Anant Prasad anantprsd5

## index.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8" >
    <title>Weather</title>
    <script>
        function requestJSON(url){
            var script = document.createElement('script')
            script.src = url

## facebook.py

# importing required libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# importing or loading the dataset
dataset = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00488/Live.csv')

# distributing the dataset into two components X and Y

## GSoC2017_Report_Anant_Prasad.md

      
        
          
            
              
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                anantprsd5
                / GSoC2017_Report_Anant_Prasad.md
            
            
              Last active
              January 14, 2019 06:02
            
              
                GSoC 2017 Phimpme Android, Anant Prasad - FOSSASIA
              
          
        
      
        
  
      
    This Summer, I worked on FOSSASIA's Phimpme Android project which is an open source image application that aims to replace proprietary photo editing applications. It offer features such as taking photos, adding filters, editing images and uploading them to social networks. This application features a well structured gallery to view all the photos stored in the device, an edit image section with all the transformation, stickers and tuning features, a camera with all the necessary functionalities. It also allows user to share the images online without having to install any other applications.
GSoC project page
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8" >
	<title>Weather</title>
	<script>
	function requestJSON(url){
	var script = document.createElement('script')
	script.src = url

	# importing required libraries
	import numpy as np
	import matplotlib.pyplot as plt
	import pandas as pd

	# importing or loading the dataset
	dataset = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00488/Live.csv')

	# distributing the dataset into two components X and Y