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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

Kaggle Grandmaster Series – Exclusive Interview with 2x Kaggle Grandmaster Prashant Banerjee

A
Analytics Vidhya Last Updated : 07 Jan, 2021
7 min read

“Data Science is full of opportunities and challenges and I want to explore those opportunities.”

Mindset is one of the factors that is gonna determine how fruitful your Kaggle journey is going to be in the long run. Many People tend to give up right in the beginning after negative reviews and failure without realizing that is the path most of the Kaggle Grandmasters take.

To challenge your belief on this, we are back with the 11th edition of the Kaggle Grandmasters series and this time joining us to tell his story is Prashant Banerjee.

Kaggle Grandmaster Series Prashant Banerjee

Prashant is a 2x Kaggle Grandmaster with the titles in the Notebooks and Discussions category. He holds the 20th and 54th rank respectively. His kernels are highly valued in the community and there is a lot of buzzes when it comes to his discussions.

Further, Prashant completed his Bachelor of Technology(ME) from the Institute of Engineering and Technology (IET) Kanpur and holds a Business Diploma in Business Administration. He currently works as a Data Analyst at Puma.

You can go through the previous Kaggle Grandmaster Series Interviews here.

 

In this interview, we cover a range of topics, including:

  • Prashant’s Education and Work
  • Prashant’s Kaggle Journey from Scratch to becoming a Kaggle Grandmaster
  • Prashant’s Advice for Beginners in Data Science
  • Prashant’s Inspiration and Future plans

 

Prashant’s Education and Work

data sciecne interview work

Analytics Vidhya (AV): Please tell us something about your educational background and how did you get interested in the Data Science Field? 

BachePrashant Banerjee (PB): I did a Bachelor of Technology in Mechanical Engineering from the Institute of Engineering and Technology (IET) Kanpur. I also pursued a Post Graduate Diploma in Business Administration(major- Finance and Statistics) from Jaipuria Institute of Management, Lucknow.

I always knew analytics is always my cup of tea. So, I got a Data Analyst role at Puma Power. But, I realized that I was not doing justice with my skills and caliber. I have a passion for numbers and challenges. So, I researched a lot for other opportunities where I can use my skills. Then, I came to know about the Data Science field. I kept on researching the Data Science field and it has further enhanced my interest in it.

Now, I can say that –

“Data Science is full of opportunities and challenges and I want to explore those opportunities.”

 

AV: You work at Puma Power as a Data Analyst. Could you describe your Daily tasks and Job role?

AV: My work as a Data Analyst encompasses various roles and responsibilities as follows:-

  1. My day starts with team meetings to address specific business objectives.
  2. Then, I collect data from specific departments and crunch, interpret and analyze it, and report the results.
  3. Then, I create presentations and reports based on recommendations and findings.
  4. I work with management to prioritize business and information needs and identify opportunities for improvement.
  5. Locate and define new process improvement opportunities
  6. I create presentations and reports based on recommendations and findings
  7. Then, I also acquire data from various sources and maintain databases.
  8. I define new data collection and analytics processes.

AV: What kind of skillset does a DS aspirant need to have to land a role in Puma Power’s data team?

PB: A candidate should have a few basic qualities to get a role in the Data Science team. Amongst others, these are:-

  1. Proficient in Python or R as a programming tool.
  2. Strong foundations in statistics, modeling, machine learning, data processing, etc.
  3. Knowledge of data structures.
  4. Numerical and logical reasoning aptitude.
  5. An analytical and inquisitive mind for problem-solving.

 

Prashant’s Kaggle Journey from Scratch to become 2x Kaggle Grandmaster

data science interview kaggle

AV: You are a Kaggle Double Grandmaster – Notebooks and Discussions. That is a RARE achievement and takes a remarkable amount of dedication and effort. What were the challenges you faced in achieving each title individually and how did you overcome them?

PB: I joined Kaggle two years ago and I faced lots of difficulties initially.

I started with notebooks but did not get a positive response from the community. It is quite difficult to gain recognition at the start of your Kaggle Journey. I realized that the fault lies in my notebooks. So, I worked on reorganizing my notebooks. I restructure my notebooks in a neat manner, writing clear and concise code with proper explanation. Slowly, I gained recognition and thereafter I received favorable results. Thus, I overcome the challenge with perseverance and hard work.

Similarly, I participated in discussions but faced lots of difficulties. So, I started posting useful resources to learn machine learning and deep learning. I started threads that are aimed at beginners. With these little tricks, I overcome the challenge on the kaggle discussions front.

Kaggle is full of challenges and opportunities, but it’s up to us how to take overcome challenges and take advantage of opportunities.

 

AV: What is your check-list of the must-do tasks for creating an expert-level notebook?

