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“If you torture the data long enough, it will confess.”
― Ronald H. Coase,
Essays on Economics and Economists

Background

Introduction


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Photo by Bruce Hong on Unsplash

“You can have data without information, but you cannot have information without data.” — Daniel Keys Moran

Background

Pandas is one of those packages that makes analysing data much easier. Pandas is an open source library for data analysis in Python. It was developed by Wes McKinney in 2008. …


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Every Data tells us a story and this story is depicted using data visualizations-Saurav Anand

Introduction


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Photo by Priscilla Du Preez on Unsplash

The only time a lazy man succeeds is when he tries to do nothing :- Evan Esar

Background

  • Training the model which means fitting the algorithm on the training data.
  • Testing the model and predicting the output values
  • Finding the accuracy of the model.
  • Hyperparameter tuning to…


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Photo by William Iven on Unsplash

Background


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In good information visualizations, there are no rules , no guidelines , no templates, no standard technologies , no style books … You must simply do whatever it takes — Edward Tufte

Introduction

If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too.

  • Using default themes that are aesthetically pleasing.
  • Setting custom color palettes.
  • Making attractive statistical plots.
  • Easily and flexibly displaying distributions.


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https://unsplash.com/photos/uAFjFsMS3YY

Introduction

  1. Matplotlib is a 2-D plotting library that helps in visualizing figures.
  2. It took inspiration from MATLAB programming language and provides a similar MATLAB like interface for graphics.
  3. It really integrated well pandas which is used for data manipulation
  4. It is a robust, free and easy library for data visualization.

Matplotlib installations and basics

Installation


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“One of the holy grails of machine learning is to automate more and more of the feature engineering process.” ― Pedro Domingos

Introduction

  1. Data reading and merging and making it ready to use.
  2. Data preprocessing which refers data cleaning and data wrangling.
  3. Optimization where the feature and model selection process is done.
  4. Applying it to the application to predict the accurate values.


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Photo by Mpho Mojapelo on Unsplash

Introduction

Intuition of Logistic Regression


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Database is the information you loose when your memory crashes — Dave Barry

Introduction

Saurav Anand

Machine Learning | Data Science |Artificial Intelligence Enthusiast |https://www.linkedin.com/in/saurav-anand-92229584/|https://www.kaggle.com/saurav9786

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