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https://github.com/ashwanikumar04/artificial-neural-network

A simple artifical neural network
https://github.com/ashwanikumar04/artificial-neural-network

Last synced: 12 days ago
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A simple artifical neural network

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README 

        
This is based on the content provided [here](https://www.udemy.com/deeplearning/learn/v4/overview) and simplified with comments for personal understanding.

This is a simple ANN which predicts if a customer will leave the bank or not.



## Data loading

We will use Pandas to load the data in csv



```python

import pandas as pd

dataset = pd.read_csv("data.csv")



#We are taking only the independent variables which impact the dependent variable (i.e. if the customer leaves the banks)

X = dataset.iloc[:,3:13].values



y = dataset.iloc[:,13].values

```



## Encoding

Now, we need to encode categorical data, as in mathematical equations we need to use only numerical values.

We encode all the independent variables which are non numeric.

In our current case, we need to encode ```Geography``` and ```Gender```



```python

# Encoding categorical values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X_1 = LabelEncoder()

X[:,1]= labelencoder_X_1.fit_transform(X[:,1])

labelencoder_X_2 = LabelEncoder()

X[:,2]= labelencoder_X_2.fit_transform(X[:,2])



```



We need to include dummy variables to avoid un-necessary preference to any encoded value as all the variables are of same importance.



```python

onehotencoder = OneHotEncoder(categorical_features=[1])

X = onehotencoder.fit_transform(X).toarray()

```



### Avoid dummy variable trap

We need to avoid dummy variable trap. More info on dummy variable trap is [here](http://www.algosome.com/articles/dummy-variable-trap-regression.html)



```python

#Done to avoid Dummy variable trap. Reducing dummy variable by one

X = X[:,1:]

```



## Data split

Splitting the data in training and test set



```python

#Splitting the dataset into the training and test set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)



```



## Feature Scaling

Feature scaling is done to avoid dominance of one independent variable on others.

We need to fit and transform the training dataset.

But test dataset is only transformmed.



```python

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

```



## Artificial Neural Network

Now we will create ANN with following approach

- Randomly initialise the weights to small numbers close to 0 (but not 0)

- Input the first observation of your dataset in the input layer, each feature in one input node.

- Forward-Propagation from left to right, the neurons are activated in a way that the impact of each neuron's activation is limited by the weights. Propagate the activations until getting the predicted result y.

- Compare the predicted result to the actual result. Measure the generated error.

- Back-Progpagation: from right to left, the error is back-propagated so that the weights are updated accordingly.

- Repeat Steps 1-5 till whole data set is exhausted.



```python

#ANN 

import keras

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import Dropout

classifier = Sequential()



#Adding the input layer

#Number of nodes in hiddedn layer = average of number of nodes in input layer and number of nodes in output layer

#It is recommended to use RELU for hidden layers and Sigmoid (for two categories)/Softmax (for more than two categories) for output layer

classifier.add(Dense(activation="relu", input_dim=11, units=6, kernel_initializer="uniform"))

classifier.add(Dropout(rate=0.1))

#Adding second hidden layer 

classifier.add(Dense(activation="relu", units=6, kernel_initializer="uniform"))

#Adding Dropout to avoid overfitting

classifier.add(Dropout(rate=0.1))

#Adding output layer 

classifier.add(Dense(activation="sigmoid", units=1, kernel_initializer="uniform"))



# Compiling the ANN

# If dependent variable is binary then loss = binary_crossentropy else loss = categorical_crossentropy

classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

classifier.fit(X_train,y_train,batch_size=10,epochs=100)

y_pred = classifier.predict(X_test)

y_pred = (y_pred>0.5)



```



    Epoch 1/100

    8000/8000 [==============================] - 2s - loss: 0.4904 - acc: 0.7954     

    Epoch 2/100

    8000/8000 [==============================] - 2s - loss: 0.4357 - acc: 0.7960     

    Epoch 3/100

    8000/8000 [==============================] - 2s - loss: 0.4333 - acc: 0.7960     

    Epoch 4/100

    8000/8000 [==============================] - 2s - loss: 0.4330 - acc: 0.7960     

    Epoch 5/100

    8000/8000 [==============================] - 2s - loss: 0.4277 - acc: 0.7960     

    .

    .

    . 

    Epoch 95/100

    8000/8000 [==============================] - 2s - loss: 0.4204 - acc: 0.8295     

    Epoch 96/100

    8000/8000 [==============================] - 2s - loss: 0.4206 - acc: 0.8306     

    Epoch 97/100

    8000/8000 [==============================] - 2s - loss: 0.4219 - acc: 0.8325     

    Epoch 98/100

    8000/8000 [==============================] - 2s - loss: 0.4209 - acc: 0.8305     

    Epoch 99/100

    8000/8000 [==============================] - 2s - loss: 0.4194 - acc: 0.8304     

    Epoch 100/100

    8000/8000 [==============================] - 2s - loss: 0.4206 - acc: 0.8300     



```python

import numpy as np

new_prediction = classifier.predict(sc.transform(np.array([[0.,0,600,1,40,3,60000,2,1,1,50000]])))

new_prediction=(new_prediction>0.5)

print("The customer will leave" if new_prediction else"The customer will not leave")

```



    The customer will not leave



This is a simple ANN to determine if a customer will leave the bank or not.