does this code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import tensorflow as tf

# Load the dataset
csv_file_path = "garments_worker_productivity.csv"
df = pd.read_csv(csv_file_path)

# Convert "actual_productivity" to a binary classification 
threshold = 0.5
df["productivity_class"] = (df["actual_productivity"] >= threshold).astype(int)

# Visualization of class distribution
sns.countplot(x="productivity_class", data=df)
plt.title("Distribution of Productivity Classes")
plt.show()

# Data preprocessing
X = pd.get_dummies(df.drop(["actual_productivity", "productivity_class"], axis=1))
y = df["productivity_class"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# TensorFlow setup
X_train_scaled = tf.convert_to_tensor(X_train_scaled, dtype=tf.float32)
y_train = tf.convert_to_tensor(y_train, dtype=tf.int32)
X_test_scaled = tf.convert_to_tensor(X_test_scaled, dtype=tf.float32)
y_test = tf.convert_to_tensor(y_test, dtype=tf.int32)

# Neural Network Model setup
model = tf.keras.Sequential([
    tf.keras.layers.Dense(16, activation="relu", input_shape=(X_train_scaled.shape[1],)),
    tf.keras.layers.Dropout(0.3),  # Adding dropout to reduce overfitting
    tf.keras.layers.Dense(12, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

# Optimizer with a custom learning rate
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

# Compile model
model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"])

# Learning rate scheduler
def scheduler(epoch, lr):
    if epoch < 10:
        return float(lr)  # Explicitly convert to float
    else:
        new_lr = lr * tf.math.exp(-0.1)
        return float(new_lr)  # Also explicitly convert to float

callback = tf.keras.callbacks.LearningRateScheduler(scheduler)

# Model training
history = model.fit(
    X_train_scaled, 
    y_train, 
    epochs=500,
    batch_size=32,
    verbose=1, 
    validation_split=0.2,
    callbacks=[callback]
)

# Plot training history
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.plot(history.history["accuracy"], label="Train")
plt.plot(history.history["val_accuracy"], label="Validation")
plt.title("Model Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend(loc="upper left")

plt.subplot(1, 2, 2)
plt.plot(history.history["loss"], label="Train")
plt.plot(history.history["val_loss"], label="Validation")
plt.title("Model Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend(loc="upper left")

plt.tight_layout()
plt.show()

# Evaluate model
score = model.evaluate(X_test_scaled, y_test, verbose=0)

# Test Predictions and Evaluation
test_predictions = model.predict(X_test_scaled).flatten()
test_predictions = tf.round(test_predictions)

print("Test Accuracy:", score[1])
from sklearn.metrics import precision_score, recall_score, f1_score
print("Precision:", precision_score(y_test.numpy(), test_predictions.numpy()))
print("Recall:", recall_score(y_test.numpy(), test_predictions.numpy()))
print("F1 Score:", f1_score(y_test.numpy(), test_predictions.numpy()))


complete the task:
B.	Parameter Tuning
3.3	Tune the parameters or training rules/algorithms of your neural network model and re-train your model so that testing performance of the model is improved.