House Price Prediction Challenge &#8211; The Latest Hackathon For Data Scientists
We are back with a new hackathon and this one is a month-long challenge based on a community-contributed dataset. In this hackathon, we are challenging the machinehack community to build a regression model to analyze and accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. In this competition, provided the 12 influencing factors your role as a data scientist is to predict the prices as accurately as possible. The challenge has started on 25th Sep Friday at 6 pm IST. Problem Statement & Description In this hackathon, your goal as a data scientist is to create a regression model that provided the 12 influencing factors. It can be extremely difficult to figure out the right set of attributes that are contributing to understanding the buyer’s behavior as such. This dataset has been collected across various property aggregators across India. Click here to participate Dataset Description: The unzipped folder will have the following files. Train.csv – 29451 rows x 12 columns (Includes target column)Test.csv – 68720 rows x 11 columnsSample Submission – Acceptable submission format. (.csv/.xlsx file with 68720 rows) Attribute Description: POSTED_BY – Category marking who has listed the propertyUNDER_CONSTRUCTION – Under Construction or NotRERA – Rera approved or NotBHK_NO – Number of RoomsBHK_OR_RK – Type of propertySQUARE_FT – Total area of the house in square feetREADY_TO_MOVE – Category marking Ready to move or NotRESALE – Category marking Resale or notADDRESS – Address of the propertyLONGITUDE – Longitude of the propertyLATITUDE – Latitude of the property The datasets will be made available for download on Sep 25th, Friday at 6 pm IST. This hackathon and the bounty will expire on Oct 26th, Monday at 7 am IST. Bounties The top 3 competitors in this competition will receive a free pass to the Deep Learning 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 pointsWe ask that you respect the spirit of the competition and do not cheatThis hackathon will expire on 26th October, Monday at 7 am ISTUse of any external dataset is prohibited and doing so will lead to disqualification Evaluation The submission will be evaluated using the RMSLE (Root Mean Squared Logarithmic Error) metric. One can use np.sqrt(mean_squared_log_error( actual, predicted))This hackathon supports private and public leaderboardsThe public leaderboard is evaluated on 30% of Test dataThe private leaderboard will be made available at the end of the hackathon which will be evaluated on 100% Test data Click here to participate
We are back with a new hackathon and this one is a month-long challenge based on a community-contributed dataset. In this hackathon, we are challenging the machinehack community to build a regression model to analyze and accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are […]
We are back with a new hackathon and this one is a month-long challenge based on a community-contributed dataset. In this hackathon, we are challenging the machinehack community to build a regression model to analyze and accurately predict the house prices in India.
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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.