This repository hosts the development of the Artificial Neural Network library.
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
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.
| 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 |
You can ask questions and join the development discussion: