MATHCO.THON: The Data Scientist Hiring Hackathon by TheMathCompany

MachineHack, in association with TheMathCompany, invites data science enthusiasts to build a machine learning model for predicting the estimated selling price of cars for buyers.
MATHCO.THON: The Data Scientist Hiring Hackathon by TheMathCompany

MachineHack, in association with TheMathCompany, is launching a fortnight-long hiring hackathon for data scientists and machine learning practitioners from July 02 to July 19, 2021. The winners will get the exclusive opportunity to build a rewarding analytics career at TheMathCompany.

TheMathCompany is a modern, hybrid consulting firm that builds custom AI applications for Fortune 500 and equivalent enterprises. We enable analytical transformations by building the core capabilities of enterprises to unlock immense value from data. Our well-rounded consulting model addresses pressing gaps that exist within conventional analytics service providers and off-the-shelf products. Our experts offer diverse problem-solving capabilities, with speedy delivery, reusability, and scalability of applications customized to the needs of the business – powered by Co.dx, our proprietary AI master engine. ​

TheMathCompany has won multiple awards and is recognized as a leading global analytics firm –

Show your data science mettle by participating in the hiring hackathon and get the exclusive opportunity to build a rewarding career in the analytics industry. The hackathon is open to data scientists, ML practitioners, analytics professionals, and enthusiasts to showcase their expertise. 

The challenge starts on 02nd July, 2021.

Click Here To Participate.

Problem Statement & Description

Customers looking to buy a new car expect an optimum ROI depending upon their price range. However, the sheer variety of cars with differentiated capabilities and features such as model, make, mileage, production year, category, fuel type, engine volume, colour, and accessories, makes it challenging for buyers to make an informed decision. To that end, MachineHack, in association with TheMathCompany, calls the data science community to develop a machine learning model for forecasting the price of a car within a budget with the best features available.

To solve the car price problem, MachineHack has created a training dataset of 9237 rows with 18 columns, including the ‘Price’ column as the target variable, and a testing dataset of 8245 rows with 17 columns.

The hackathon demands a few prerequisite skills such as multivariate regression, big dataset, underfitting vs overfitting, and the ability to optimize RMSE to generalize well on unseen data.

Submission Guidelines

The participants must submit a .csv/.xlsx file with exactly 8245 rows with one column (i.e. Price). The submission will return an ‘Invalid Score’ if any extra columns or rows are presented. 

Sklearn models support the predict() method to generate the predicted values.

Evaluation Criteria

The evaluation of the hackathon will be done using the RMSLE metric. One can use ‘np.sqrt(mean_squared_log_error(actual, predicted)’ to calculate the same. 

The hackathon will also support private and public leaderboards, where the public leaderboard will be evaluated on 70% of test data. On the other hand, the private leaderboard will be made available at the end of the hackathon and will be assessed on 100% of test data.

** Along with completing the challenge, participants also need to answer a Multiple-Choice Questionnaire to get shortlisted for an interview with TheMathCompany. 

The final score will represent the score achieved based on the ‘Best Score’ on the public leaderboard.


TheMathCompany will select the top three (3) winners based on the given criteria. The prize money is for interested candidates willing to get interviewed/hired by TheMathcompany.

First Prize: INR 40,000

Second Price: INR 20,000

Third Prize: INR 10,000

The hackathon will end on 19th July 2021.

Click Here To Participate.

Dataset Description:

  • Train.csv – 19237 rows x 18 columns (includes ‘price’ columns as target)
  • Test.csv – 8245 rows x 17 columns
  • Sample Submission.csv — Please check the “Evaluation” section for more details on generating a valid submission.

Attribute Description:

  • ID
  • Price
  • Levy
  • Manufacturer
  • Model
  • Production year
  • Category
  • Leather interior
  • Fuel type
  • Engine volume
  • Mileage
  • Cylinders
  • Gearbox type
  • Drive wheels
  • Doors
  • Wheels
  • Colour
  • Airbags


  • Multivariate regression
  • Big dataset, underfitting vs overfitting
  • Optimizing RMSLE score as a metric to generalize well on unseen data.

Click here to participate in the hackathon.

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