# Install TensorFlow version 2.3.0 using pip
!pip install tensorflow==2.3.0

# Import necessary libraries
import pandas as pd # Importing pandas library and assigning it an alias "pd"
from sklearn.model_selection import train_test_split # Importing train_test_split function from sklearn.model_selection library
from keras.models import Sequential # Importing Sequential model from keras library
from keras.layers import Dense # Importing Dense layer from keras library
from tensorflow.keras.callbacks import EarlyStopping # Importing EarlyStopping callback from tensorflow.keras.callbacks library

# Load the dataset (assuming you have a CSV file named 'data.csv')
# Import the necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split

# Load the dataset into a pandas dataframe
df = pd.read_csv('data.csv', index_col=0)

# Split the dataset into features (X) and target variable (y)
# The 'target' column is dropped from the features and assigned to the target variable
X = df.drop(columns='target').values
y = df['target'].values

# Split the data into training and testing sets
# The data is split into 80% for training and 20% for testing
# The training and testing sets for both features and target variable are assigned
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Define the model architecture (using Sequential API)
model = Sequential() # Creates a sequential model object

# Add input layer with 10 neurons
model.add(Dense(units=10, activation='relu', input_shape=(X.shape[1],))) # Adds a dense layer with 10 neurons, ReLU activation function, and input shape based on the number of features in X

# Add hidden layer with 5 neurons and ReLU activation function
model.add(Dense(units=5, activation='relu')) # Adds a dense layer with 5 neurons and ReLU activation function

# Add output layer (regression) with linear activation function
model.add(Dense(units=1)) # Adds a dense layer with 1 neuron and linear activation function for regression

# Compile the model using Adam optimizer and binary cross-entropy loss function
model.compile(optimizer='adam', loss='binary_crossentropy') # Changed loss function to binary cross-entropy for binary classification

# Set up early stopping (stop training if validation loss doesn't improve for 5 epochs)
es = EarlyStopping(monitor='val_loss', patience=5) # Added import statement for EarlyStopping and changed monitor to 'val_loss' for validation loss

# Train the model on X_train and y_train with a batch size of 32 for 100 epochs (or until early stopping is triggered)
# Define the number of epochs to train the model for
epochs = 100
# Define the batch size for each training iteration
batch_size = 32
# Define the percentage of data to use for validation during training
validation_split = 0.3
# Define the early stopping callback to prevent overfitting
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)
# Train the model using the fit() function, passing in the training data, number of epochs, batch size, verbosity, validation split, and callbacks
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0, validation_split=validation_split, callbacks=[es])