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https://github.com/rafat3000/glass-classification

Classification data and using ANN model
https://github.com/rafat3000/glass-classification

adam adam-optimizer ann keras labelencoder matloptlib pandas scaler sklearn tensorflow

Last synced: 15 days ago
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Classification data and using ANN model

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README 

        
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# Glass Classification using Neural Network



## Dataset Information

The dataset used for this model is related to the classification of different types of glass based on various physical and chemical attributes. The attributes include:

- RI: Refractive Index

- Na: Sodium content

- Mg: Magnesium content

- Al: Aluminum content

- Si: Silicon content

- K: Potassium content

- Ca: Calcium content

- Ba: Barium content

- Fe: Iron content

- Type: Type of glass (Target)



## Libraries and Data Loading

```python

import pandas as pd



# Load Data 

file_path = "/kaggle/input/glass/glass.csv"

data = pd.read_csv(file_path) 

data.head()

Sample output:



RI	Na	Mg	Al	Si	K	Ca	Ba	Fe	Type

1.5210	13.64	4.49	1.10	71.78	0.06	8.75	0.0	0.0	1

1.5176	13.89	3.60	1.36	72.73	0.48	7.83	0.0	0.0	1

1.5161	13.53	3.55	1.54	72.99	0.39	7.78	0.0	0.0	1

python

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# Check distribution of the target variable

data['Type'].value_counts()

Output:



yaml

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Type

2    76

1    70

7    29

3    17

5    13

6     9

Name: count, dtype: int64

Data Preprocessing



Splitting Features and Target

python

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from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler



# Split Features and Target

X = data.drop('Type', axis=1)

y = data['Type'] - 1  # Adjust target to zero-based indexing



# Scaling features using Standard Scaler

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)



# Split data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

Building the Neural Network



Using TensorFlow and Keras to build an Artificial Neural Network (ANN) for classification.



python

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from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense



# Build Sequential model

model = Sequential()



# Add input layer and first hidden layer

model.add(Dense(128, input_dim=X_train.shape[1], activation='relu'))



# Add more hidden layers

model.add(Dense(128, activation='relu'))

model.add(Dense(128, activation='relu'))

model.add(Dense(256, activation='relu'))

model.add(Dense(128, activation='relu'))



# Add output layer with 7 units (for the 7 glass types) and softmax activation

model.add(Dense(7, activation='softmax'))



# Model summary

model.summary()

Model Compilation

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# Compile the model

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Training the Model

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# Train the model

history = model.fit(X_train, y_train, epochs=100, batch_size=10, validation_split=0.2)

Sample training output:



arduino

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Epoch 1/100

14/14 ━━━━━━━━━━━━━━━━━━━━ 2s 25ms/step - accuracy: 0.2974 - loss: 1.8536 - val_accuracy: 0.4286 - val_loss: 1.5107

Epoch 2/100

14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6038 - loss: 1.3588 - val_accuracy: 0.4857 - val_loss: 1.3663

...

Epoch 100/100

14/14 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9985 - loss: 0.0211 - val_accuracy: 0.6857 - val_loss: 4.4292

Conclusion



The model achieves a high training accuracy, though the validation accuracy suggests overfitting. Further hyperparameter tuning or regularization might improve the model's performance on unseen data.



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This Markdown structure covers the key sections of the code, including data loading, preprocessing, model building, training, and evaluation.