Forest Cover Classification: Weekend Hackathon #12
Another weekend and another exciting hackathon, and this time with an open dataset. Yes, you heard it right ! In this weekend hackathon, we are using an open dataset and we have added some noise in the target variable to keep the spirit of competition right. The participants are provided with 55 distinguishing features to build a classification model that can predict the forest cover type in future. The goal of this competition is to predict the forest cover types (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data) The challenge will start on July 17th Friday at 6 pm IST. Click here to participate Problem Statement & Description The dataset has been taken from UCI, but to keep the spirit of competition right, we have added some noise in the labels. In this hackathon, we challenge all Machinehackers to predict the forest cover types (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data). The actual forest cover type for a given 30 x 30-meter cell was determined from US Forest Service (USFS) Region to Resource Information System data. Independent variables were then derived from the data obtained from the US Geological Survey and USFS. The data is in raw form (not scaled) and contains binary columns of data for qualitative independent variables such as wilderness areas and soil type (one-hot-encoded). Given are 55 distinguishing factors that can predict the forest cover types. Your objective as a data scientist is to build a machine learning model that can accurately classify the forest cover types (the predominant kind of tree cover) from strictly cartographic variables. Data Description:- The unzipped folder will have the following files. Train.csv –  29050 rows x 55 columnsTest.csv –  551962 rows x 54 columnsSample Submission – Sample format for the submission. Target Variable: Cover_Type The datasets will be made available for download on July 17th, Friday at 6 pm IST This hackathon and the bounty will expire on July 20th, Monday at 7 am IST Below are the file formats for the provided data Train.csv Glimpse of training set, all features are not included. Test.csv Glimpse of test set, all features are not included. Sample_Submission.xlsx Glimpse of sample submission. Click here to participate Bounties The top 3 competitors will receive a free pass to the Computer Vision DevCon 2020 Know more about the Computer Vision DevCon 2020. Click here to participate Rules One account per participant. Submissions from multiple accounts will lead to disqualificationThe submission limit for the hackathon is 10 per day after which the submission will not be evaluatedAll registered participants are eligible to compete in the hackathonThis competition counts towards your overall ranking pointsYou will not be able to submit once you click the “Complete Hackathon” button. You may ignore this featureWe ask that you respect the spirit of the competition and do not cheatThis hackathon will expire on 20th July, Monday at 7 am ISTUse of any external dataset is prohibited and doing so will lead to disqualification Evaluation The leaderboard is evaluated using multi-class log loss for the participant’s submission. Click here to participate
Another weekend and another exciting hackathon, and this time with an open dataset. Yes, you heard it right ! In this weekend hackathon, we are using an open dataset and we have added some noise in the target variable to keep the spirit of competition right. The participants are provided with 55 distinguishing features to […]
In this weekend hackathon, we are using an open dataset and we have added some noise in the target variable to keep the spirit of competition right
["data science hackathon", "Hackathon", "Machine learning hackathon", "Machinehack Hackathon"]
["Go", "machine learning", "programming_languages:R", "AI", "Machine learning hackathon", "programming_languages:Go", "computer vision", "Hackathon", "data science hackathon", "Machinehack Hackathon", "R", "ai_applications:computer vision"]
["AI", "machine learning", "computer vision", "R", "Go", "programming_languages:R", "programming_languages:Go", "ai_applications:computer vision"]
Experienced Data Scientist with a demonstrated history of working in Industrial IOT (IIOT), Industry 4.0, Power Systems and Manufacturing domain.
I have experience in designing robust solutions for various clients using Machine Learning, Artificial Intelligence, and Deep Learning.
I have been instrumental in developing end to end solutions from scratch and deploying them independently at scale.