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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

7 Proven Steps to Impress the Recruiter with Your Machine Learning Projects

M
Manpreet Last Updated : 27 Mar, 2021
9 min read
This article was published as a part of the Data Science Blogathon.

Introduction

After arming yourself up with all the relevant industry skills, after putting in hours of your time, energy, and soul into your projects, comes the most
daunting task – “APPLYING FOR A JOB”. Fortunately, your profile appears at the top of the list and you get shortlisted for the interview. WHAT NEXT?

The first definite thing that comes to your mind is revising all the concepts and going through all that Machine Learning jargon, but there’s another ‘must-do that should be on your preparation list which has the maximum potential to bring the trophy home (A.K.A to help you bag that job) – ‘REVISING YOUR PROJECTS’.

ML Projects image
SOURCE: Author’s humorous efforts

There is no escaping when it comes to talking about one of your projects. You sure know all the Machine learning hacks and concepts but they are hard of any use if neither of them ever sees the face of a Machine Learning model (i.e. executing on those skills and coming up with solutions that matter). One sure-shot question you’ll always face in a Data Science interview will be regarding your projects. Recruiters specifically ask this question to know:

    1. how effectively you can put your Machine Learning skills to use,
    2. how much efforts you’ve put into building a project,
    3. your thought process and capability to navigate to a solution,
    4. whether you really understand what Machine Learning truly is, and
    5. your
      ability to communicate your methodology and solution to the clients.

These 5 points will be strategically focused on throughout this article.

This question may seem a bit daunting to answer at first but with a clear understanding of the project and a concise way of talking about it, this question can turn out to be the only question that lets you steer the interview in your favor and impress the recruiters with what you already know. This question can be thrown your way in any of the following ways:

    1. Can you walk us through one of your projects?
    2. Can you give us a brief of an exciting project you’ve worked on?
    3. What’s
      the recent project you’ve worked on?

There is no one-size-fits-all answer to this question but some structure can be brought to it and worked upon based on the nature of the project, its
complexity, and your view of the problem statement.

In this article are listed 7 basic steps (points) which you can keep in mind while structuring your answer and briefing about your project. These 7 steps have been ordered in a linear way and the answers in accordance with each of these steps can definitely help you form a concrete version of your response.

It is not necessary to follow it in a linear fashion and your responses can be modified according to your needs.

 

1. Select the right project

This is the most crucial step in the process. It is not a great practice to talk about projects which are irrelevant to the company you’ve applied to.

Suppose the company builds robots that interact with the user and act accordingly. Intuitively, you know that such a company deals with a lot of Natural Language Processing (NLP). In such a case, it is almost irrelevant to talk about a project that predicts house prices based on some numerical features.

Having a relevant project that has a use case that would be of interest to the company and aid its operations will be the wisest choice you’ll make. Adding irrelevant projects will only indicate that you cannot prioritize well. The selected projects could be from your current organization, internships, or datasets chosen from online platforms (Eg. Kaggle, UCI ML repository, etc).

 

2. Brief about the project (and relevant stakeholders!)

There has to be absolute clarity when it comes to a briefing about your project as it is the first step that will grab the interviewer’s attention. Explain in easy words what it is that you were trying to achieve with this specific project. A minimum of 5-7 lines should suffice in explaining the problem statement at hand.

Specifying the stakeholders of the project will indicate to the recruiter that you have enough knowledge of the project and know exactly how it can be implemented in a real scenario. It also showcases your business acumen associated with the problem, how well do you know the problem, and how you can articulate it to the mentioned stakeholders of the project in a way that will give them key insights.

It could be a regression problem predicting the value of air tickets at a specific time of the day or a classification problem predicting whether a customer will buy the insurance provided by the company or not. Having a thorough knowledge of the problem statement will help you think of the key stakeholders involved.

“A stakeholder is someone who will be directly affected by the findings or the predictions at the end of the project cycle.”

In the case of the air-ticket prediction problem,

  • it could be a traveler who is comparing prices of flights at different times on a ticket-booking website, the team members who want to ensure that they display the lowest prices on the website to keep attracting customers,
  • the marketing team wants to use the cheapest price predictions as their USP (Unique Selling Point) and market the service accordingly.

There can be various other stakeholders involved apart from the ones mentioned above.

 

3. What does one single row feature?

One single row represents exactly what the problem is trying to solve. A single row comprises all the features used and the dependent target variable that the Machine Learning model will predict. One way to talk about it is to start with the dependent (target) variable and explain what the final prediction would look like. The features can then be talked about by dividing them into categorical features and numerical features.

ML projects Flight Price Prediction Row
SOURCE: Flight Price Prediction Row

You might ask ‘Why this is even needed?’ It’s because experience has proven that talking about every single column (during EDA) doesn’t give the exact gist of the features involved and there are chances you might end up looping in your own explanation trying to figure out which column to talk about next. Whereas, focusing on just one row makes the explanation much concise and easy for the recruiter to comprehend.

 

4. Where did your data come from?

It is normal to think that this should be the first point while starting to talk about your project but it can be perceived the other way around too. After
talking about your project objective, its stakeholders, and the features you intrigue the interviewer to know more about the project. After you’ve generated enough curiosity for the approach you’ve taken to solve the problem you can always mention the source of your dataset.

This dataset could come from the following sources:

    1. Any coursework you were are a part of
    2. Dataset extracted from online repositories (Kaggle, UCI ML Repository, data.gov.in, etc)
    3. Ethically mined data using third-party APIs

You should always reveal the source of your dataset as it marks the authenticity of the project you’re talking about.

 

5. Exploratory Data Analysis(in B-R-I-E-F)

This section is a “TRAP!”

