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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
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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

New Case Study for Analytics Interviews: Dawn of Taxi Aggregators

Tavish Srivastava
Tavish Srivastava Last Updated : 26 Feb, 2019
7 min read

Introduction

Cab aggregator is a new business concept in India. Today we have Ola, Uber, Taxi for Sure etc. which not only compete to provide better service to customers but also design more consumer friendly price structures. Gone are the days, when your trip cost only depended on the number of kilometers. Today it depends on a number of factors making it confusing enough to puzzle customers and the drivers. Here are a few charges you might see on your hired taxi’s invoice :

  1. Base Fare : Initial amount billed to sit in a cab
  2. Excess km fare : Billed amount on distance after complimentary ride
  3. Time fare : Billing on the time spent in the cab
  4. Minimum fare: This is the minimum amount you will be billed
  5. Tolls and excess fee : This is the excess charge you need to pay to compensate for the long distances outside main city
  6. Taxes : Taxes is are over and above the bill
  7. Premium multiplier : In crowded/congested time, you will be bill something like 1.4 – 2.5 X of the actual bill amount.

You see ? Undoubtedly, travelling in India has become a lot convenient. But, the companies have found subtle ways to get compensated well. Now, imagine comparing these taxi services with all these components together. Can we make this puzzle easier for Bengaluru, India population ? Let’s crack this puzzle for once and for all.

In this article, we’ll solve a case study of taxi aggregators. Along side, I’ll also focus on the essentials required for solving a case like a pro. Specially, consultant firms like Bain, BCG, McKinsey prefer candidates who think like a pro in solving any case study. Let’s make  you one.

Disclaimer: This case is a hypothetical scenario associated with the popular brand names. The intention is to let people relate the importance of this case study in real life. We do not intend to promote / besmirch any brand. 

You should also check out the ‘Ace Data Science Interviews‘ course if you’re struggling to land your first data science job! The course has been curated by experts who has conducted hundreds of interviews and includes multiple resources including the most comprehensive interview guide!

 

Why is structured thinking key to success in any case study?

Always remember, playing with structured thinking in any case study will put you at the front foot (dominant
successposition). The technique of structured thinking is beneficial in many ways such as:

  1. It helps you to understand & solve the problem quickly as compared to any other traditional approach.
  2. It allows you to put a framework around unstructured problem.
  3. The framework allows you to explore the hidden aspects of the problem which might get neglected otherwise.
  4. Recruiters don’t look for exact answers from the case study, but the thought process used by a candidate in solving that problem. A structured thought process is well appreciated by recruiters around the world.

Now, you have the weapon. The questions are below. Let’s get ready!

 

Resources to improve your Structured Thinking

If you’ve trembled at the thought of ‘structured thinking’, don’t worry! There are numerous ways using which you can build and improve this skill. It’s easy to build but difficult to master. With time and practice, that can also be achieved. I was terrible at solving case studies until few years back. But, I patiently worked towards it and became better with time.

Here are few useful resources to build structured thinking:

 

Basic parameters of Taxi Aggregators

In this case study, the taxi aggregator companies are selected from Indian markets. The currency used to denote travel fare is INR (Indian National Rupee). For people outside India, if you face any difficulty in understanding the severity of the travel fare, simply divide the fare by your currency’s conversion rate to get travel fare in your currency.

There are four categories of taxis:

  1. Micro : Super small taxi like Nano/E2O etc.
  2. Mini : These are hatchback options like Tata Indica.
  3. Sedan : These are Sedan options like Toyota Etios.
  4. Luxury : These are super luxurious cab options like Mercedes, BMW etc

Here are the taxi service providers you need to consider :

Aggregator 1

Ola: They have three category for Taxis : 1. Micro , 2. Mini , 3. Prime

ola

In addition to this, minimum fare of Micro is fixed at Rs. 50.

 

Aggregator 2

Taxi For Sure: They also have 3 categories of Taxis as shown:

Tfs

 

Aggregator 3

  • Uber: It does not have a micro option but has a luxury as well called XL in addition to mini and sedan.

Uber

 

Questions

  1. Which of the MICRO vehicles will be cheapest if your distance lies between 1 to 8 kms?
  2. Which MINI vehicles are the cheapest if your distance is between 1 to 10 kms?
  3. If you get a free upgrade from Ola Micro to Mini, will it be cheaper than Uber Mini for distance 2-6 kms?
  4. Uber is charging a muliplier of 2.1 and Ola is charging a multiplier of 1.4 on their Sedan Vehicles (Ola Prime vs. UBER X). Which one will cost less ?
  5. You have already booked UberGo for a multiplier of 1.5 and now you are getting a Ola Mini vehicle without peak charges? The challenge is that if you cancel an Uber you will incur a cancellation charge penalty. However, if you choose to cancel, you stand a chance to save on peak charges for Uber. At what distance will you break even on the cancellation charges on Uber, in case you choose to go ahead with Ola?

