Virtual ML.Net: Part-1 COVID-19 Data Analysis using .Net DataFrame API

Part-1: COVID-19 Data Analysis using .Net DataFrame API

COVID-19

• As per Wiki Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, the capital of China’s Hubei province, and has since spread globally, resulting in the ongoing 2019–20 coronavirus pandemic.
• The virus had caused a pandemic across the globe and spreading/affecting most of the nations.
• The purpose of notebook is to visualize the trends of virus spread in various countries and explore features present in ML.Net such as DataFrame.

Acknowledgement

• Johns Hopkins CSSE for dataset
• COVID-19 data visualization by Akshay Sb

Dataset

• 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE - Daily reports.

Introduction

DataFrame: DataFrame is a new type introduced in .Net. It is similar to DataFrame in Python which is used to manipulate data in notebooks. It’s a collection of columns containing data similar to a table and very helpful in analyzing tabular data. It works flawlessly without creating types/classes mapped to columns in a table which we used to do with ML.Net. It has support for GroupBy, Sort, Filter which makes analysis very handy. It’s a in-memory representation of structured data.

In this tutorial we’ll cover below features

• Load a CSV
• Metadata
  • Description
  • Info
• Display records
  • Head
  • Sample
• Filtering
• Grouping
• Aggregate

For overview, please refer below links

• An Introduction to DataFrame
• Exploring the C# Dataframe API

Part-2 covers time series analysis and prediction using ML.Net

Summary

Below is the summary of steps we’ll be performing

1. Define application level items
   • Nuget packages
   • Namespaces
   • Constants
2. Utility Functions
   • Formatters
3. Load Dataset
   • Download Dataset from Johns Hopkins CSSE
   • Load dataset in DataFrame
4. Analyze Data
   • Date Range
   • Display Dataset - display(dataframe)
   • Display Top 5 Rows - dataframe.Head(5)
   • Display Random 6 Rows - dataframe.Sample(6)
   • Display Dataset Statistics - dataframe.Description()
   • Display Dataset type information - dataframe.Info()
5. Data Cleaning
   • Remove Invalid cases
6. Data Visualization
   • Global
     • Confirmed Vs Deaths Vs Recovered
     • Top 5 Countries with Confirmed cases
     • Top 5 Countries with Death cases
     • Top 5 Countries with Recovered cases
   • India
     • Confirmed Vs Deaths Vs Recovered

Note : Graphs/Plots may not render in GitHub due to security reasons, however if you run this notebook locally/binder they will render.

1. Define Application wide Items

Nuget Packages

// ML.NET Nuget packages installation
#r "nuget:Microsoft.ML"
#r "nuget:Microsoft.Data.Analysis"

// Install XPlot package
#r "nuget:XPlot.Plotly"
    
// CSV Helper Package for reading CSV
#r "nuget:CsvHelper"

Installed package XPlot.Plotly version 3.0.1
Installed package Microsoft.Data.Analysis version 0.4.0
Installed package CsvHelper version 15.0.5
Installed package Microsoft.ML version 1.5.0

Namespaces

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.Data.Analysis;
using Microsoft.AspNetCore.Html;
using System.IO;
using System.Net.Http;
using System.Globalization;
using CsvHelper;
using CsvHelper.Configuration;
using XPlot.Plotly;

Constants

// Column Names
const string FIPS = "FIPS";
const string ADMIN = "Admin2";
const string STATE = "Province_State";
const string COUNTRY = "Country_Region";
const string LAST_UPDATE = "Last_Update";
const string LATITUDE = "Lat";
const string LONGITUDE = "Long_";
const string CONFIRMED = "Confirmed";
const string DEATHS = "Deaths";
const string RECOVERED = "Recovered";
const string ACTIVE = "Active";
const string COMBINED_KEY = "Combined_Key";

// File
const string DATASET_FILE = "05-27-2020";
const string FILE_EXTENSION = ".csv";
const string NEW_FILE_SUFFIX = "_new";
const char SEPARATOR = ',';
const char SEPARATOR_REPLACEMENT = '_';
const string DATASET_GITHUB_DIRECTORY = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/";

// DataFrame/Table
const int TOP_COUNT = 5;
const int DEFAULT_ROW_COUNT = 10;
const string VALUES = "Values";
const string INDIA = "India";

2. Utility Functions

Formatters

By default the output of DataFrame is not proper and in order to display it as a table, we need to have a custom formatter implemented as shown in next cell.

// Formats the table

Formatter<DataFrame>.Register((df, writer) =>
{
    var headers = new List<IHtmlContent>();
    headers.Add(th(i("index")));
    headers.AddRange(df.Columns.Select(c => (IHtmlContent) th(c.Name)));
    var rows = new List<List<IHtmlContent>>();
    var take = DEFAULT_ROW_COUNT;
    for (var i = 0; i < Math.Min(take, df.Rows.Count); i++)
    {
        var cells = new List<IHtmlContent>();
        cells.Add(td(i));
        foreach (var obj in df.Rows[i])
        {
            cells.Add(td(obj));
        }
        rows.Add(cells);
    }

    var t = table(
        thead(
            headers),
        tbody(
            rows.Select(
                r => tr(r))));

    writer.Write(t);
}, "text/html");

Copy dataset csv and replace Separator in cells

// Replace a characeter in a cell of csv with a defined separator
private void CreateCsvAndReplaceSeparatorInCells(string inputFile, string outputFile, char separator, char separatorReplacement)
{
    var culture = CultureInfo.InvariantCulture;
    using var reader = new StreamReader(inputFile);
    using var csvIn = new CsvReader(reader, new CsvConfiguration(culture));
    using var recordsIn = new CsvDataReader(csvIn);
    using var writer = new StreamWriter(outputFile);
    using var outCsv = new CsvWriter(writer, culture);

    // Write Header
    csvIn.ReadHeader();
    var headers = csvIn.Context.HeaderRecord;
    foreach (var header in headers)
    {
        outCsv.WriteField(header.Replace(separator, separatorReplacement));
    }
    outCsv.NextRecord();

    // Write rows
    while (recordsIn.Read())
    {
        var columns = recordsIn.FieldCount;
        for (var index = 0; index < columns; index++)
        {
            var cellValue = recordsIn.GetString(index);
            outCsv.WriteField(cellValue.Replace(separator, separatorReplacement));
        }
        outCsv.NextRecord();
    }
}

3. Load Dataset

Download Dataset from Johns Hopkins CSSE

We’ll be using COVID-19 dataset from Johns Hopkins CSSE. The csse_covid_19_data directory has .csv file for each day and we’ll be performing analysis on latest file present. Latest file present at the time of last modification of this notebook was 05-27-2020.csv. If you wish to use a different file, update DATASET_FILE constant in Constants cell above.

We’ll download file to current directory.

// Download csv from github
var originalFileName = $"{DATASET_FILE}{FILE_EXTENSION}";
if (!File.Exists(originalFileName))
{
    var remoteFilePath = $"{DATASET_GITHUB_DIRECTORY}/{originalFileName}";
    display(remoteFilePath);
    var contents = new HttpClient()
        .GetStringAsync(remoteFilePath).Result;
        
    File.WriteAllText(originalFileName, contents);
}

https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports//05-27-2020.csv

Load dataset in DataFrame

Issue: We can load csv using LoadCsv(..) method of DataFrame. However, there is an issue of not allowing quotes and separator(comma in this case) in a cell value. The dataset, we are using has both of them and LoadCsv fails for it. As a workaround, we’ll use CSVHelper to read the csv file and replace command separator with underscore, save the file and use it to load in DataFrame LoadCsv(..) method.