Your dataset has so many features that you can have that massive urge of talking about each one of those (and that too in detail!). This is the section where you have to keep in mind that there is a difference between a ‘Data Analyst’ and a ‘Data Scientist’. 

According to an article,

“Some of the main differences revolve around automation of the analysis — data scientists focus on automating analysis and predictions with algorithms using programming languages like Python, whereas data analysts use stationary, or past data, and in some cases, will create predicted scenarios with tools like Tableau and SQL.”

This clearly helps you estimate the amount of time you should put in talking about the exploration you have done. It is sufficient to talk about the features and their impacts on the target variable but talking about a single feature in detail will hardly be of any help. You’ve already explained what each variable means while talking about your row above and most of the features are intuitive enough.

For example, if you have a regression problem predicting the price of a particular house it is quite intuitive to know that the bigger the area of the house, the more the value of it. So dwelling on one column and talking about it in-depth will only steal your time and indicate to the interviewer that you cannot prioritize well. Rather you can quickly skim through the EDA by showing them the graphs (if you’re allowed to use PowerPoint Presentations while talking about your project then you definitely have a very good opportunity to structure it well and present it in the most concise and smart way).

Remember, it’s your approach to solve the problem at hand and what insights you can provide to the stakeholders involved that are of interest to the interviewer.

EDA is a “part” of the process and not the whole deal.

Author
SOURCE: Author

There can be use cases which require an extensive amount of explaining the analysis. The length of your discussion should be modified accordingly if you feel there is a need to stress on a particular feature to be able to explain the model building and the approach taken.

 

6. Model Building

This is your “ARENA” which has the maximum capability of proving your skills as a true Data Scientist.

This section can be divided into 4 subparts:

  1. The approach
  2. Training Process
  3. Model Tuning
  4. Performance
    Metrics

An often overlooked phase of a project is building a baseline model. It is quite usual in the initial phases of your learning to skip this step as it is hardly ever talked about. In simple words, a baseline model is a simplistic version of a Machine Learning model that you can easily build on the dataset by doing very little preprocessing.

For example, you have a regression problem then the first Machine Learning model that quickly comes to your mind is Linear Regression. So, you use the basic dataset, do a bit of preprocessing on the data that is sufficient enough and
necessary for a model to make predictions and run your model. The score received on this model will then become a comparing point for other models you build after tuning and final processing. It creates an impression when you include baseline model while sharing your approach.

This is also where you extensively talk about your oversampling/undersampling techniques if the dataset was highly imbalanced. You can also specify the various ways in which you tackled data leakages, overfitting, bias-variance tradeoffs, and improved your accuracy while using the learning curves. There are various other aspects that you can highlight in this section and showcase the skills you have mastered and applied, some of those can be:

  1. Feature Scaling – Standardization and Normalization
  2. The encoded variables – One Hot Encoding, Label Encoding
  3. The Feature Reduction techniques used
  4. Feature engineering that was performed

One question to definitely come your way is regarding the model you finalized: ‘Which model did you choose and why?’ Relying on just one Machine Learning model for your predictions is not a good practice and therefore you test other models to finalize on the one that gives you better accuracy on the unseen data. Here you talk about the comparison between different models you experimented on and the final model you chose to make predictions.

After selecting a model, you choose a set of hyperparameters based on trial and error or using approaches like GridSearchCV or RandomizedSearchCV. Explaining the model tuning process gives you an edge and indicates to the interviewer that you are aware of the basic Machine Learning concepts.

Finally, you talk about the metric you chose to evaluate your model. Selecting an evaluation metric suitable to the use case is of utmost importance as it indicates your ability to completely understand the problem at hand and evaluate it in a way that affects the business involved directly without having to bargain on its most important aspects. It is a great indicator of your ability to analyze effectively and logically.

 

7. Model Deployment

It is one thing to build a Machine Learning model based on a training dataset in a Jupyter notebook and a totally different thing to be able to use that model to predict values on the data it has never seen before. Learning a way or two to deploy your model makes sure that you know how to take your project in the production phase and make it easier for a layman to use it without having to see the technicalities that go behind it. You could deploy it using a web app or an API. It is always highly beneficial if you have your project model deployed on any of the platforms and have it ready to show it to the recruiter to gain those extra brownie points.

If not to impress the recruiter, you’d still want to deploy it to show the world where you’ve been putting in all that BLOOD, SWEAT, AND TEARS!

Model Deployment
SOURCE: Author’s humorous efforts

Last But Not The Least:

There will definitely be some questions that will pop up in the interviewer’s mind during your explanation of the project and you should leave no stone unturned when it comes to revising your project. You should keep some questions about your project ready to be answered if and when they are asked to you.

 

Important Note:

There is no doubt you worked day in and day out to understand the nuances of the project and completed it with 100% of your potential. During your interview, it is not how many hours you put in but how concisely you can convey all the Technical as well as the Business aspects of it in the short period of time that you have.

AUTHOR’S NOTE:

Having been in this field for 3 years now, I can confidently say that my love for data and its magic only increases with each passing day. This article is the result of interviews that showed me the right way to talk about my own projects. It is curated based on all the interview questions I had to counter and refine on those experiences every time. There is never a one-size-fits-all answer but this guide can be one of the succinct ways you can organize your answers.

In case you have any feedback or wish to discuss further on this topic, please comment below or drop a text on my LinkedIn and I’d be more than happy to connect.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

M
Manpreet

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Responses From Readers

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Daksh Wahi
Daksh Wahi

Bang on ..the topic which is mostly neglected , explained beautifully 👌👌.

123

Anurag Singh
Anurag Singh

Nice article

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Manpreet Kaur
Manpreet Kaur

I am glad you liked it Anurag! Hope it helped :D

123
Gurkeerat Singh
Gurkeerat Singh

Very nice article!! Thanks for putting

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