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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Winners solutions & approach: The QuickSolver MiniHack, DataFest 2017

sunil
sunil Last Updated : 07 May, 2017
6 min read

Introduction

The best way to learn data science is to work on data problems and solve them. What is even better is to solve these problems with thousands of data scientists around solving the same problem in a competition. And if you get to know what the winners did to solve the problem – you know you are participating in an Analytics Vidhya Hackathon!

We conducted 3 machine learning hackathons as part of AV DataFest 2017. The QuickSolver was conducted on 30 April 2017. More than 1800 data scientists from across the globe competed to grab the top spots.

If you missed this hackathon, you did miss an amazing opportunity. Well, you can still learn from the winners & their strategies. To hear the experiences and strategies of the data scientists who won, join us for the DataFest Closing Ceremony on 10 May 2017.

 

Problem Statement

The problem statement revolved around a digital publication house. They publish articles on varied categories & topics like general knowledge, practical well being, sports & health. These articles are written by distinguished authors in this field. To keep the reader engaged on the website, the portal recommends articles to its readers randomly.

They want to enhance their customer experience, and understand the interest of their customers in detail. Instead of recommending articles to its reader randomly they would like to recommend articles based on their interest & are more likely to read. Currently, the portal has an option to rate articles based after reading.

The data scientists had to predict how much would the reader like the article based on the given data.

 

Winners

The winners used different approaches and rose up on the leaderboard. Below are the top 3 winners on the leaderboard:

Rank 1: Mark Landry

Rank 2: Rohan Rao

Rank 3: Piyush Jaiswal

Here are the final rankings of all the participants at the leaderboard.

All the Top 3 winners have shared their detailed approach & code from the competition. I am sure you are eager to know their secrets, go ahead

 

Piyush Jaiswal, Rank 3

Piyush Jaiswal

 

Piyush is a Data Science Analyst at 64 Squares based in Pune. He is a machine learning enthusiast and has participated in several competitions on Analytics Vidhya.

 

Here’s what Piyush shared with us.

Piyush says ” The model was built around 4 sets of features primarily.

1. Meta data on User : Age buckets and variable V1

2. Meta data on Article : Time since article was published, Number of articles by the same author & Number of articles in the same category

  • User Preferences
  • How does he/she rate in general : Since it involves the target variable, it was calculated using a 5 fold approach for train data and then using the entire train data for the test data
    • Mean, Median, Min, Max rating of the user
    • % ratings in low (0 to 1), medium (2 to 4) and high (5 to 6) buckets
  • Article Characteristics when he/she rates low/medium/high
    • Mean, Min & Max of ‘Vintage Months’ when the user rates low/medium/high
    • Mean, Min & Max of ‘Number of Articles by same author’ when the user rates low/medium/high
    • Was planning to expand on this but could not because of limited time

3. Article Preferences

  • How is the article rated in general: Since it involves the target variable, it was calculated using a 5 fold approach for train data and then using the entire train data for the test data
    • Mean, Median, Min, Max rating of the article
    • I was thinking of creating % ratings in low , medium and high buckets but could not do so because of time constraint
  • User characteristics when the article is rated low/medium/high
    • Again, could not implement this due to time constraint

4. Choice of model was Xgboost. Tried an ensemble between 2 xgb models but it gave little boost (from 1.7899 to 1.7895)

Solution: Link to Code

 

Rohan Rao, Rank 2

Rohan Rao

Rohan is a Senior Data Scientist at Paytm & a Kaggle Grandmaster. Rohan holds multiple accolades on his name. He has won several competitions on Analytics Vidhya and has been actively participating in machine learning competitions. His approach always has interesting insights.

 

Here’s what Rohan shared with us.

Rohan says,
1. Since I didn’t have much time to explore the data, I stuck to some old-school basics of quick feature engineering and stable ensembling.