// Load and create a copy of dataset file
var newFileName = $"{DATASET_FILE}{NEW_FILE_SUFFIX}{FILE_EXTENSION}";
display(newFileName);
CreateCsvAndReplaceSeparatorInCells(originalFileName, newFileName, SEPARATOR, SEPARATOR_REPLACEMENT);

05-27-2020_new.csv

var covid19Dataframe = DataFrame.LoadCsv(newFileName);

4. Data Analysis

Data analysis is a critical activity in the field of Data science. It provides ways to uncover the hidden attributes of a dataset which can’t be analyzed or predicted by simply looking at the data source. DataFrame makes the analysis simple by providing great API’s such as GroupBy, Sort, Filter etc. Jupyter notebook is great tool for this kind of activity which maintains values of variables executed in a cell and providing it to other cells.

Finding the range in the records in Dataset

In DataFrame, Columns property allows access to values within a column by specifying column name. we’ll use Last_Update column to get the date and sort it to get the start and end date

// Gets the data range

var dateRangeDataFrame = covid19Dataframe.Columns[LAST_UPDATE].ValueCounts();
var dataRange = dateRangeDataFrame.Columns[VALUES].Sort();
var lastElementIndex = dataRange.Length - 1;

var startDate = DateTime.Parse(dataRange[0].ToString()).ToShortDateString();
var endDate  = DateTime.Parse(dataRange[lastElementIndex].ToString()).ToShortDateString(); // Last Element

display(h4($"The data is between {startDate} and {endDate}"));

The data is between 5/28/2020 and 5/28/2020

Display 10 records

Here we have 12 columns which includes Country, State, Confirmed, Deaths, Recovered and Active cases

display(covid19Dataframe)

index	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	45001	Abbeville	South Carolina	US	2020-05-28 02:32:31	34.223335	-82.46171	35	0	0	35	Abbeville_ South Carolina_ US
1	22001	Acadia	Louisiana	US	2020-05-28 02:32:31	30.295065	-92.4142	397	22	0	375	Acadia_ Louisiana_ US
2	51001	Accomack	Virginia	US	2020-05-28 02:32:31	37.76707	-75.63235	780	12	0	768	Accomack_ Virginia_ US
3	16001	Ada	Idaho	US	2020-05-28 02:32:31	43.452656	-116.241554	798	22	0	776	Ada_ Idaho_ US
4	19001	Adair	Iowa	US	2020-05-28 02:32:31	41.330757	-94.47106	7	0	0	7	Adair_ Iowa_ US
5	21001	Adair	Kentucky	US	2020-05-28 02:32:31	37.1046	-85.281296	96	19	0	77	Adair_ Kentucky_ US
6	29001	Adair	Missouri	US	2020-05-28 02:32:31	40.190586	-92.600784	46	0	0	46	Adair_ Missouri_ US
7	40001	Adair	Oklahoma	US	2020-05-28 02:32:31	35.88494	-94.65859	82	3	0	79	Adair_ Oklahoma_ US
8	8001	Adams	Colorado	US	2020-05-28 02:32:31	39.87432	-104.33626	3006	118	0	2888	Adams_ Colorado_ US
9	16003	Adams	Idaho	US	2020-05-28 02:32:31	44.893337	-116.45452	3	0	0	3	Adams_ Idaho_ US

Display Top 5 records

covid19Dataframe.Head(5)

index	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	45001	Abbeville	South Carolina	US	2020-05-28 02:32:31	34.223335	-82.46171	35	0	0	35	Abbeville_ South Carolina_ US
1	22001	Acadia	Louisiana	US	2020-05-28 02:32:31	30.295065	-92.4142	397	22	0	375	Acadia_ Louisiana_ US
2	51001	Accomack	Virginia	US	2020-05-28 02:32:31	37.76707	-75.63235	780	12	0	768	Accomack_ Virginia_ US
3	16001	Ada	Idaho	US	2020-05-28 02:32:31	43.452656	-116.241554	798	22	0	776	Ada_ Idaho_ US
4	19001	Adair	Iowa	US	2020-05-28 02:32:31	41.330757	-94.47106	7	0	0	7	Adair_ Iowa_ US

