Help me with developing a predictive regression model using Genetic Algorithm and Neural Network in Matlab.

Hello – I am having a PhD in engineering from a top university and have more than 20 years’ experience in Matlab simulations and done work in the area of regression, neural networks, optimization, etc. I am highly qualified for this project and would like to speak with you further about taking this project on. If you'd like to view my previous work along with excellent feedback from my employers, take a look at my Freelancer Portfolio. Please provide details of your work. Thanks.

Hi! My name is Fernando Téllez. I am a senior year student of electrical engineering at Universidad Simón Bolívar (USB), one of the most prestigious universities in my country (Ranked 34° at the QS University Rankings: Latin America 2015). I code using matlab ON A DAILY BASIS to solve any kind of engineering, logical, statistical and mathematical problem.

I have well experienced in doing such kind of jobs.............................................................................................................................................................

Hello , I am an expert at MATLAB and Netural Networks and can help you in analysing the data. Please ping me for dicussion. Thanks

Highly experienced with several machine learning and computer vision algorithms such linear regression, neural networks, SVM, random forest and so on and optimization method such as genetic algorithm and PSO. Also I have used matlab for most projects since 2010.

My preferred method of freelancing is an interactive approach to project solving. I have an MSEE specializing in Digital Signal/Image/RF Processing. I do my work in MATLAB (expert). I am also an expert console C programmer. I am also a novice Python programmer.

Ok, We can train the neural network to predict the output, the optimum value for the linear model coefficient will be returned after training. The efficiency of the model will be shown on several test data. Moreover, the code will be commented in details and divided into sections to facilitate your further use.