How to do Kernel regression by hand in Python .Kernel Regression in Python
Black Friday — A Detailed Analysis & Prediction using Visualization and XGBoost. Nand Lal Mishra.
Multiple Linear Regression — What and Why? Multiple linear regression is a model for predicting the value of one dependent variable based on two or more independent variables.
In Supervised Learning, we mostly deal with two types of variables i.e numerical variables and categorical variables.
Real Estate Sale Prices, Regression, and Classification: Data Science is the Future of Fortune Telling. Well algorithms and machine learning are a perfect example of modern fortune telling in practice.
Data Processing Example using Python. Just some of the steps involved in prepping a dataset for analysis and machine learning.
Understanding the math behind Logistic Regression, creating a model by hand. This is the first article in a series of articles where we will understand the “under the hood” workings of various ML algorithms, using their base math equations.
Deep-dive into regression. Often times in the workplace, business stakeholders and managers associate machine learning and big data with deep learning.
Regression Analysis: Regression analysis is a reliable method in statistics to determine whether a certain variable is influenced by certain other(s).
The problem we will tackle is predicting the average global land and ocean temperature using over 100 years of past weather data. We are going to act as if we don’t have access to any weather forecasts.
This post is about the ordinary least square method (OLS) for simple linear regression. If you are new to linear regression, read this article for getting a clear idea about the implementation of simple linear regression. This post will help you to understand how simple linear regression works step-by-step. The simple linear regression is a model with a single regressor (independent variable) x that has a relationship with a response (dependent or target) y that is
Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. Positive Correlation indicates the extent to which those variable increase.
Variable Selection in Regression Analysis with a Large Feature Space: Feature selection in regression analysis using Fisher z-transformations of correlation coefficients and Euclidian distance.
How to use SVMs across regression problems. It is a common misconception that support vector machines are only useful when solving classification problems.
Since recent years, data have been playing an important role in the world by increasing access to datasets and computers that are able to process big datasets in minutes.
Do robust regressions always outperform OLS when analysing outliers? When it comes to regression analysis — outliers.
One way to predict home prices is to become an ascended spiritual master who is able to see all timelines and dimensions, astral travel to the exact time.
Example1: We have a data set from a hotel data with GDP of Singapore during the year 2014 to 2019. We will use regression analysis with seasonal fluctuation to predict the GDP for the year 2020.
This article is about applications of analytics used by Leading Firms Kohl’s Department Store is a very large national chain department store. They’re doing what’s called Smartphone Targeting.
Whether you are new to data science or even an experienced veteran, interpreting the results of a machine learning algorithm can be a challenge. People tend to ignore OLS assumptions before interpreting the results of this. Therefore, this is an important step for analysing various statistics released by OLS.