Display Random 6 records

covid19Dataframe.Sample(6)

index	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	18025	Crawford	Indiana	US	2020-05-28 02:32:31	38.288143	-86.44519	23	0	0	23	Crawford_ Indiana_ US
1	51065	Fluvanna	Virginia	US	2020-05-28 02:32:31	37.84158	-78.27715	87	6	0	81	Fluvanna_ Virginia_ US
2	13241	Rabun	Georgia	US	2020-05-28 02:32:31	34.883896	-83.403046	17	1	0	16	Rabun_ Georgia_ US
3	12003	Baker	Florida	US	2020-05-28 02:32:31	30.3306	-82.284676	29	3	0	26	Baker_ Florida_ US
4	21133	Letcher	Kentucky	US	2020-05-28 02:32:31	37.123066	-82.85346	4	0	0	4	Letcher_ Kentucky_ US
5	12077	Liberty	Florida	US	2020-05-28 02:32:31	30.23766	-84.88293	209	0	0	209	Liberty_ Florida_ US

Display Dataset Statistics such as Total, Max, Min, Mean of items in a column

covid19Dataframe.Description()

index	Description	FIPS	Lat	Long_	Confirmed	Deaths	Recovered	Active
0	Length (excluding null values)	3009	3346	3346	3414	3414	3414	3414
1	Max	99999	71.7069	178.065	370680	37460	391508	229780
2	Min	0	-52.368	-164.03539	0	0	0	-364117
3	Mean	27622.38	35.3851	-78.927086	1667.1909	104.16784	688.3679	882.6784

Display Dataset type information for each column

covid19Dataframe.Info()

index	Info	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	DataType	System.Single	System.String	System.String	System.String	System.String	System.Single	System.Single	System.Single	System.Single	System.Single	System.Single	System.String
1	Length (excluding null values)	3009	3414	3414	3414	3414	3346	3346	3414	3414	3414	3414	3414

5. Data Cleaning

Data Cleaning is another important activity in which remove the irrelevant data present in our dataset. This irrelevant data can be due missing values, invalid values or an outlier. The columns with less significance is removed for better analysis and prediction of our data. In order to keep this notebook simple, we’ll use one of the techniques to remove invalid data. In this we are going to remove invalid Active cases such as the ones having negative values. The other techniques we can apply on data could be DropNull to remove rows with null values, FillNull to fill null values with other such as mean, average. We can transform DataFrame and remove some of the unnecessary columns.

Remove invalid Active cases

covid19Dataframe.Description()

index	Description	FIPS	Lat	Long_	Confirmed	Deaths	Recovered	Active
0	Length (excluding null values)	3009	3346	3346	3414	3414	3414	3414
1	Max	99999	71.7069	178.065	370680	37460	391508	229780
2	Min	0	-52.368	-164.03539	0	0	0	-364117
3	Mean	27622.38	35.3851	-78.927086	1667.1909	104.16784	688.3679	882.6784

From the above description table, we could see negative value for Active cases which seems to be incorrect as number of active cases is cases is calculated by the below formula

Active = Confirmed - Deaths - Recovered

In order to check for invalid active cases, we’ll use DataFrame Filter to retrieve active column values whose value is less than 0.0

// Filter : Gets active records with negative calues

PrimitiveDataFrameColumn<bool> invalidActiveFilter = covid19Dataframe.Columns[ACTIVE].ElementwiseLessThan(0.0);
var invalidActiveDataFrame = covid19Dataframe.Filter(invalidActiveFilter);
display(invalidActiveDataFrame)

