adhishagc/csc-369-2-machine-learning-submission-2018

CSC 369 2.0 Machine Learning Assignment Submission 2018

Table of Contents

1. Import of Libraries

2. File loading and Pre-Processing

   • Label conversion to Numericals
   • Checking for Missing Data
   • Split of Features & Labels
   • Outlier Detection
   • Handling Missing Data & One Hot Encoding (Coding of Discrete inputs)
   • Feature Extraction
   • Normalization

3. KNN Model using SKLearn

   • Model Training
   • Validation
   • Post Training Analysis
     • Accuracy Score
     • Confusion Matrix
     • Classification Report
       • Precision
       • Recall
       • F-Measure
     • Variation of K Neighbor Classifier
       • With Outliers
       • Without Outliers

4. Logistic Regression using Keras

   • Model Training and Validation
   • Post Training Analysis
     • Accuracy Score
     • Confusion Matrix
     • Classification Report
       • Precision
       • Recall
       • F-Measure

5. Neural Network using Keras

   • Model Training and Validation
   • Post Training Analysis
     • Accuracy Score
     • Confusion Matrix
     • Classification Report
       • Precision
       • Recall
       • F-Measure

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