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Neural Network theory and implementation for Regression

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Introduction and background

Inthis article, we are going to build the regression model from neural networks for predicting the price of a house based on the features. Here is the implementation and the theory behind it. The neural network is basically if you see is derived from the logistic regression, as we know that in the logistic regression:

Formulae for Logistic Regression: y = ax+b

so for every node in each layer, we will apply it and after this output is from the activation function which will have the input from logistic regression and the output is output from the activation function.

So now we will implement the neural network with 5 hidden layers.

Implementation

1. Import the libraries which we will going to use

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2. Import the dataset and check the types of the columns

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3. Now build your training and test set from the dataset.

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4. Now we have our data we will now make the model and I will describe to you how it will predict the price.

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Here we are making the neural network 5 hidden layer with activation layer as `relu` and with their nodes in them. The final output is the house price.

5. Now we will fit our dataset and then predict the value.

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Here we are ready with the neural network and we can use it to predict the house price.

Points to remember for future

  1. Here you can select the number of the layer and then you can see the variance in prediction.
  2. You can also select the activation function which will result in the variance in the accuracy.

Thanks for browsing my pattern, and I hope it benefits you in theory and in practice!!!! 

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