feature-scaling

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

A Machine Learning Approach of Emotional Model

Exemplary, annotated machine learning pipeline for any tabular data problem.

Karma of Humans is AI

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Pandas, Numpy, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the features. Using Scikit-Learn , implemented Algorithms to increase the effective R2 score.

Machine Learning Notebooks

Machine learning algorithms repository

Machine-learning models to predict whether customers respond to a marketing campaign

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Capstone project for Udacity's Intro to Machine Learning Course

The purpose of this project is to predict house prices based of off the Boston house price dataset. The project implements univariate and multivariate linear and polynomial regression models.

EDA and Feature engineering with Plotly library!

The purpose of this project is to analyze the impact of climate change on air quality for the city of Austin and create a machine learning model that can establish a correlation between the level of air pollutants like Ozone and NO2 and the climate parameters by using regression models and null hypothesis.

This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.

Aplicação de Aprendizado de Máquina (Machine Learning) para o setor de energia. #Previsão #Carga #Geração #Afluentes

A collection of working snippets used for machine learning related tasks.

A Mathematical Intuition behind Linear Regression Algorithm

R package implementing Gradient Descent and its variants for regression tasks

Improving Machine Learning models performances through Feature Engineering and Feature Scaling techniques such as Principal Component Analysis (PCA), Dummy variables, Standard Scaling and Data Normalization

MLB Team Runs Allowed Prediction Project (Linear Regression)

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue which eventually leads toward the absence of an employee and provide a proper solution to reduce the absenteeism

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

Applied unsupervised learning techniques on demographic and spending data for a sample of German households.

📏 Generic feature scaling methods

Gradient Descent for N features using two datasets: Boston House data, Power Plant Data