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TrainImprove.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import accuracy_score
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
+from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
+from tensorflow.keras.regularizers import l2
+from joblib import dump
+
+# 1. Read Data
+data = pd.read_excel('gender.xlsx')
+
+# 2. Preprocess Data
+data['Gender'] = data['Gender'].map({'M': 1, 'F': 0})
+
+# 3. Convert text data into numerical data using TF-IDF
+tfidf = TfidfVectorizer(analyzer='char', ngram_range=(1, 3))
+X = tfidf.fit_transform(data['Name']).toarray()  # Convert names into numerical features
+y = data['Gender'].values  # Labels: 1 for Male, 0 for Female
+
+# 4. Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+# 5. Build the Neural Network Model
+model = Sequential()
+model.add(Dense(128, activation='relu', kernel_regularizer=l2(0.01), input_shape=(X_train.shape[1],)))  # L2 regularization
+model.add(BatchNormalization())  # Batch normalization
+model.add(Dropout(0.5))  # Dropout to prevent overfitting
+model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.01)))  # L2 regularization
+model.add(BatchNormalization())  # Batch normalization
+model.add(Dropout(0.5))  # Dropout to prevent overfitting
+model.add(Dense(1, activation='sigmoid'))  # Output layer with sigmoid for binary classification
+
+# 6. Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# 7. Define callbacks
+early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)  # Early stopping
+reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.00001)  # Learning rate reduction
+
+# 8. Train the model with epochs and callbacks
+model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2, 
+          callbacks=[early_stopping, reduce_lr])
+
+# 9. Save the model after training
+model.save('gender_prediction_model_Improve.h5')
+
+# 10. Save the TF-IDF vectorizer
+dump(tfidf, 'tfidf_vectorizer_Improve.joblib')
+
+# 11. Evaluate the model
+y_pred = (model.predict(X_test) > 0.5).astype("int32")  # Convert probabilities to binary output
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Model Accuracy: {accuracy * 100:.2f}%")

ml-st1.py

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+import streamlit as st
+import os
+import pandas as pd
+from tensorflow.keras.models import load_model
+from joblib import load
+
+# Set Streamlit page configuration
+st.set_page_config(page_title="Gender Prediction", page_icon="🧑‍🎓", layout="centered")
+
+# Load the pre-trained model
+@st.cache_resource
+def load_prediction_model():
+    return load_model('gender_prediction_model.h5')
+
+# Load the TF-IDF vectorizer
+@st.cache_resource
+def load_vectorizer():
+    tfidf_vectorizer_file = 'tfidf_vectorizer.joblib'
+    if not os.path.exists(tfidf_vectorizer_file):
+        st.error(f"❌ {tfidf_vectorizer_file} not found. Please ensure the file exists in the current directory.")
+        st.stop()
+    return load(tfidf_vectorizer_file)
+
+# Prediction function
+def predict_gender(name, model, tfidf):
+    vectorized_name = tfidf.transform([name]).toarray()  # Transform name into feature vector
+    gender = model.predict(vectorized_name) > 0.5  # Get prediction
+    return 'Male' if gender[0][0] == 1 else 'Female'
+
+# Load model and vectorizer
+model = load_prediction_model()
+tfidf = load_vectorizer()
+
+# Streamlit UI
+st.title("Gender Prediction from Name")
+st.write("Enter a name to predict the gender using the pre-trained model.")
+
+# Input form
+name = st.text_input("Enter a name:")
+if st.button("Predict"):
+    if name:
+        predicted_gender = predict_gender(name, model, tfidf)
+        st.success(f"The predicted gender for '{name}' is: **{predicted_gender}**")
+    else:
+        st.warning("Please enter a valid name.")