By using back propagation algorithm in Multilinear Regression
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Updated
Mar 15, 2018 - Jupyter Notebook
By using back propagation algorithm in Multilinear Regression
In this post, we develop a Multiple Linear Regression model in Python using the Gradient Descent Algorithm for estimating Model Coefficients to predict the prices of houses in the San Francisco Bay Area.
This is a project that contains several techniques of AI to data forecast.
STAT-420 Methods of Applied Statistics - SUMMER 2017 - UIUC
Practice machine learning from Aurelien Geron's book
Various homework assignments for multi-linear regressions
Learning is an art, Lets learn Machine Learning.
Implementing Multi-Linear Regression using R.
Data mining
This case study is for the analysis of viewers of a particular show, so that more viewers can be indulged in the show.
My Machine Learning Repository
Project for customer management in the Marketing Analytics Department of a large retail bank. The aim of this project is to know which marketing activity effectively retains customers. We have information about individual customer profitability (CLV) and a survey was conducted as well. A research model explaining/predicting individual customer p…
Colaborate work with Bernhard Hofbauer for our final project in the course "statistische methoden der datenanalyse" in the WS2020
Regression analysis of voice measurements to predict UPDRS scores for people susceptible to Parkinson's Disease
A/B test run by an e-commerce website. The company has developed a new web page to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. Your goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or per…
Jupyter Notebook kernel exploring sales of Toyota Corolla. The exercise contains various Machine Learning Linear Regression algorithms that have been used to predict the sale price of Toyota Corolla.
Calculating multilinear regression using independent variables as many as you want, then plot the prediction result against actual result in python
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