Machine Learning Library for C++

This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

Applying feature scaling with linear regression in python

Our curated repository compiles comprehensive notes covering various machine learning concepts, algorithms, and applications, providing a structured resource for both beginners and experienced practitioners to deepen their understanding and proficiency in the field.

Content: Multivariate regression, Feature scaling, Polynomial regression, gradient descent, regression using sklearn

This project will focus on data preparation and will follow the steps : data cleaning, handling text and categorical attributes, and feature scaling.

This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.

Calories_Brunt_Prediction

Machine Learning in Scikit-Learn and TensorFlow

IU Lessons

Credit Card Fraud Detection

Machine learning to predict which passengers survived the Titanic shipwreck

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.

Chapter 12: Data Preparation for Fraud Analytics

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

A school bootcamp for hands on learning of Machine Learning

Industry specific framework of Feature Engineering in Machine Learning