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Implement Back-Propagation Algorithm for Classification Problems

In this machine learning project, we will implement Back-propagation Algorithm from scratch for classification problems.

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What will you learn

  • How to forward-propagate an input to calculate an output.
  • How to back-propagate error and train a network.
  • How to apply the back-propagation algorithm to a real-world predictive modeling problem.
  • How to develop an algorithm from scratch
  • How to implement back-propagation algorithm, which is the classical feed-forward artificial neural network.

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, so in this machine learning project we present the novel Sorting Smoothing Method to estimate the real probability of default.

With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.