Release v1.0.6

New Features

• Imputation Strategy
  In data analysis and machine learning, an "Imputation Strategy" refers to a method for handling missing or incomplete data within a dataset. The strategy involves substituting missing values with estimated ones based on various algorithms or statistical methods.

What's Changed

Main Features

• Issue 25 - refractored java files by @acsolle66 in #37
• Add example how to use RandomForest by @acsolle66 in #45
• Multilayerperceptronexample by @kisharnath in #52
• Multilayerperceptron example added with seaborndataset by @kisharnath in #51
• chore(53): Add github pages to the project #53 by @Samyssmile in #54
• chore(): CONTRIBUTING.md added by @Samyssmile in #56
• Pages by @Samyssmile in #57
• chore(): GitHub Page by @Samyssmile in #59
• chore(): GitHub Page by @Samyssmile in #60
• feat(#64): Drop Custom Data Preparation #64 by @Samyssmile in #66
• Simple math classes (improvements needed) by @GolemIron in #65
• chore(): SVM Iris example by @Samyssmile in #68
• chore(): random forest iris example added by @Samyssmile in #69
• fix(#70): correct leaf node counting in DecisionTree implementation by @Samyssmile in #71
• Feat(#23) Replace filterIncompleteRecords boolean with Imputation Enum for Enhanced Data Handling by @acsolle66 in #72
• chore():Reformat Codebase with Google Formatter #78 by @Samyssmile in #79
• chore(): Write JUnit Tests #80 by @Samyssmile in #81
• chore(#76): add examples for all ML Algorithms with Seaborn dataset by @acsolle66 in #82
• Release 1.0.6 by @Samyssmile in #83

New Contributors

• @acsolle66 made their first contribution in #37
• @kisharnath made their first contribution in #52

Full Changelog: 1.0.5...1.0.6