atifkarim/code.html

## code.html
<!DOCTYPE html>
<html>
<head>
<title>Chart using XML Data</title>
<script type="text/javascript" src="https://canvasjs.com/assets/script/jquery-1.11.1.min.js"></script>
<script type="text/javascript" src="https://canvasjs.com/assets/script/canvasjs.min.js"></script>
<script type="text/javascript">
    window.onload = function() {
		  var dataPoints = [];
		//   var dataPoints_1 = [];

        function getDataPointsFromCSV(csv) {
            var dataPoints = csvLines = points = [];
            csvLines = csv.split(/[\r?\n|\r|\n]+/);

            for (var i = 0; i < csvLines.length; i++)
                if (csvLines[i].length > 0) {
                    points = csvLines[i].split(",");
                    dataPoints.push({
                        x: parseFloat(points[0]),
								y: parseFloat(points[1])
                    });
                }
            return dataPoints;
		  }

		  function getDataPointsFromCSV_1(csv) {
            var dataPoints = csvLines = points = [];
            csvLines = csv.split(/[\r?\n|\r|\n]+/);

            for (var i = 0; i < csvLines.length; i++)
                if (csvLines[i].length > 0) {
                    points = csvLines[i].split(",");
                    dataPoints.push({
                        x: parseFloat(points[0]),
								// y: parseFloat(points[1]),
								y: parseFloat(points[2])
                    });
                }
            return dataPoints;
        }

	$.get("https://raw.githubusercontent.com/atifkarim/Time-Series-Forecasting-Using-Machine-Learning-Algorithm/develop/csv_column_2.csv", function(data) {
	    var chart = new CanvasJS.Chart("chartContainer", {
		    title: {
		         text: "Chart from CSV",
		    },
		    data: [{
					type: "line",
		         dataPoints: getDataPointsFromCSV(data)
				},
				{
		         type: "line",
		         dataPoints: getDataPointsFromCSV_1(data)
		      }
			]
	     });

	      chart.render();

	});
  }
</script>
</head>
<body>
	<div id="chartContainer" style="width:100%; height:300px;"></div>
</body>
</html>
	<!DOCTYPE html>
	<html>
	<head>
	<title>Chart using XML Data</title>
	<script type="text/javascript" src="https://canvasjs.com/assets/script/jquery-1.11.1.min.js"></script>
	<script type="text/javascript" src="https://canvasjs.com/assets/script/canvasjs.min.js"></script>
	<script type="text/javascript">
	window.onload = function() {
	var dataPoints = [];
	// var dataPoints_1 = [];

	function getDataPointsFromCSV(csv) {
	var dataPoints = csvLines = points = [];
	csvLines = csv.split(/[\r?\n\|\r\|\n]+/);

	for (var i = 0; i < csvLines.length; i++)
	if (csvLines[i].length > 0) {
	points = csvLines[i].split(",");
	dataPoints.push({
	x: parseFloat(points[0]),
	y: parseFloat(points[1])
	});
	}
	return dataPoints;
	}

	function getDataPointsFromCSV_1(csv) {
	var dataPoints = csvLines = points = [];
	csvLines = csv.split(/[\r?\n\|\r\|\n]+/);

	for (var i = 0; i < csvLines.length; i++)
	if (csvLines[i].length > 0) {
	points = csvLines[i].split(",");
	dataPoints.push({
	x: parseFloat(points[0]),
	// y: parseFloat(points[1]),
	y: parseFloat(points[2])
	});
	}
	return dataPoints;
	}

	$.get("https://raw.githubusercontent.com/atifkarim/Time-Series-Forecasting-Using-Machine-Learning-Algorithm/develop/csv_column_2.csv", function(data) {
	var chart = new CanvasJS.Chart("chartContainer", {
	title: {
	text: "Chart from CSV",
	},
	data: [{
	type: "line",
	dataPoints: getDataPointsFromCSV(data)
	},
	{
	type: "line",
	dataPoints: getDataPointsFromCSV_1(data)
	}
	]
	});

	chart.render();

	});
	}
	</script>
	</head>
	<body>
	<div id="chartContainer" style="width:100%; height:300px;"></div>
	</body>
	</html>