my intersted orgs

1st Years/ROHIT R/ORGANISATION/ROHIT_ORG.txt

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+
+#1 Coala
+ tech-django,python,antlr,emberjs,reactjs
+ interest level-9
+ knowledge-4
+ why?-the idea seems interesting to have a language independant code analyser
+ rejection wouldnt affect much..focused on learning new stuff
+ if org not there next time-doesnt matter,focusing on learning and contributing wherever i can
+#2 BEEWARE
+ tech-python,android,javascript,java
+ interest level-9
+ knowledge-4
+ why-interesting idea and also cause interest to learn javascript and java and implement it
+ rejection wouldnt affect much..focused on learning new stuff
+ if org not there next time-focusing on learning and contributing wherever i can
+
+#3 QUILL
+ tech-ruby,rails,javascript
+ interest level-8
+ knowledge-4
+ why?-interested in web development
+ rejection wouldnt affect much..focused on learning new stuff
+ if org not there next time-doesnt matter,focusing on learning and contributing wherever i can
+
+time planned working for gsoc per week-3-4 hrs 
+

SCA2csa50.ipynb

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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "SCA2csa50.ipynb",
+ "provenance": [],
+ "toc_visible": true,
+ "authorship_tag": "ABX9TyNKEf6xifBIPYt8yyh2SWwf",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/rohit-rkmr/GSoC-2019-Preparation/blob/master/SCA2csa50.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ESW7sUudcWB1"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R513HTOQst01"
+ },
+ "source": [
+ "#**CLASSIFICATION OF IRIS PLANT SPECIES**\n",
+ "Submitted by\n",
+ "\n",
+ "Rohit R\n",
+ "\n",
+ "CS5A\n",
+ "50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0X59CFu8tt4W",
+ "outputId": "243a56b3-55e2-4bd7-b3c2-05e5dc0f4785",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 134
+ }
+ },
+ "source": [
+ "\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "#for splitting\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "#uses k-nearest neighbors modelk \n",
+ "import numpy as np\n",
+ "#for using arrays\n",
+ "\n",
+ "# Import iris dataset \n",
+ "from sklearn.datasets import load_iris\n",
+ "iris = load_iris()\n",
+ "\n",
+ "\n",
+ "# Split dataset into training and testing sets.\n",
+ "X_tr, X_tst, y_tr, y_tst = train_test_split(iris['data'], iris['target'],train_size=0.75,test_size=0.25, random_state=0)\n",
+ "# train set and test set are in a 3:4 ratio\n",
+ "knn = KNeighborsClassifier(n_neighbors=1)\n",
+ "# K=1 so looks for the first closest neighbor\n",
+ "knn.fit(X_tr, y_tr)\n",
+ "\n",
+ "d1 = float(input(\"Enter sepal length : \"))\n",
+ "d2 = float(input(\"Enter sepal width : \"))\n",
+ "d3 = float(input(\"Enter petal length : \"))\n",
+ "d4 = float(input(\"Enter petal width : \"))\n",
+ "\n",
+ "# let this be our new iris data sample \n",
+ "X_new = np.array([[d1,d2,d3,d4]])\n",
+ "print(\"Our sample - \" + str(X_new));\n",
+ "prval = knn.predict(X_new);\n",
+ "# print the type by putting the value predicted\n",
+ "print('sample species is '+ str( iris['target_names'][prval]))\n",
+ "\n",
+ "# prediction score \n",
+ "print(str(knn.score(X_tst, y_tst)*100) + ' % accuracy');\n"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Enter sepal length : 3.2\n",
+ "Enter sepal width : 2.7\n",
+ "Enter petal length : 3\n",
+ "Enter petal width : 2.6\n",
+ "Our sample - [[3.2 2.7 3. 2.6]]\n",
+ "sample species is ['versicolor']\n",
+ "97.36842105263158 % accuracy\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}