PB: I focus on different things when writing a notebook. They are listed below:-

  1. Quality content
  2. Clear and concise code
  3. Proper explanation and reasoning
  4. Easy to navigate and follow

AV: Which of your notebooks has gained maximum upvotes and what is the key to creating a popular notebook?

PB: The notebook – “ALASKA2: Image Steganalysis – All you need to know” has gained maximum upvotes.

The key to creating a popular notebook is that it should appeal to the masses. It must be a good quality notebook with knowledgeable content and it should serve a specific purpose.

 

AV: On the Kaggle discussions front, do you have a framework in mind when you answer a question or start a new thread?

In general, I don’t have a framework in mind when I participate in discussions. I try to be specific, to the point, clear, concise, and polite when I interact with someone.

 

Prashant’s Advice to the Beginners in Data Science

data science interview beginner kaggle grandmaster series

AV: We see most aspirants going for the competitions and hackathons while skimming over the discussions aspect. What is the benefit of participating in discussions as a beginner?

PB: Competitions and hackathons are great as they provide an opportunity to practice and hone your skills. But, you should also actively participate in discussions. There are lots of benefits from participating in discussions.

I think the most important benefit is that discussions will boost your knowledge and skills. Kaggle has a solid knowledge-sharing community. You can share your knowledge, clarify your doubts, ask for help, and discuss anything you want.

Discussions help you to build your profile. You will get noticed by others and build relationships with them.

 

AV: On the other hand, what are the 5 key points a beginner should keep in mind when trying to become a Notebooks Grandmaster?

PB: Notebooks GM is not an easy task. So, I will list 5 key points to keep in mind while trying to become a notebooks GM. I hope, these 5 points will help beginners to become notebooks GM.

They are listed below:-

  1. Quality

    I think that quality is the most important thing in notebooks. A beginner should produce great quality notebooks to become a notebooks GM.

  2. Knowledge

    If you share your knowledge with the Kaggle community, then you become notebooks GM relatively quickly. That’s what I did

  3. Hard work and Dedication

    Notebooks GM is a great achievement. But, you need lots of hard work and dedication for that.

  4. Perseverance

    Perseverance is another quality a beginner should have. Sometimes, you may not receive a positive response to your notebook. But, you should not be discouraged and move on.

  5. An eye for opportunity

    A beginner should always look for opportunities. If a competition is launched, then try to write notebooks associated with that competition.

     

Prashant’s Inspiration

AV: Can you list down 5 notebooks and 5 discussion threads that inspired you during your Kaggle journey?

PB: My top 5 inspirational notebooks are listed below:-

  1. Titanic Survival Prediction End to End ML Pipeline by Poonam Ligade

    Every beginner should work on the very famous Titanic dataset. I have worked on it too and I found the above notebook quite useful. As the name suggests, it is a complete End to End ML Pipeline. The author presented different aspects of Machine Learning in great detail.

  2. Comprehensive Data Exploration with Python

    While working on a problem, we should first explore the dataset to gain insights about it. The above notebook, tells just about that. This notebook describes the data exploration process in detail.

  3. Titanic Top 4% with ensemble modeling

    The above is a very useful notebook on the Titanic dataset. It teaches us the machine learning modeling process step by step. It also teaches us how to make predictions using ensemble models.

  4. Stacked Regressions : Top 4% on LeaderBoard

    This is a high-quality notebook by Serigne. He describes how to approach a regression problem and how to make predictions using stacked regressions in great detail.

  5. Credit Card Fraud Detection Predictive Models

    This is a gem by Gabriel Preda. He describes how to solve a classification problem using Predictive Modeling.

 

The top 5 inspirational discussion threads are listed below:-

  1. How to Learn Data Science on Kaggle

    The above discussion thread presents various resources to learn Data Science on Kaggle.

  2. How to Become a Data Scientist at Your Own

    The above post is aimed at beginners. The author describes how to become Data Scientist at your own.

  3. Chai Time Data Science by Sanyam Bhutani

    In the above post, we get links to a youtube channel where we can see lots of interviews from kaggle GMs and competition winners.

  4. Six steps to more professional data science code

    In the above post, we get to know how to write more professional data science code.

  5. Intro to Machine Learning Course Discussion

    In the above thread, we can chat with other Kagglers about various aspects of Machine Learning.

 

End Notes

Another enriching interview with another Kaggle Grandmaster. I hope this adds value to your journey down the road.

This is the ninth interview of the Kaggle Grandmasters Series. We recommend you go through a couple of the previous interviews as well-

What did you learn from this interview? Are there other data science leaders you would want us to interview? Let me know in the comments section below!

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Analytics Vidhya

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