by Dr. Kfir Bar at Bar-Ilan University. Quick access to Jupyter notebook at the link below:

Data drift detection for machine learning using Evidently AI and Valohai. MLOps pipeline: preprocessing, training, drift monitoring and conditional retraining. Python, scikit-learn, California Housing example.

Machine Learning project for California House Price Prediction using Linear Regression, One-Hot Encoding, Pickle, and Streamlit.

📊 Analyze learning curves to diagnose model performance and enhance predictions using real-world housing data in this hands-on exploration of machine learning.

End-to-end machine learning pipeline for the California Housing Prices dataset, covering exploratory data analysis, data cleaning, feature engineering, Lasso regression for house price prediction (R² = 0.78), and SVM classification of price tiers into Low, Medium, and High categories (75.5% accuracy).

California housing data analysis and preprocessing — missing value imputation, geographic visualization, and correlation analysis.

A regression-based machine learning project to predict house prices using the California Housing dataset and scikit-learn.

A machine learning project using a custom-built Perceptron, Linear Regression, and a Neural Network to predict California housing prices based on the California Housing Dataset. Includes full data preprocessing, visualization, and evaluation pipeline. 3rd year University Project

End-to-end regression analysis and ML pipeline on the California Housing dataset using Julia — EDA, preprocessing, Linear Regression, Random Forest, and XGBoost with MLJ.jl, visualized in a Jupyter notebook.

Predicting California house prices using various regression models (Linear, Ridge, Lasso, Decision Tree, Random Forest) with hyperparameter tuning.

This repository contains two ML Internship projects (Month 2): Housing Price Prediction using Linear Regression on the California Housing dataset, and Iris Flower Classification using Random Forest and Logistic Regression on the Iris dataset.

End-to-end prediction project using various technologies to predict housing prices in California.

Projeto de Machine Learning.

This project is developed as part of the Data Mining course. It covers detailed EDA and comparative analysis of various data clustering algorithms on the California Housing 1990 Census dataset to evaluate performance and efficiency.

Предсказание стоимости жилья, используя распределённые вычисления в PySpark и различные подходы к предобработке признаков.