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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

Winning Strategies for ML Competitions from Past Winners

Kunal Jain
Kunal Jain Last Updated : 21 Oct, 2016
4 min read

Introduction

We launched Knocktober last night and we were happy to see the excitement it has created among all the participants. This time we raised the bar for Analytics Vidhya hackathons. I’m sure the faint hearted ones would have panicked after seeing the problem set. And the ones who didn’t budge from their resolve of winning Knocktober will make history on Analytics Vidhya. Since the competition is unique, we thought of providing you the winning strategies from our past competition winners.

Read on, to know the hackathon approach of three top data scientists. They have also shared useful tips & tricks, that will definitely help you to improve your leaderboard position.

 

SRK

1.Sudalai Rajkumar (SRK) , Senior Data Scientist, AV Rank 1

 

 

 

His approach in past competitions:

  1. Understanding the problem and dataset
  2. Pre-processing the data: Data cleansing, Outlier removal, Normalization / Standardization, Dummy variable creation
  3. Feature engineering : Feature selection, Feature transformation, Variable interaction and Feature creation
  4. Selecting the modeling algorithm
  5. Parameter tuning through cross validation
  6. Building the model
  7. Checking the results by making a submission

Once you’ve executed these 7 steps, a basic framework will be ready to do more experimentation. Further, you can concentrate more on:

  1. Feature engineering – This is where bigger improvements come from most of the times
  2. Building varied kind of models and ensembling them – This will help go that extra mile towards the end

Last but not the least,  we must perform a solid local validation. Else, we might end up over fitting on the public leader board.

Tips from SRK:

1. Understanding the problem – It is really important to have a thorough understanding of the problem that we are trying to solve. Only after we’ve understood the problem clearly, we can derive suitable insights from data to tackle the problem and obtain good results.

2. Structured Thinking – It’s a unique way of thinking through the problems. Being a data scientist, one needs to be more structured in his/her thinking in order to obtain good results.

3. Effective communication of results – Effective communication of derived results is as important as performing the data analysis.

Read detailed article here

 

Rohan Rao

2. Rohan Rao, Lead Data Scientist, AV Rank 4

 

 

 

 

His approach in past competitions:

  1. Understand the problem / objective you are trying to solve.
  2. Understand and summarize what data you have / need.
  3. Carefully read about the evaluation metric.
  4. Explore and visualize the data, build simple, base models for benchmark.
  5. Setup a robust / thorough validation framework consistent with the evaluation conditions.
  6. Work on feature engineering and optimizing algorithms.
  7. Try out as many different models / ideas as you can.
  8. Ensemble / Blend / Stack multiple models.
  9. Never hesitate in asking questions, taking help or even teaming up with others.

Tips from Rohan:

  1. Gauge the complexity of the problem: Explore the data as much as possible. Plot features, summarize columns, build benchmark models, and during the process, get a sense of the problem, data, time, complexity, etc. And then slowly build a good solid concrete solution by working on one idea after another.
  2. Algorithm: I use XGBoost and feature engineering for building ML solutions and it’s been a part of my winning solution for most of the contests I’ve done well in, so a big thanks to the community who are actively developing and improving it each day. I also like Collaborative Filtering techniques, which I’ve implemented very often in my work.
  3. Feature Selection Ways: My thumb rule of feature selection is based on CV or Val scores. If selecting a feature improves CV score, I use it, else discard. For large number of features, I usually build small quick models and check variable importance or information gain, and select the top-x from them.

Read detailed article here

 

Steve Donoho

3. Steve Donoho, Top Data Scientist

 

 

 

 

His approach in past competitions:

  • Well, I start by simply familiarizing myself with the data. I plot histograms and scatter plots of the various variables and see how they are correlated with the dependent variable. I sometimes run an algorithm like GBM or Random Forest on all the variables simply to get a ranking of variable importance.
  • I usually start very simple and work my way toward more complex if necessary. My first few submissions are usually just “baseline” submissions of extremely simple models – like “guess the average” or “guess the average segmented by variable X.” These are simply to establish what is possible with very simple models. You’d be surprised that you can sometimes come very close to the score of someone doing something very complex by just using a simple model.

Tips from Steve:

  1. Making Predictions: This is an important step that is often missed by many – they just throw the raw dependent variable into their favorite algorithm and hope for the best. But sometimes you want to create a derived dependent variable.
  2. I probably spend 50% of my time on data exploration and cleansing depending on the problem.

Read detailed article here

Go on & use these tips from the winners and grab your first win in Knocktober.

 

EndNotes

I am sure these approaches and tips will provide you an upper hand in the competition. Learn and improve from these tips. If you want to register for the ongoing competition, click here. I recommend that you atleast register for Knocktober and explore the problem statement. I assure you it will be a great learning experience for you.

Did you find this article? Do you have any questions? Post them in the comments below and let me know how I can help you further. Also, I would like to hear, if you have any feedback for us.

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

data science competitionData science competitionsknocktobermachine learningML competitonspythonRwin ML competitionswinners approachwinners strategies
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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