A Machine Learning Approach of Emotional Model

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

Karma of Humans is AI

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 algorithms repository

Machine Learning Notebooks

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

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

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.

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.

A Mathematical Intuition behind Linear Regression Algorithm

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

R package implementing Gradient Descent and its variants for regression tasks

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.

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

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

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

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

This is a Project which uses live weather data using API, and predicts visibility in the weather.

It's about my analysis on large and real life problem based data set available @Kaggle and applied machine learning and Deep learning techniques to build necessary model. Follow me on @Kaggle : https://www.kaggle.com/harshkothari21

Tariff is a list of expenses that incur while transporting the goods from one distance to another distance. Tariff is also dependent on seasonal and non-seasonal factors also. This project is aimed at predicting the tariff ratesfor truck load by using the different machine learning algorithms like lasso regression, elastic net regression, ridge regression and linear regression. Tariffisa combination of lot ofthings and tariff rate is dependent on some ofthe factorslikeYear, Road, SeasonalImpact, Fuel Cost,Distance, Weight, Toll charge, Demand, labour cost, travel expenses etc. Using some ofthese factors and by employing the above-mentioned machine learning regression algorithms we will be trying to predict the tariff rates on the trucks. By doing this we can help the industriesto estimate the tariffratesso that they can take the necessary actions and they can make their business run inprofitable way. This model helps small- and large-scale firms to control and manage the cost on transport.

Importing Dataset, missing data,

📏 Generic feature scaling methods

Normalizes a value according to the specified steps, using feature scaling.

Data Analysis using Unsupervised Learning on Lyft dataset

Feature-Scaling

Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor

Unsupervised Learning: Identify Target Customers