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Classification Central �A Classification Playground for Aspiring Data Scientists By Data Scientists

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How many of you wish you had a site to learn about classification on an interactive basis?

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How many of you wish you had a good explanation for each piece of code that’s happening in a model on an interactive basis?

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Project Goal

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Provide Aspiring Data Scientists with

1. A clear understanding of each step within the classification modeling process

2. A tutorial of how to classify their own dataset

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Classification Techniques�Explained

Logistic Regression

KNN

Random Forest

Naïve Bayes

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  1. Check for and Remove Null Values
  2. Summarize Your Data
  3. Replace the Classifier with 0 and 1
  4. Implement Test and Training Sets
  5. Determine the Accuracy Score
  6. Output a Confusion Matrix

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�Logistic Regression Summary

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�KNN Summary

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�Random Forest Summary

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Naïve Bayes Summary

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Tutorial Steps

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What is the most important step in any modeling?

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Understanding your dataset!!!

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How do you understand your data?

  • By performing Exploratory Data Analysis
  • Histogram
  • Box Plot
  • Outlier Analysis
  • Central Tendency
  • Dispersion

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Determine Column Name Containing the Nulls, then Delete

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Let’s Replace Categorical Values to Quantitative

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Replace Cont’d

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Test and Training Data

Part 1: Select independent variables

Part 2: Select a dependent variable

Part 3: Splits the dataset into test and training 

Part 4: Create a model

Part 5: Train the data

Part 6: Test the model

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Accuracy: Can you spot the difference?

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How accurate is the model?

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Output Confusion Matrix

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Data Report

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Null Values

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Visualizations

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Classification Central Video Walk-Through:�

https://www.youtube.com/watch?v=QCucIBfCy84