feature-scaling

A Machine Learning Approach of Emotional Model

MLB Team Runs Allowed Prediction Project (Linear Regression)

Machine learning project clustering countries based on socio-economic & demographic indicators using K-Means, iterative imputation & feature scaling.

A decoupled Machine Learning regression application utilizing FastAPI and Streamlit to predict house market valuations. Implements Scikit-Learn data pipeline scaling protocols (StandardScaler), a low-latency predictive API instance, and a dashboard layout tailored for interactive property feature analysis using the California Housing dataset.

This repository is a collection of basic code templates for Data Preparation. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.

This project implements Support Vector Regression (SVR) to predict the salary of an employee based on their position level. The script uses a dataset that contains position levels and corresponding salaries, applying feature scaling to improve the performance of the SVR model. The results are visualized to show how well the model fits the data.

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

Importing Dataset, missing data,

A Python implementation of Logistic Regression to classify social network ads based on age and estimated salary, featuring data visualization and performance metrics such as confusion matrix and accuracy score.

A complete machine learning project to detect fraudulent credit card transactions. It includes data preprocessing, feature scaling, model training (Logistic Regression), evaluation, and deployment using Streamlit. Built with modular, production-ready code and a simulated dataset for privacy-safe demonstrations.

EDA and preprocessing on the UCI Adult income dataset (missing-value imputation, log/robust scaling, encoding) comparing Logistic Regression and SVM classifiers across several experiment variants, including hyperparameter tuning.

We use machine learning and data analysis to predict resale prices of Singapore flats. Our documentation covers data preprocessing, feature engineering, regression, and model selection. Discover how we improved predictions to optimize solutions.

Implementación desde cero de un clasificador multiclase mediante regresión logística (One-vs-All), incluyendo análisis exploratorio de datos, visualización y entrenamiento con descenso de gradiente.

A school bootcamp for hands on learning of Machine Learning

Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.

📶In this repository, we will do feature engineering with Python.

A comprehensive Deep Learning-based Heart Disease Prediction System that analyzes patient clinical data and predicts cardiovascular disease multi-class risk classification (Low, Medium, High Risk) through an Artificial Neural Network (ANN) and binary disease detection via Random Forest and Logistic Regression models.

Data Analysis using Unsupervised Learning on Lyft dataset

Multi-variable linear regression trained with gradient descent (feature standardization + loss curve).