"ModernCV" CV and Cover Letter
Version 1.11 (19/6/14)
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Ever since the invention of the camera, people started speculating the birth of a video-camera that would take live moving-footage of anything. Videos were to be stored on tapes and reviewed as much as the taker or who owns the footage pleases. Audience depended on the owner of the video. As technology advanced, taking videos became easier and thus its sharing too. Videos can be taken from phones and uploaded right away to social media sites. Tampering with videos and changing their ownership became not only possible but simple. Different methods have been used to determine whether the videos are as they were when taken or not. Some of these methods will be investigated in this report.
The geological survey was carried out in Bisteutar and Jhyadi villages in the Bhimtar area. These villages lie at latitudes 27.709 N and 27.707 N and longitudes 85.670 E and 85.676 E. Bisteutar village can clearly be separated into two morphologies, the side facing Indrawati river has alluvial deposits whereas the one facing Jhyadi khola has slaty phyllites. Jhyadi village has slaty phyllites lying over low grade sandstone. Cracks 5-10 centimeters in width and 2-3 meters in depth were observed in both the villages thus implying high probability of possible landslides in the monsoon season.
This document contains the instructions for preparing a camera-ready manuscript for the proceedings of ACL-2015. The document itself conforms to its own specifications, and is therefore an example of what your manuscript should look like. These instructions should be used for both papers submitted for review and for final versions of accepted papers. Authors are asked to conform to all the directions reported in this document.
We have used the data-set on Zomato Restaurants which was available to public, and shared by an useron: "https://www.kaggle.com" .This, data-set contains restaurant’s ID,City,Country,Cuisines offered,Average Cost for two,Currency,Aggregate rating(in float data-type), Rating text(Excellent,Very Good,Good, Average, Poor, Not Rated) and some other relevant data. We have tried to visualize somebar graphs and other plots, and find some general trends to make a conclusion. Then we have usedKNN-regression method to make predictions and finally we have made conclusions.