How to Predict Students Gender Using Height and Weight Data with Deep Neural Networks in Keras and Python?
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Condition for Predicting Students Gender Using Height and Weight Data with Deep Neural Networks in Keras and Python
Description: This code implements a deep learning model to predict a student's gender based on their height and weight using an artificial neural network (ANN) in TensorFlow. The dataset is preprocessed by encoding gender labels, splitting data into train-test sets, and handling class balance.
Step-by-Step Process
Step1: Import necessary libraries such as pandas, TensorFlow, scikit-learn,and visualization tools (matplotlib, seaborn).
Step2: Read the dataset (weight-height.csv) using pandas and display the first few rows to understand its structure.
Step3: Check if there are any missing values in the dataset using isnull().sum() to ensure data quality.
Step4: Encode the categorical Gender column into numeric values using LabelEncoder for compatibility with the model.
Step5: Split the data into input features (x with height and weight) and output labels (y with gender).
Step6: Calculate and visualize the distribution of the target variable Gender using a bar plot to check for class imbalance.
Step7: Split the data into training and testing sets using train_test_split,ensuring the model has separate data for training and evaluation.
Step8: Define the architecture of the ANN with an input layer,two hidden layers (with ReLU activation), and a sigmoid output layer for binary classification.
Step9: Train the model using the training set with the Adam optimizer and binary cross-entropy loss, evaluating performance on the validation set.
Step10: Make predictions on the test set, calculate performance metrics(accuracy, F1-score, precision, recall), and display the classification report and confusion matrix.
Sample Code
#Import Necessary Libraries
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow.keras.layers import Dense,Dropout,Input
from tensorflow.keras.models import Model
from sklearn.metrics import (classification_report,confusion_matrix,accuracy_score,
f1_score,recall_score,precision_score)
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv("/home/soft12/Downloads/sample_dataset/Website/Dataset/weight- height.csv")
#Check null values in a dataset
df.isnull().sum()
label = LabelEncoder()
df['Gender'] = label.fit_transform(df['Gender'])
x = df.drop(['Gender'],axis=1)
y = df['Gender']
#check class imbalance
class_counts = y.value_counts()
# Plot the class distribution
plt.figure(figsize=(8, 6))
sns.barplot(x=class_counts.index, y=class_counts.values, palette="viridis")
plt.title('Class Balance Check', fontsize=16)
plt.xlabel('Class', fontsize=14)
plt.ylabel('Count', fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=.2,random_state=42)
def ANN_model(input_shape):
# Input layer
inputs = Input(shape=(input_shape,))
# Hidden layers
layer1 = Dense(64, activation='relu')(inputs)
Dropout1 = Dropout(0.2)(layer1)
layer2 = Dense(32, activation='relu')(Dropout1)
Dropout2 = Dropout(0.2)(layer2)
# Output layer
output_layer = Dense(1, activation='sigmoid')(Dropout2)
# Build the model
ann_model = Model(inputs=inputs, outputs=output_layer)
# Compile the model with Adam optimizer and binary crossentropy loss function
ann_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return ann_model
model = ANN_model(X_train.shape[1])
model.fit(X_train,y_train,batch_size=2,epochs=10,validation_data=(X_test,y_test))
y_pred = model.predict(X_test)
y_pred = [1 if i>0.5 else 0 for i in y_pred]
print("___Performance_Metrics___\n")
print('Classification_Report:\n',classification_report(y_test, y_pred))
print('Confusion_Matrix:\n',confusion_matrix(y_test, y_pred))
print('Accuracy_Score: ',accuracy_score(y_test, y_pred))
print('F1_Score: ',f1_score(y_test, y_pred))
print('Recall_Score: ',recall_score(y_test, y_pred))
print('Precision_Score: ',precision_score(y_test, y_pred))
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