2. Used raw features + count features initially with XGBoost.

3. On plotting feature importance, I found the user-id to be the most important variable. So, split the train data into two halves, and used the average rating of users from one half of the data as a feature in the second half, and built my model on only the second half of the training data. This gave a major boost and the score reached 1.80

4. I ensembled few XGBoosts with different seeds and to finally get below 1.79

5. Some final tweaks like directly populating the common IDs between train & test and clipping the predictions between 0 and 6, gave minor improvements as well.

Tuning parameters and using linear models didn’t really work. I even tried building a multi-class classification model, but that performed much worse than regression, which is natural considering the metric is RMSE.

Solution: Link to Code

 

Mark Landry, Rank 1

Mark Landry

Mark is a Competitive Data Scientist & Product manager at H2O.ai. Mark is also an active participant in machine learning competition on Analytics Vidhya & Kaggle. Mark has won several hackathons and ranked highly for his machine learning skills. He has several other accomplishments on his name.

Here’s what Mark shared with us.

Mark says,

The random forest takes a while to train (relatively). This time I have rerun everything from a clean R environment, and I haven’t been using seeds in the modeling. I get a 0.9991357 correlation with my final submission file, which ought to perform similarly on the leaderboard, but it certainly is far from ideal.

In short, features were created using data.table in R  and the modeling was done with three H2O models: random forest and two GBMs.

The progression of the models is actually represented in the way the features are laid out. The initial submission scored 1.94 on the public leaderboard (which scored very close to private) and was quickly tuned to about 1.82. This spanned the first six target encodings, no IDs, only the three Article features directly. Over time, I kept adding more (including a pair that surprisingly reduced model quality) all the way to the end. Light experimentation on the modeling hyperparameters, which are likely underfit. Random forest wound up my strongest model, most likely due to lack of internal CV of the GBMs that led to me staying fairly careful.

I kept the User and Article IDs out of the modeling from the start. At one point I used the most frequent User_ID values, but this did not help – the models were already picking up enough of the user qualities. The main feature style is target encoding, so in the code you will see sets of three lines that calculate the sum of response, count of records, and then performs an average that removes the impact of the record to which it is applying the “average”. You’ll see the same style in my Xtreme ML Hack solution (I started from that code, in fact) and also Knocktober 2016.

The short duration and unlimited submissions for this competition kept me moving quickly, but at a disadvantage for model tuning. No doubt these parameters are not ideal, but that was just a choice I made to keep the leaderboard as my improvement focus and iterations extremely short, rather than internal validation. Had either the train/test split or public/private split not appeared random, I would have modeled things differently. But this was a fairly simple situation for getting away with such tactics.

A 550-tree default Random Forest was my best individual model.

Here is the feature utilization.

image

I used a pair of GBMs with slightly different parameters. These features are for the model with 200 trees, a 0.025 learning rate, depth of 5, row sampling of 60% and column sampling of 60%. image

Thanks to Analytics Vidhya for hosting this competition and all of AV DataFest 2017, as well as all participants who worked on the problem and pushed the leaderboard. This was my first six-hour competition and it was as fun as the half-week competitions.

Solution: Link to Code

 

End Notes

It was great fun interacting with these winners and to know their approach during the competition. Hopefully, you will be able to evaluate where you missed out.

If you have any questions feel free to post them below.

Check out all the upcoming competitions here

machine learningmachine learning algorithmmachine learning applicationmachine learning competitionmachine learning datasetsminihackminihack winnerswinners approach
sunil
sunil

Sunil Ray is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.

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Responses From Readers

Clear

Gianni
Gianni

Thanks to all guys, Just because all solutions are in R, I was wondering if some of python programmers would like to publish their solutions also if not perfect or in top ten..

123

Sunil Ray
Sunil Ray

Thanks Gianni for the comment! The objective of this article is sharing winners approach irrespective of tool they are using. You can post this tool specific requirement to our slack channel, here any community member will share their python code (https://analyticsvidhya.slack.com/messages/C0SEHHCUB/details/) specific to this challenge. Regards, Sunil

123

Sam
Sam

Thank you for the article. Where can one get data and the description of the problem. Without that one cannot get much value out of this article. Thank you for your help.

123

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