Key Historical Data: Fill & Download for Free

These apps can definitely provide more sophisticated recommendations. The ability to take you & your friends' check-in history and create recommendations for new places to go based on historical data as well as a combination of additional factors such as venue popularity/size, trending nearby, time of day, weekday vs. weekend, home city vs. travelling city, etc.

Financial models are important tools for all business decisions. When you're looking to raise finance, buy or sell a business, assess strategic options, or simply plan for the future, you are going to need a forecast. This is likely to come from a financial model.Finance professionals usually need to work with large volumes of numerical data. However, the key to successfully handle such data is the ability to organize and structure it meaningfully to render them useful to senior management for decision-making.This entails the creation of robust and dynamic financial models facilitating accurate and efficient analysis of historical data and appropriate projection of financial performance.To further understand the scope of financial modelling, please watch this:

First of all the GO rank analyzer link is this and the code for same was made by Pragy AgarwalFind your GATE CSE 2018 MarksAs you can see above, you have to submit your answer key URL there. After this you will get your mark calculated out of 100, score out of 1000 and also the expected rank range.To directly answer how accurate the predictions were, this is the 2018 result for CSE branch:GATE CSE 2018 Result ResponsesMark calculation is 100% correct, Score calculation is ~100% correct and rank prediction too is correct within the range given. Now, I’ll detail how these are possible.To predict the mark out of 100, only thing required is answer key. And we used GO keys which were not only fully correct for last 2 years but our answer debate was also accepted by GATE. So, no surprise that this was accurate.To calculate the GATE score, we need some variables:Top 0.1% mean mark, standard deviationTotal mean + total standard deviation or 25 whichever is lower (for last many years this is always 25 for CSE branch)Since about 33000 people submitted their result (Estimate GATE 2018 Rank) we got the Top 0.1% mean and standard deviation almost precisely. So, score calculation also turned out to be preciseNow, the most difficult part - rank prediction. Since we got 33,000 submissions our rank prediction was 100% accurate within this set. But actual GATE rank in CSE is out of nearly 100,000 and we had to scale the rank to fit this interval. For this, we relied on previous year statistics with regard to how many people where in each 5 mark interval and then applied a suitable scaling factor for each 5 mark interval. This one was the only thing really being “PREDICTED” and hence we needed to give a rank range. Since, nearly one-third people actually submitted their result, the predicted rank range also turned out to be good enough.Now, for branches like ME and EE with multiple papers, we had to consider Normalization also but that was just straight forward calculation using formulae. We could accurately predict which set got penalized and which set got extra mark after normalization as we had the actual data for each paper. So, for all the branches our predictions were pretty accurate for mark and score. But, our rank prediction for other branches were not good in 2018 because we had no historical data available. This should be fixed for GATE 2019 because we now have the historical data.All the best for GATE 2019 :)