Veera Pallati's Blog

StyleCop helps to consistent coding guide lines across the team and enforces the coding guide lines as configured in the configuration. We can always customize the rules or rule set using configuration files. Steps: Create any .NET project and install the following nuget projects. Add the following in the project file Create the folder " StyleCopConfig " under project root folder and create the files - " stylecop.json " and " StyleCop.ruleset " Add the following content in the stylecop.json { "$schema": "https://raw.githubusercontent.com/DotNetAnalyzers/StyleCopAnalyzers/master/StyleCop.Analyzers/StyleCop.Analyzers/Settings/stylecop.schema.json", "settings": { "documentationRules": { "companyName": "VajraTechMinds.com", "copyrightText": "Copyright (c) {companyName}. All Rights Reserved.\r\nLicensed under the MIT License. See LICENSE in the project root for ...

  Setup: Assuming you have the solution with class library and test project using xunit. Make sure the following packages are included in the test project. The below yaml builds, tests and generate the code coverage report. name: .NET on: push: branches: [ "master" ] pull_request: branches: [ "master" ] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Setup .NET uses: actions/setup-dotnet@v3 with: dotnet-version: 6.0.x - name: Restore dependencies run: dotnet restore - name: Build run: dotnet build --no-restore - name: Test run: dotnet test --no-build --verbosity normal --collect:"XPlat Code Coverage" --logger trx --results-directory coverage - name: ReportGenerator uses: danielpalme/ReportGenerator-GitHub-Action@5.1.10 with: reports: coverage/*/coverage.cobertura.xml targetdir: CoverageReport reportty...

I have just started Data science path and this is my first blog post related to data science. It is important to understand the data types, before we get deep in to the data science. Why we need to understand the data types, the reason is statistical analysis depends on the type of data. Data types Qualitative:  This has two types of data types - Normal & Ordinal Normal : This data type represents the labels.                      Examples : Gender - Male/Female, Color : Red/Blue/Block Ordinal : It is similar to "Normal" data type, except that it contains the natural ordering. Ordinal values can also be expressed in numbers.                     Example: Rating : Very Poor, Poor, Average, Good, Very Good. The only difference between Normal/Ordinal is that, there is no ordering in Normal data type where as in Ordinal data type has ordering in it. Quantita...