Artificial Neural Network

This repository hosts the development of the Artificial Neural Network library.

About Artificial Neural Network

Artificial Neural Network, is a deep learning API written in Python. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

Artificial Neural Network is:

• Simple
• Flexible
• Powerful

First contact with Artificial Neural Network

The core data structures of Artificial Neural Network are consign and result. It implement four model in two layer neural network for helping you fast build predictor.

For installation run :

pip install Artificial-Neural-Network-Classifier

Here is an exemple :

from Artificial_Neural_Network_Classifier import artificialneuralnetwork_classifier
import pandas as pd
import numpy as np

# Reading and cleaning dataset form a CSV file

df = pd.read_csv('admission_data.csv')
df = df.apply(pd.to_numeric, errors='coerce')
df = df.dropna()

# Select X dataset (consign) and convert them in numpy matrix 
x = np.matrix(df[["GRE Score","TOEFL Score","University Rating","SOP","LOR ","CGPA"]].to_numpy() )

# Select Y dataset (response) and convert them in numpy matrix 
y = np.matrix(df[["Research"]].to_numpy())

# Train the model
ANN = artificialneuralnetwork_classifier(x,y)

Let make prediction

X = np.matrix([[318,110,3,4,3,8.8] ])
print(Ann.predict(X))

It is a binairy classifier. Mean that your response should be 0 or 1. And your dataset response may also be binary.

Admission data used for this exemple :

GRE Score	TOEFL Score	University Rating	SOP	LOR	CGPA	Research
337	118	4	4.5	4.5	9.65	1
324	107	4	4	4.5	8.87	1
316	104	3	3	3.5	8	1
322	110	3	3.5	2.5	8.67	1
314	103	2	2	3	8.21	0
330	115	5	4.5	3	9.34	1
321	109	3	3	4	8.2	1
308	101	2	3	4	7.9	0
302	102	1	2	1.5	8	0
323	108	3	3.5	3	8.6	0

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Support

You can ask questions and join the development discussion:

• Facebook page .

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