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ai_all_resources

A curated list of Best Artificial Intelligence Resources

end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

fbprophet-grocey-sales

financial-risk-prediction---h2o

Hackathon

house-price-prediction

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this challenges one to build ML model to predict the final price of each home. Practice Skills

machinehack

message-polarity-prediction

All of us receive a ton of messages and emails on a daily basis. Collectively, that is a lot of data which can provide useful insights about the messages that each of us gets. What if you could know whether a certain message has brought you good news or bad news before opening the actual message. In this challenge, we will use Machine Learning to achieve this. Given are 53 distinguishing factors that can help in understanding the polarity(Good or Bad) of a message, your objective as a data scientist is to build a Machine Learning model that can predict whether a text message has brought you good news or bad news. You are provided with the normalized frequencies of 50 words/emojis (Freq_Of_Word_1 to Freq_Of_Word_50) along with 3 engineered features listed below: TotalEmojiCharacters: Total number of individual emoji characters normalized. (eg. :) ) LengthOFFirstParagraph: The total length of the first paragraph in words normalized StylizedLetters: Total number of letters or characters with a styling element normalized Target Variable: IsGoodNews The data is now available for download on. Proceed to Start/Continue Hackathon and click on the attachments to download the datasets. Data Description The unzipped folder will have the following files. Train.csv – 947 observations. Test.csv – 527 observations. Sample Submission – Sample format for the submission. Below are the file formats for the provided data Train.csv Test.csv Sample_Submission.xlsx Start Date FridayMay 01 End Date MondayMay 04 Difficulty Beginner Total Registration 322 Generic Rules One account per participant/team. Submissions from multiple accounts will lead to disqualification. The submission limit for the hackathon is 10 per day after which the submissions will not be accepted. All registered participants are eligible to compete in the hackathon. We ask that you respect the spirit of the competition and do not cheat. Use of any external dataset is prohibited and doing so will lead to disqualification. Hackathon Specific Rules One account per participant. Submissions from multiple accounts will lead to disqualification The submission limit for the hackathon is 10 per day after which the submission will not be evaluated All registered users are eligible to participate in the hackathon We ask that you respect the spirit of the competition and do not cheat Start Date FridayMay 01 End Date MondayMay 04 Difficulty Beginner Total Registration 322 Evaluation The leaderboard is evaluated on the standard f1_score metric from sklearn Start Date FridayMay 01 End Date MondayMay 04 Difficulty Beginner Total Registration 322 Prize

power-plant-energy-output-prediction

The dataset was collected from a Combined Cycle Power Plant over 6 years (2006-2011) when the power plant was set to work with a full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH), and Exhaust Vacuum (V) to predict the net hourly electrical energy output (PE) of the plant. A combined-cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST), and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is collected from and has an effect on the Steam Turbine, the other three of the ambient variables affect the GT performance.

product-backorder-prediction

telecom-churn

The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models.