index	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	90004	Unassigned	Arizona	US	2020-05-28 02:32:31	<null>	<null>	0	2	0	-2	Unassigned_ Arizona_ US
1	90018	Unassigned	Indiana	US	2020-05-28 02:32:31	<null>	<null>	0	159	0	-159	Unassigned_ Indiana_ US
2	90022	Unassigned	Louisiana	US	2020-05-28 02:32:31	<null>	<null>	84	105	0	-21	Unassigned_ Louisiana_ US
3	90024	Unassigned	Maryland	US	2020-05-28 02:32:31	<null>	<null>	0	67	0	-67	Unassigned_ Maryland_ US
4	90032	Unassigned	Nevada	US	2020-05-28 02:32:31	<null>	<null>	0	6	0	-6	Unassigned_ Nevada_ US
5	90033	Unassigned	New Hampshire	US	2020-05-28 02:32:31	<null>	<null>	10	51	0	-41	Unassigned_ New Hampshire_ US
6	90038	Unassigned	North Dakota	US	2020-05-28 02:32:31	<null>	<null>	0	8	0	-8	Unassigned_ North Dakota_ US
7	90056	Unassigned	Wyoming	US	2020-05-28 02:32:31	<null>	<null>	0	13	0	-13	Unassigned_ Wyoming_ US
8	<null>		C. Valenciana	Spain	2020-05-28 02:32:31	39.484	-0.7533	11089	1332	9970	-213	C. Valenciana_ Spain
9	<null>		Cantabria	Spain	2020-05-28 02:32:31	43.1828	-3.9878	2283	202	2287	-206	Cantabria_ Spain

If we take any record(index 5) and apply above formula to calculate

Active(-13) = Confirmed(10) - Deaths(51) - Recovered(0)

We could see invalid active cases.

In order to remove it, we’ll apply a Filter to DataFrame to get active values greater than or equal to 0.0.

// Remove invalid active cases by applying filter

PrimitiveDataFrameColumn<bool> activeFilter = covid19Dataframe.Columns[ACTIVE].ElementwiseGreaterThanOrEqual(0.0);
covid19Dataframe = covid19Dataframe.Filter(activeFilter);
display(covid19Dataframe.Description());

index	Description	FIPS	Lat	Long_	Confirmed	Deaths	Recovered	Active
0	Length (excluding null values)	3001	3338	3338	3395	3395	3395	3395
1	Max	99999	71.7069	178.065	370680	37460	142208	229780
2	Min	0	-52.368	-164.03539	0	0	0	0
3	Mean	27564.82	35.48979	-79.35968	1664.4899	103.38468	505.34668	1055.7584

As seen above, negative active cases have been removed

6. Visualization

Visualization of data helps business owners make better decisions. The DataFrame maintains data in a tabular format. In order to prepare data for different plots, I have used DataFrame features such as Sum, GroupBy, OrderBy, OrderByDescending etc.

For visualization, I have used open source library called as XPlot.Plotly. Different plots have been used such as Bar, Pie and Line/Scatter Graph.

Global

Collect Data

//  Gets the collection of confirmed, deaths and recovered

var confirmed = covid19Dataframe.Columns[CONFIRMED];
var deaths = covid19Dataframe.Columns[DEATHS];
var recovered = covid19Dataframe.Columns[RECOVERED];

// Gets the sum of collection by using Sum method of DataFrame
var totalConfirmed = Convert.ToDouble(confirmed.Sum());
var totalDeaths = Convert.ToDouble(deaths.Sum());
var totaRecovered = Convert.ToDouble(recovered.Sum());

Confirmed Vs Deaths Vs Recovered cases

display(Chart.Plot(
    new Graph.Pie()
    {
        values = new double[]{totalConfirmed, totalDeaths, totaRecovered},
        labels = new string[] {CONFIRMED, DEATHS, RECOVERED}
    }
));

Top 5 Countries with Confirmed cases

In order to get top 5 countries data, I have used DataFrame’s GroupBy, Sum, OrderByDescending methods

![top-5-confirmed-countries-global](C:\Users\lenovo\Downloads\covid-19-master\covid-19-master\part-1\assets\top-5-confirmed-countries-global.png)// The data for top 5 countries is not present in the csv file.
// In order to get that, first DataFrame's GROUPBY is used aginst the country.
// Then it was aggregated using SUM on Confirmed column.
// In the last, ORDERBYDESCENDING is used to get the top five countries.

var countryConfirmedGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(CONFIRMED).OrderByDescending(CONFIRMED);
var topCountriesColumn = countryConfirmedGroup.Columns[COUNTRY];
var topConfirmedCasesByCountry = countryConfirmedGroup.Columns[CONFIRMED];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> confirmedCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    confirmedCases.Add(Convert.ToInt64(topConfirmedCasesByCountry[index]));
}

var title = "Top 5 Countries : Confirmed";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = confirmedCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);

