A Machine Learning Approach of Emotional Model

MLB Team Runs Allowed Prediction Project (Linear Regression)

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.

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.

Importing Dataset, missing data,

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.

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.

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

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

Data Analysis using Unsupervised Learning on Lyft dataset

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.

Linear regression model to predict the price of a car based on its mileage.

We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.

A new method of supervised feature scaling using decision tree

This repository contains files for a regression analysis of housing prices in Ames, Iowa.

Oversampling Class Imbalance, Feature Scaling, Parameter Tuning, Assessing Model Performance