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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
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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
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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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 Naïve Bayes
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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 Proven Ways for improving the “Accuracyâ€_x009d_ of a Machine Learning Model
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
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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

What I learnt about Time Series Analysis in 3 hour Mini DataHack?

Aarshay
Aarshay 02 Aug, 2019
5 min read

Last weekend, I participated in the Mini DataHack by Analytics Vidhya and I learnt more about Time Series in those 3 hours than I did by spending many hours leading up to the event. Hence, I thought I will share my learnings with all of you.

 

What was Mini DataHack?

In short, Analytics Vidhya came up with an idea to shorten up their Signature hackathons and the result was Mini DataHack. It was basically a 3 hour hackathon, where the problem area was released upfront. The philosophy behind this Mini Hackathon was to provide a power pack of learnings on a focused area in a short duration.

My preparations

Since it was already decided that the problem would be about Time Series – I made sure I was well equipped with knowledge and packages about Time Series. Infact, I even wrote a guide to Time Series in Python.

1200

 

Action packed 3 hours

If AV Signature hackathon is equivalent to an ODI in cricket, Mini DataHack was like a T20 match – shorter, action packed and full of twists! Close to 900 people registered for the Mini DataHack – a very high number, given that it was floated only 6 days before the event! It was a very intense competition from the word GO. Vopani made first submission in under 5 minutes and SRK (who won the competition) was not on top till a few minutes before the finish time.

Honestly, it was very difficult to guess the outcome until it happened.

 

Learnings from Mini DataHack

Needless to say, I learnt a lot about Time series in these 3 hours. Here is a brief summary of my learnings:

  • If there is one thing which matters the most in creating time series forecasts, it is the importance of plotting and visualizing trends with your eyes. Very often, people get caught in optimizing the evaluation metrics – which a lot of people did in this competition as well. But the winner made sure he plotted past data and forecasts on a single plot and it definitely served him well!
  • Along with using Time Series Forecasting techniques like ARIMA, a good idea is to formulating the problem as a supervised regression problem. As per the winner and experienced Kagglers, this works better in most cases. The supervised algorithm can contain variables such as Day of the month, Hour of the Day, Day of the Week, Days gone from series starting, Month of the Year, Week of the Year etc. Here is what SRK (the winner) said about his approach:

Approach from SRK:

I used both xgboost and linear regression to get to my final score. Variables used in the models are:srk
1. Day of the month
2. Hour of the day
3. Day of the week
4. Ordinal date (Number of days from January 1 of year 1)

At first, I plotted the DV using a scatter plot and here were some of my observations:

  1. There is an increasing trend
  2. The trend at the initial part is different from the later part of the training data

I then trained a xgboost model on the full dataset which I think helped to capture the overall trend of all these input variables. This is the one which scored a rmse of 139 on the public LB. But since xgboost is a space splitting algorithm, I thought it won’t be able to capture the increasing trend and so it may not be able to extrapolate the same in test set.
So, I decided to run a linear regression model to capture the increasing trend. Since the initial part has a different pattern compared to the later part, training the linear regression model only on the later part of the training set made more sense to me and I did that. This one scored a rmse of about 182 on the public LB.
I averaged both of these models and made the final submission which scored 155 rmse in public LB and 196 rmse in the Private LB.

One more inference from the modeling is that:
Including month of the year, week of the year variables in XGB gave good results in public LB. But when I checked the plot of predicted counts in test set, it took a dip after a certain time period due to the way in which the xgb captures the information. So including these variables might give a good public LB score but most probably will not give a good private LB score. So I dropped these variables while building the models.


Codes are present in my github and the link is
https://github.com/SudalaiRajkumar/ML/tree/master/AV_MiniHack1
—

I think each word in the approach above can be weighed in Gold! A natural question coming to my mind was “How can XGBoost perform better than Time Series methods?” And here is what Vopani added:

 

Note from Vopani:

I’m not surprised XGB and linear models performed so well. I tried out a lot of models and found XGB far superior than any other.
I’ve had a good exposure to time series problems since I worked on many such projects, and in almost all of them I converted the problem into a structure which would fit any supervised algorithm, like what was done here by most people, including SRK and me.
Its no fault of the dataset, its just that XGB is way too clever and powerful, and is able to capture linear and seasonal trends pretty well with the basic date features.
A real time-series challenge is one where the values are given in order without the date variable. Then, you can’t really use an XGB-type model and thats when the power of the ARIMA-type models comes into the picture.
Unfortunately, its pointless keeping out the date variable since there is a lot of useful information there which can boost accuracy and hence, ultimately, XGB ends up the winner.

 

In Summary:

  • XGBoost is a powerful technique which can be used in this case but should be used wisely. It has a tendency to overfit in local regions and doesn’t cater well to the overall trend.
  • Some of the experienced players who used only XGBoost ended up with positions below 50. To cover for this flaw, the winner SRK averaged the XGBoost model with a linear regression model in his final submission.
  • The variables to be modeled with XGBoost should be selected wisely as using all might overfit the data and not generalize well. The winner SRK removed the last 2 in his final submission as he saw the tendency for the model to overfit.
  • The good performance of XGBoost models doesn’t mean that traditional forecasting techniques should be completely ignored. If applied properly they work nicely as the #2 ranked player used an ARIMA model

 

End Note:

I learnt a lot from participating in this Mini DataHack and I can’t help wanting more of these! I hope AV comes up with the next action packed weekend soon!

Mini DatathonTime SeriesTime Series Analysis
Aarshay
Aarshay 02 Aug, 2019

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