Top 5 Countries with Deaths

// Get the data
var countryDeathsGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(DEATHS).OrderByDescending(DEATHS);
var topCountriesColumn = countryDeathsGroup.Columns[COUNTRY];
var topDeathCasesByCountry = countryDeathsGroup.Columns[DEATHS];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> deathCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    deathCases.Add(Convert.ToInt64(topDeathCasesByCountry[index]));
}

var title = "Top 5 Countries : Deaths";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = deathCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);

Top 5 Countries with Recovered cases

// Get the data
var countryRecoveredGroup = covid19Dataframe.GroupBy(COUNTRY).Sum(RECOVERED).OrderByDescending(RECOVERED);
var topCountriesColumn = countryRecoveredGroup.Columns[COUNTRY];
var topRecoveredCasesByCountry = countryRecoveredGroup.Columns[RECOVERED];

HashSet<string> countries = new HashSet<string>(TOP_COUNT);
HashSet<long> recoveredCases = new HashSet<long>(TOP_COUNT);
for(int index = 0; index < TOP_COUNT; index++)
{
    countries.Add(topCountriesColumn[index].ToString());
    recoveredCases.Add(Convert.ToInt64(topRecoveredCasesByCountry[index]));
}

var title = "Top 5 Countries : Recovered";
var series1 = new Graph.Bar{
        x = countries.ToArray(),
        y = recoveredCases.ToArray()
    };

var chart = Chart.Plot(new []{series1});
chart.WithTitle(title);
display(chart);

India

Confirmed Vs Deaths Vs Recovered cases

Filtering on Country column with INDIA as value

// Filtering on Country column with INDIA as value

PrimitiveDataFrameColumn<bool> indiaFilter = covid19Dataframe.Columns[COUNTRY].ElementwiseEquals(INDIA);
var indiaDataFrame = covid19Dataframe.Filter(indiaFilter);
            
var indiaConfirmed = indiaDataFrame.Columns[CONFIRMED];
var indiaDeaths = indiaDataFrame.Columns[DEATHS];
var indiaRecovered = indiaDataFrame.Columns[RECOVERED];

var indiaTotalConfirmed = Convert.ToDouble(indiaConfirmed.Sum());
var indiaTotalDeaths = Convert.ToDouble(indiaDeaths.Sum());
var indiaTotaRecovered = Convert.ToDouble(indiaRecovered.Sum());

display(Chart.Plot(
    new Graph.Pie()
    {
        values = new double[]{indiaTotalConfirmed, indiaTotalDeaths, indiaTotaRecovered},
        labels = new string[] {CONFIRMED, DEATHS, RECOVERED}
    }
));

Conclusion

I hope you have enjoyed reading the notebook, and might have got some idea on the powerful features of DataFrame in .Net. Data science capabilities are emerging fast in the .Net ecosystem which abstracts lot of complexity present in the field. The focus of this notebook is data analysis and there is nothing present from a Machine Learning perspective such as making a prediction. In Part-2, I have done time series analysis and predictions using ML.Net

Source Code: https://github.com/praveenraghuvanshi1512/covid-19

If you liked it, please like/comment at Comments. It’ll encourage me to write more.

Contact

LinkedIn : https://in.linkedin.com/in/praveenraghuvanshi
Github : https://github.com/praveenraghuvanshi1512
Twitter : @praveenraghuvan

References

• Using ML.NET in Jupyter notebooks
• An Introduction to DataFrame
• DataFrame - Sample
• Getting started with ML.NET in Jupyter Notebooks
• Tips and tricks for C# Jupyter notebook
• Jupyter notebooks with C# and R running
• Data analysis using F# and Jupyter notebook — Samuele Resca
• Exploring the C# Dataframe API by Jon Wood
• Coronavirus-COVID-19-Visualization-Prediction
• Data Modelling & Analysing Coronavirus (COVID19) Spread using Data Science & Data Analytics in Python Code

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