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
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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 where threshold is 0.5
# This threshold is chosen assuming productivity above 50% is considered satisfactory
threshold = 0.5
df["productivity_class"] = (df["actual_productivity"] >= threshold).astype(int)

# Visualization to understand the distribution between the classes
sns.countplot(x="productivity_class", data=df)
plt.title("Distribution of Productivity Classes")
plt.show()

# Preprocessing: Normalize Features and Convert categorical variables into numeric
# Features are normalized to bring them onto the same scale which helps in faster convergence during gradient descent
# Categorical fields are converted using one-hot encoding to create binary columns for each category
X = pd.get_dummies(df.drop(["actual_productivity", "productivity_class"], axis=1))
y = df["productivity_class"]

# Splitting data into training and testing to evaluate the performance after learning on the training set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardizing features to have zero mean and unit variance
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Convert scaled training and testing data to TensorFlow tensors
# TensorFlow APIs prefer tensors as they utilize GPU computation efficiently 
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)

# Building a sequential model with 3 layers
# Using ‘relu" for hidden layers due to its efficiency and prevention of vanishing gradients
# Output layer uses ‘sigmoid" function as it"s well-suited for binary classification
model = tf.keras.Sequential([
    tf.keras.layers.Dense(12, activation="relu", input_shape=(X_train_scaled.shape[1],)),
    tf.keras.layers.Dense(8, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

# ‘adam" optimizer is used, which is an improvement over SGD and has adaptive learning rate capabilities
# Binary cross-entropy is chosen as it is the standard loss function for binary classification tasks
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

# Training the model on the training dataset while validating on 20% of it
history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=10, verbose=1, validation_split=0.2)

# Evaluating model"s performance on unseen test data
score = model.evaluate(X_test_scaled, y_test, verbose=1)
test_predictions = model.predict(X_test_scaled).flatten()
test_predictions = tf.round(test_predictions)

# Display detailed performance metrics
print("Test loss:", score[0])
print("Test accuracy:", score[1])
print("Precision:", precision_score(y_test, test_predictions))
print("Recall:", recall_score(y_test, test_predictions))
print("F1 Score:", f1_score(y_test, test_predictions))

accomplish these tasks: 
A.	Developing ANN using ML Libraries
3.1	Retrieve the datasets you prepared in Part 1 above.
3.2	Construct, train and test a multilayer feedforward neural network with the same datasets prepared in Part 1 using a selected machine learning library, like scikitlearn or Keras.  Detail the hyperparameters and training rules/algorithms you adopted in the report. Compare the performance of this model with the neural network classifier you trained in Part 1 above.