Assumptions

  1. For all trips assume that every km journey will be completed in 4.5 minutes.
  2. No toll to be assumed in all the trips.
  3. No Taxes are applied for simplicity.
  4. Multipliers are applied on final bill amount.
  5. Cancellation charges can not be waived off if the booking has been made.

 

How to approach this case study?

Before you rush to answer section, think!

thinkTry to find out your way of solving this case. This phase is known as “approaching the problem”. It requires complete understanding of the problem. Considering the fact that this case has too many numbers, it will be a good practice to check their trend. This will help you to analyze taxis fare much faster.

Using structured thinking, break the problem into 3 sections (one for each taxi class). Now, look for trends within each taxi type. Always keep the questions asked in mind. This gives you an aim to target. Once you start digging for answers, it becomes much easier to know where to stop!

 

Answers

Which of the MICRO vehicles will be cheapest if your distance lies between 1 to 8 kms?

1 . Only Ola and Taxi for sure have the option of taxi type Micro. So, if we compare the two in the given distance range, TFS (Taxi For Sure) starts lower and crosses Ola at 4 km. Beyond 4 km Ola comes out to be cheaper. The answer will be cannot be determined in the distance range.

mini compareWhich MINI vehicles are the cheapest if your distance is between 1 to 10 kms?

2.  All the three taxi aggregators have option for Mini taxi. We clearly see that Uber is the cheapest car for the distance range and also seem to be the cheapest option for distances beyond 10 km looking at the trend.

micro compareIf you get a free upgrade from Ola Micro to Mini, will it be cheaper than Uber Mini for distance 2-6 kms?

3. Free upgrade is a common scenario in Ola from Micro to Mini. So, this is a very relevant question to ask. The trend looks to be very interesting. Uber Go starts at a slightly lower rate than Ola Micro but Ola takes over after 5 km distance. So, again our answer will be cannot be determined in the distance range. However, it is interesting to notice that a Ola Micro taxi comes out to be more expensive than Uber Mini car for shorter than 5 km distance.

OlamicrovsUberminiUber is charging a muliplier of 2.1 and Ola is charging a multiplier of 1.4 on their Sedan Vehicles (Ola Prime vs. UBER X). Which one will cost less ?

4. Multipliers are often added in peak traffic hours. It becomes very difficult to compare rates in such cases. Here’s one of those scenarios. Ola is generally more expensive compared to Uber, but in such extreme multiplier cases, we see that Ola comes out to be cheaper option throughout.

OlapremvsUberXYou have already booked UberGo for a multiplier of 1.5 and now you are getting a Ola Mini vehicle without peak charges? The challenge is that if you cancel an Uber you will incur a cancellation charge penalty. However, if you choose to cancel, you stand a chance to save on peak charges for Uber. At what distance will you break even on the cancellation charges on Uber, in case you choose to go ahead with Ola?

5. What you need to keep in mind is the cancellation charges of UBER. Here is the table for OLA vs. UBER :

penalty 1

penalty 2

As can be seen from both table and the graph, the break even only happens between 13-14 kms. Hence, you should make a switch only if you want to travel more than 13 kms.

 

End Notes

This was a fun exercise and hopefully useful for Indian population, who are confused with which cab service to take. You can expect such realistic case study in your interviews which might break many myths you had about the subject. For instance, before doing this analysis, I always assumed OLA Micro is the cheapest available option for all distances. However, looks like UBER Mini can be cheaper than OLA micro for smaller distance.

Did you like reading this article ? Have you encountered any such case studies in your interview? Do share your experience / suggestions in the comments section below.

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

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Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Aiswarya
Aiswarya

Hi Tavish, A very interesting case study. I have a small query. In the text it is mentioned that Uber does not have a MINI service.But in the solution part of the article, it is been considered as Uber does not have a micro taxi service but has a mini taxi service available. Which is the correct assumption?? In case Uber has a micro service then UberGo will be a better service compared to Ola or Taxi For Sure.

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Tavish Srivastava
Tavish Srivastava

Uber doesn't have a micro option. Article has been corrected.

123
kharishraju
kharishraju

Hi Tavish Nice article.I was trying to derive the solution part of question 2 for the Mini for UBER X. There is Minimum KM that can be used to travel with the Minimum fare. I am unable to arrive at the values for uber which you shared in the solution. Can you please help me with that part alone of uber calculation.

123

Shivi
Shivi

This is very simple solution. A better approach should have how these companies analytically reach to a consensus that only these parameters are included in the calculation part. Just a thought. Regards, Shivi

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