Ahalytix Internship Hiring AI Challenge
The aim of this challenge is to predict the "traffic volume" using the given dataset and provide EDA
Welcome to Ahalytix Internship Hiring AI Challenge!
The aim of this challenge is to predict the amount of traffic volume based on the given data and provide an EDA (in form of .ipynb and .pdf). The dataset consists of 3 csv files:
TRAIN.csv consists of 9 attributes:
- date_time DateTime Hour of the data collected in local CST time
- holiday Categorical US National holidays plus regional holiday, Minnesota State Fair
- temp Numeric Average temp in kelvin
- rain_1h Numeric Amount in mm of rain that occurred in the hour
- snow_1h Numeric Amount in mm of snow that occurred in the hour
- clouds_all Numeric Percentage of cloud cover
- weather_main Categorical Short textual description of the current weather
- weather_description Categorical Longer textual description of the current weather
- traffic_volume Numeric Hourly I-94 ATR 301 reported westbound traffic volume (Target)
This data should be used to train the model, no additional data is allowed to be used for the training process.
TEST.csv consists of the testing data required for prediction except the Target column.
Along with this, an EDA notebook and pdf have to be submitted to display data analytics skill. These files may be used as reference for the interview process.
A "SampleSubmission".csv is also provided for ease of the participants. To know the exact submission format, please check out "Submission Guidelines".
The participants have to train a model for traffic volume prediction based on the given data.
The output of the model should be "output.csv" containing the following columns in this exact order for predictions on TEST.csv:
- Index (Row index of TEST.csv data)
- Value (Target Attribute)
NOTE: Files with analytics for the given data should also be submitted in both ".ipynb" and ".pdf" along with "output.csv" in a zip format. The EDA files should include detailed analysis such as stats of various attributes, their correlations and possible regressions equations. Please provide graphs and figures to make it easier to assess. Final submission will be .zip file containing the submission files. The submission will not be auto-graded and scoring can take up to 48 hours to grade. Please find below the sample screenshot of the final submission in ".zip" format.
Attention: Please do not use WinRAR to create the .zip file. This will produce error while grading.
How to make a submission?
- Click on "My Submission"
- On the next page, click on "+ New Submission"
- Upload your ZIP file in the next page and click on "Submit for Review"
- You must submit your ZIP file by uploading it in the "My Submissions" section of this challenge.
- Your submission will NOT be auto graded. The organisers will score your submission manually.
- If there is any error in the submission, your final score will be marked as 0.
- Grading will be done by experts out of 100 based on Root Mean Square Error (RMSE) of output.csv file and EDA efficiency.
- If the scores are tied, the person reaching the score FIRST will get the better rank.
- We are using automatic anti-cheat system. Our system flags submission which have low trust scores. Manually modifying your submission CSV (via comparison of any form etc.), using image comparison techniques (pixel matching, file size matching etc.), in any form, can lead to your disqualification without any notice.
- The participants must use only the provided dataset for training.
- The submission should be in a proper format as described by "Submission Guidelines".
- Late submission will not be accepted beyond provided deadline (Indian Standard Time).
NOTE: We may request for the code files if there is any discrepancy in your score. Your score will be marked invalid or you can be disqualified if the request for code is not fulfilled by you.
- Reward: Internship Offering with stipend Rs.12,000 to Rs.15,000
- The Top 3 participants will receive certificates from "Ahalytix" as well for commendable effort.
- Top 3 participants will receive a permanent place in Dockship's "Hall of Fame".