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data_processing.py
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data_processing.py
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#importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import tensorflow as tf
#deriving the dataset
df = pd.read_csv('/content/drive/MyDrive/voice.csv')
#Reading the dataset & Pre-Processing
df.isnull().sum()
df.isna().sum()
print("gender set dimensions : {}".format(df.shape)) #Dimension of the dataset
#Visualization
df['label'].value_counts().plot.bar()
#1 for Male and 0 for Female
df.label = [1 if each == "male" else 0 for each in df.label]
#1 for Male and 0 for Female
#Splitting & Scaling the dataset
y = df['label'].copy()
X = df.drop('label', axis=1).copy() #Drop irrelevant feature
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42) #split to training and testing dataset
X.shape #After dropping the irrelevant feature we notice that we have 3168 rows and 20 columns
#Modeling, used 2 hidden layers
inputs = tf.keras.Input(shape=(X.shape[1],))
x = tf.keras.layers.Dense(64, activation='relu')(inputs)
x = tf.keras.layers.Dense(64, activation='relu')(x)
outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.Model(inputs, outputs)
model.summary()
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=[
'accuracy',
tf.keras.metrics.AUC(name='auc')
]
)
history = model.fit(
X_train,
y_train,
validation_split=0.2,
batch_size=32,
epochs=100,
callbacks=[
tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
patience=3,
restore_best_weights=True
)
]
)
model.evaluate(X_test, y_test)