Philip Kalinda
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Data Wrangling Techniques - Part 1​

Description

This post is dedicated to sharing some of the techniques I apply to data when solving data science problems. Data comes in so many different shapes and sizes making it necessary to process data appropriately in order to make it suitable for whatever your objectives are.

There are numerous ways you can manipulate and transform data and within this post I will name a few techniques that I have found useful throughout the stages of solving data science problems and that I prefer to use. 
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    • 10. StackerPy - Model Stacking for Scikit-Learn Models
    • 9. Keep It Plain And Simplex: Linear Programming for the PL Fantasy Football
    • 8. Optimisation of Feature Selection in Machine Learning using Genetic Algorithms
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    • 3. Multi-Class Logistic Classification and K-Means Clustering of Iris Data
    • 2. Restaurant Mult-Criteria Decision Analytics
    • 1. FIFA Pro Clubs Promotion Statistical Probabilities Analysis
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  • Home
  • About
  • Data Science & Analytics ›
    • 10. StackerPy - Model Stacking for Scikit-Learn Models
    • 9. Keep It Plain And Simplex: Linear Programming for the PL Fantasy Football
    • 8. Optimisation of Feature Selection in Machine Learning using Genetic Algorithms
    • 7. Kaggle - Titanic: Machine Learning From Disaster
    • 6. DrivenData - Pump It Up: Data Mining The Water Table
    • 5. DrivenData - Predicting Blood Donations
    • 4. Hackerrank - Email Prediction
    • 3. Multi-Class Logistic Classification and K-Means Clustering of Iris Data
    • 2. Restaurant Mult-Criteria Decision Analytics
    • 1. FIFA Pro Clubs Promotion Statistical Probabilities Analysis
  • Feedback & Comments