Review my code and suggest any improvements.
Add comments to the following code.

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Logistic Regression with TensorFlow.js</title>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>
    <style>
        body {
            font-family: Arial, sans-serif;
            padding: 20px;
        }

        #progressBar {
            width: 100%;
            background-color: #f3f3f3;
        }

        #progressBar div {
            height: 30px;
            width: 0;
            background-color: #4CAF50;
            text-align: center;
            line-height: 30px;
            color: white;
        }

        #chart_div {
            width: 1800px;
            height: 800px;
        }
    </style>
</head>

<body>

    <div class="container">
        <h1>Logistic Regression Predictions</h1>
        <input type="file" id="fileInput" accept=".csv">
        <label for="fileInput">Select CSV File</label>
        <div id="progressBar">
            <div></div>
        </div>
        <div id="chart_div"></div>
    </div>

    <script>
        document.getElementById('fileInput').addEventListener('change', handleFileSelect, false);
        function handleFileSelect(event) {
            try {
                const file = event.target.files[0];
                if (!file) {
                    throw new Error('No file selected');
                }
                const reader = new FileReader();
                reader.onload = function (e) {
                    try {
                        const contents = e.target.result;
                        const { data, labels, numberOfClients } = processCSV(contents);

                        const normalizedData = normalizeFeatures(data);

                        trainModel(normalizedData, labels, numberOfClients).then(model => {
                            const predictions = model.predict(normalizedData).dataSync();
                            drawChart(predictions, numberOfClients);
                        });
                    } catch (error) {
                        handleError(error);
                    }
                };
                reader.onerror = function () {
                    handleError('Failed to read file');
                };
                reader.readAsText(file);
            } catch (error) {
                handleError(error);
            }
        }
        function processCSV(csv) {
            const rows = csv.split('\n').filter(row => row.trim() !== '');
            const headers = rows[0].split(',').slice(1);

            const data = [];
            const labels = [];
            const numberOfClients = headers.length;

            for (let i = 1; i < rows.length; i++) {
                const cols = rows[i].split(',').map(value => parseFloat(value.trim()));
                data.push(cols.slice(1));
                labels.push(cols[1]);
            }

            return {
                data: tf.tensor2d(data, [data.length, data[0].length]),
                labels: tf.tensor2d(labels, [labels.length, 1]),
                numberOfClients: numberOfClients
            };
        }
        function normalizeFeatures(data) {
            const max = data.max(0);
            const min = data.min(0);
            return data.sub(min).div(max.sub(min));
        }
        async function trainModel(data, labels, numberOfClients) {
            const model = tf.sequential();
            model.add(tf.layers.dense({ inputShape: [data.shape[1]], units: 16, activation: 'relu' }));
            model.add(tf.layers.dense({ units: 8, activation: 'relu' }));
            model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
            model.compile({
                optimizer: tf.train.adam(),
                loss: 'binaryCrossentropy',
                metrics: ['accuracy'],
            });
            const progressBar = document.getElementById('progressBar').firstElementChild;
            await model.fit(data, labels, {
                epochs: 100,
                shuffle: true,
                callbacks: {
                    onEpochEnd: (epoch, logs) => {
                        const percentComplete = ((epoch + 1) / 100) * 100;
                        progressBar.style.width = percentComplete + '%';
                        progressBar.textContent = `Training: ${Math.round(percentComplete)}%`;
                    },
                    onTrainEnd: () => {
                        progressBar.textContent = 'Training completed';
                    }
                }
            });
            console.log(`Number of clients: ${numberOfClients}`);
            return model;
        }
        function drawChart(predictions, numberOfClients) {
            google.charts.load('current', { packages: ['corechart'] });
            google.charts.setOnLoadCallback(() => {
                const data = new google.visualization.DataTable();
                data.addColumn('string', 'Client');
                data.addColumn('number', 'Prediction');
                for (let i = 0; i < numberOfClients; i++) {
                    data.addRow([`Client ${i + 1}`, predictions[i]]);
                }
                const options = {
                    title: 'Client Predictions',
                    hAxis: { title: 'Client' },
                    vAxis: { title: 'Prediction', minValue: 0, maxValue: 1 },
                    legend: 'none'
                };
                const chart = new google.visualization.ScatterChart(document.getElementById('chart_div'));
                chart.draw(data, options);
            });
        }
        function handleError(error) {
            console.error(error);
            alert('An error occurred: ' + (error.message || error));
        }
    </script>

</body>

</html>