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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 of Mini DataHack (Time Series) – Approach, Codes and Solutions

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Analytics Vidhya Last Updated : 20 Sep, 2018
6 min read

Introduction

It takes sheer commitment and knowledge to build a predictive model in 3 hours.

The motive of this competition was to make people think, decide and implement multitude of ideas quickly. It’s the aha! factor which most companies seek in a data scientist. The ability of make justifiable and quick decisions can make any candidate stand out for a job.

More than 1200 participants from all over the world registered for this competition. Winners were chosen on the basis of RMSE score.

Since time was limited, we decided to provide a relatively simple data set. A time series problem was given. The data set had fewer variables. Therefore, participants got more time to focus on modeling techniques rather than data exploration.

This battle of intense coding and machine learning algorithms continued for 3 hours. Winners took the smartest way of all. Below are the top 3 winners:

  1. Surya Parameswaran, Data Scientist, Groupon
  2. Shailesh Mohanty, PGDBA Candidate (2015-17), IIM Calcutta
  3. Sudalai Rajkumar, Senior Data Scientist, Tiger Analytics (3rd Position)

Here is the final rankings of all participants: Leaderboard

For your learning purpose, below is the complete approach, solution and codes used by top 3 winners. 

Note: I would like to sincerely thank our winners for their immense cooperation and patience shown in sharing their competition experience.

 

Winning Approach and Solutions

Rank 3 : Sudalai Rajkumar, Chennai, India

sudalai rajkumar srk

SRK says:

In this mini-hack, I followed a similar approach, I used in previous edition of mini hack (explained here. This time also, my best model was a weighted average of XGBoost model and Linear Regression. Yes, linear regression is an under-dog but powerful team player.

It was evident from the date variable that it contained a lot of information. So, I created new time based features:

  • Day of the Month
  • Month of the Year
  • Day of the Week
  • Day of the Year
  • Ordinal Date

I used these features as input variables to the model. These two models (XGBoost and Regression) did a fairly good job in capturing the variation in sales and the increasing trend.

Then, I did data exploration, just to ensure that I don’t miss out on visible patterns in the data. Interestingly, when I created a scatterplot of sales, I saw that there were two points way higher than the rest of the sales (about 70x of the median value). When probed deeper, I found out the dates were 25th Dec 2007 and 24th Dec 2008. Also, being the last working day on both these years, I thought this higher sales could be due to:

  • Christmas Sales
  • Year end sales adjustment

After this information, when I re-checked my model output, I found that this pattern (higher sales during Christmas) didn’t get captured. So, I thought of creating a separate variable (a binary variable like Christmas flag) to capture this trend.

But, due to time constraint I couldn’t do it satisfactorily. So, I just did a manual correction for Christmas Eve sales and made the final submission. I am fairly sure that because of this last step, I ended up at 3rd position. Had I got few more minutes, I might have done better.

In the end, it was an amazing learning experience. I learned that it is essential to do some data exploration even if we use powerful algorithms as sometimes they might fail to capture the obvious patterns.

Solution: Link to Code

 

Rank 2: Sailesh Mohanty, Kolkata, India

sailesh mohanty

Sailesh says:

Unlike full fledged long hours hackathons, the key to crack a 3 hour mini hack is just being smart at handling the data.

In 3 hour, you don’t get the luxury of trying out many different approaches ( because you’ve limited time), so adopting a smart approach will give you a definite competitive advantage.

But, I tried several methods to deal with given time series data. After progressing through failed attempts, I finally found the model which helped me secure 2nd position. So, here’s a quick review of my approach used:

Method 1 – As a no-brainer , I started with times series (decomposition and forecasting) concepts to check for trend and seasonality. Then, I eliminated the unsual trends to avoid biasness and built an ARIMA model. With this method, I became aware of the hidden patterns in the data. Though, the forecast values were pretty off target, but starting here did give me a base to improve upon.

Method 2 – After scrutinizing the data, I found out that the data had abnormally high sales on Christmas Eve and September (which I believe is due to festive season). Beyond these abnormal observations, the random fluctuations in the time series seem to be roughly constant in size over time. Therefore, it wouldn’t be incorrect to describe the data using an additive model. Thus, I made forecasts using simple exponential smoothing i.e. Holt Winter’s model. But again the results were unsatisfactory. Still, I kept trying.

Method 3 – The forecast package in R contains functions to make forecasts using Neural Networks with nnetar function. I tried it and got slightly better results. Yet, I was still way down the leaderboard.

Method 4 – This time I thought of doing something drastically different. I eliminated the outliers, gave higher weight to recent data, generated a feature ‘month’ and categorized it as high sale and low sale. Then, I generated a week day feature (weekends generally had more sales) and finally used a simple XGBOOST model with random hyper parameter tuning using MLR package in R. This did the trick.

It was great fun participating in this competition. As someone who has studied and learnt statistical and analytical concepts from IIT, IIM and ISI, I want to state that AV’s blog, tutorials and competitions have been of great help to understand statistical concepts better and to keep up with the latest developments in the field. AV’s competitions also draws significant interest from my batchmates here. Thank you for making them so interesting.

Solution: Link to Code

 

Rank 1: Surya Parameswaran, Chennai, India

suryaSurya says:

This was my first hack @ AV. I joined the hack pretty late and didn’t have much time left. When I explored the data, I found evidence of year on year trend and some seasonality (specially year end sales). Sales were also erratic at places.

So, with limited time in hand, I decided to build a model using exponential smoothing time series method. This helped me fetch the winning model.

Had I started earlier, I would have ideally captured seasonal elements separately like weekly seasonal indices, holiday seasonal indices (using some generic holiday calendar) and the trend part.  With the de-seasonalized data, I would have predicted daily forecast using any of the time series model and multiplied the seasonal and trend components to it.

Overall  I enjoyed the experience and look forward to participating in many more hacks to come.

Solution: Link to Code

 

Important Learnings

The motive of this article is to make you familiar with simple & advanced techniques used in a time series problem. Here are the key takeaways from this article:

  1. Data Exploration is important: While working on a time series problem, make sure that you discover the hidden trends and seasonality (if exists) using simple plots. This will allow you to formulate strategies for later stages.
  2. XGBoost is your best friend: You must learn to train xgboost algorithm, specially the parameter tuning part. Irrespective of data sets, this algorithm is known to deliver astounding results. Here’s a nice tutorial to get started: Guide on XGBoost
  3. Domain knowledge helps: It’s important to get basic understanding various domains like retail, healthcare, automobile etc. For example: If you wouldn’t know that sales of a company goes high on festive season, how would you understand the seasonal effect? Hence, it is tacitly important.
  4. Feature Engineering: It’s important that you look for new features in the given data set. Often, it has been seen that lots of additional information is found in datetime stamp. Never miss it now. New features impart additional information to the model. This eventually results in higher predictive accuracy.

 

End Notes

It was a wonderful experience interacting with these winners and knowing about their secretive coding styles. Hopefully, you would be able to evaluate your hits and misses in this competition.

Did you find this helpful ? Do share your competition experience and feedback in comments below.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

exponential smoothingmini datahackmini hack solutionTime Seriestimeseries solutionXGBoost
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Responses From Readers

Clear

No_Mind
No_Mind

Hi Congrats to all the participants & Winners too! Can you pls share the dataset for practice purpose. Thanks

123

Jignesh
Jignesh

thank a lot manish can you please share the dataset please

123

Ram
Ram

Congrats Surya, Amazing,. you did it in 20 lines of code. . Thank you Manish. For those who were unable to participate in this hack, could you please share the dataset?

123

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