Machinehack-Mathcothon-Car-Price-Prediction
Leaderboard standing: 55 out of 2400 participants
About Data
With the rise in the variety of cars with differentiated capabilities and features such as model, production year, category, brand, fuel type, engine volume, mileage, cylinders, colour, airbags and many more, we are bringing a car price prediction challenge for all. We all aspire to own a car within budget with the best features available. To solve the price problem we have created a dataset of 19237 for the training dataset and 8245 for the test dataset.
Dataset Description
Train.csv - 19237 rows x 18 columns (Includes Price Columns as Target)
Attributes
ID
Price: price of the care(Target Column)
Levy
Manufacturer
Model
Prod. year
Category
Leather interior
Fuel type
Engine volume
Mileage
Cylinders
Gear box type
Drive wheels
Doors
Wheel
Color
Airbags
Test.csv - 8245 rows x 17 columns
Sample Submission.csv -Please check the Evaluation section for more details on how to generate a valid submission
Skills
Multivariate Regression
Big dataset, underfitting vs overfitting
Optimizing RMSLE to generalize well on unseen data
Evaluation
What is the Metric In this competition? How is the Leaderboard Calculated ?
The submission will be evaluated using the RMSLE metric. One can use np.sqrt(mean_squared_log_error(actual, predicted)) to calculate the same
This hackathon supports private and public leaderboards
The public leaderboard is evaluated on 70% of Test data
The private leaderboard will be made available at the end of the hackathon which will be evaluated on 100% of Test data
The Final Score represents the score achieved based on the Best Score on the public leaderboard
How to Generate a valid Submission File
Sklearn models support the predict() method to generate the predicted values
You should submit a .csv file with exactly 8245 rows with 1 column(Price). Your submission will return an Invalid Score if you have extra columns or rows.
The file should have exactly 1 column.
Note: Do not shuffle the sequence of the test series
Using Pandas:
submission_df.to_csv('my_submission_file.csv', index=False)