Day #71 - Kaggle Best Practices

After a long pause back to learning mode. This post is on learning's from Coursera course - Winning Kaggle Competitions (https://www.coursera.org/learn/competitive-data-science)

Session #1
Basics on Kaggle Competition

• Data - text, pictures (Format could be csv, database, text file, speech etc). Accompanied by description of features
• Model - Exactly built during competition. Transforms data into answers, Model propertiese - Product best possible prediction and be reproducible
• Submissions - Compare against models and predictions submitted
• Evaluations - How good is your model, Quality of model defined by Evaluation function (Rate of correct answers)
• Evaluation Criteria - Accuracy, Logistic Loss, AUC, RMSE, MAE

Guidelines for submissions

• Analyze data
• Fit model
• Submit
• See Public Score

Data Science Competition Platforms

• kaggle
• DrivenData
• CrowdAnalityx
• CodaLab
• DataScienceChallenge.net
• DataScience.net
• KDD
• VizDooM

Session #2
Using Kaggle

• Data format and explanations
• Evaluation Criteria
• Sample Submission File
• Timelines page

Kaggle vs Real world Competitions

Real - World Machine learning problems have Several Stages

• Understand business problem
• Problem Formulation
• Collect Data, Mine Examples
• Clean Data
• Preprocess it
• Identify Model for Task
• Select best models
• Accuracy
• Deploy model (make it available to users)
• Monitor and retrain with new data

Kaggle

• All data collected and problems fixed
• Model creation and evaluation

Summary

• Real world problems are complicated
• Competition are a great way to learn
• But Kaggle competitions don't address the questions of formalization, deployment and testing

Key insights

• Importance of understaing data
• Tools to users
• Try (Complex solutions, Advance feature engineering, doing huge calculation)

Session #3

• Linear Model (Classifying two set of points using linear lines, 2Dimesions) - Logistics Regression, SVM (Linear models with different loss functions), Good for Sparse High Dimesional data, Linear models into two subspaces
• Tree based - Use decision tree as basic block to build more complicated models (Tree based Decision Tree, Random Forest, Gradient Boosted Decision Trees), DT - Divide and Conquer approach to Recursively split spaces into sub spaces. Tree based methods split spaces into boxes
• KNN - Nearest Neighbours, Labels for points shown, Points close to each other are likely to have similar labels, K nearest objects and label with majority votes, Relies heavily on measure points
• Neural Networks - Special class of ML models, Blackbox produces most seperating curves, Play with parameters of simple feed forward networks. Good for image, sounds, text, speech

Session #4 - Data Preprocessing

• Preprocess for feature engineering
• Basic feature generation for different types of features
• Numeric, Categorical, DateTime based features

Features

• (0/1) - Binary Features
• Numeric features (Age, fare)
• Categorical (Classes)

Feature Preprocessing

• Each feature has own ways to be preprocessed
• Depends on model to use
• Linear models not for two class features
• One hot encoder
• Random forest can easily put each class seperately and predict each probability

Data Types

Structured Data

• Ordinal - Ranks 1st / 2nd / 3rd Ordinal Data
• Numerical - Specific Numeric Data
• Continuous - Petrol Prices continuous data
• Categorical - Days of Week, Months of Year

Happy Learning and Coding!!!