NeuralNetworks Regressor

Build a Neural Network Regressor to determine signal strength of an manufacturing equipment

Objective of this notebook

• The purpose of this notebook is to build a Neural Network Regressor to determine signal strength of an manufacturing equipment
• Details of the problem statement , data set , input screenshot , summary of the code/solution and final result of the project are listed in the sections to follow.

Problem Statement

A communications equipment manufacturing company has a product which is responsible for emitting informative signals. Company wants to build a machine learning model which can help the company to predict the equipment’s signal quality using various parameters.

Data Description:

The data set contains information on various signal tests performed:

• Parameters: Various measurable signal parameters.
• Signal_Quality: Final signal strength or quality

Domain:

Electronics and Telecommunication

Sample Input

Below shows a screen shot of the input data

Summary of the Solution/Code:

The code aims at building a Neural Network Regressor

• We begin by doing an Exploratory Data analyses which involves univariate, bivariate and multivariate analysis
• We then perform pre-processing on the data to remove outliers and treat null values
• We contiue pre-processing of the data to prepare it so it can be fed into a Neural Network which involves normalising the input data
• We then begin the process of building a Neural Network model
• We bein with a basic Neural Network and capture a baseline "mae" score(chosen metric for regression analysis is MAE)
• Next we start "tuning" the model in terms of no of hiden layers & try different activation functions and capture scores for each case
• Finally we compare the test/validation scores for all the various models built above and choose the best contender
• Refer python worksheet Project_RegressionUsingNeuralNetworks_ElectronAndTelDomain.ipynb for the solution

Result