From ee8c01179dfe3b4261ac0e53d22a5a6b254f6a37 Mon Sep 17 00:00:00 2001 From: Himani Negi <36325656+Himani2000@users.noreply.github.com> Date: Tue, 9 Oct 2018 01:51:14 +0530 Subject: [PATCH] Add files via upload --- machine_learning/NaiveBayes.ipynb | 1659 +++++++++++++++++++++++++++++ 1 file changed, 1659 insertions(+) create mode 100644 machine_learning/NaiveBayes.ipynb diff --git a/machine_learning/NaiveBayes.ipynb b/machine_learning/NaiveBayes.ipynb new file mode 100644 index 000000000..5a427c5cb --- /dev/null +++ b/machine_learning/NaiveBayes.ipynb @@ -0,0 +1,1659 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = pd.DataFrame(iris.data)\n", + "df.columns = [\"sl\", \"sw\", 'pl', 'pw']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def abc(k, *val):\n", + " if k < val[0]:\n", + " return 0\n", + " else:\n", + " return 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 1\n", + "5 1\n", + "6 0\n", + "7 1\n", + "8 0\n", + "9 0\n", + "10 1\n", + "11 0\n", + "12 0\n", + "13 0\n", + "14 1\n", + "15 1\n", + "16 1\n", + "17 1\n", + "18 1\n", + "19 1\n", + "20 1\n", + "21 1\n", + "22 0\n", + "23 1\n", + "24 0\n", + "25 1\n", + "26 1\n", + "27 1\n", + "28 1\n", + "29 0\n", + " ..\n", + "120 1\n", + "121 1\n", + "122 1\n", + "123 1\n", + "124 1\n", + "125 1\n", + "126 1\n", + "127 1\n", + "128 1\n", + "129 1\n", + "130 1\n", + "131 1\n", + "132 1\n", + "133 1\n", + "134 1\n", + "135 1\n", + "136 1\n", + "137 1\n", + "138 1\n", + "139 1\n", + "140 1\n", + "141 1\n", + "142 1\n", + "143 1\n", + "144 1\n", + "145 1\n", + "146 1\n", + "147 1\n", + "148 1\n", + "149 1\n", + "Name: sl, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sl.apply(abc, args=(5,))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def label(val, *boundaries):\n", + " if (val < boundaries[0]):\n", + " return 'a'\n", + " elif (val < boundaries[1]):\n", + " return 'b'\n", + " elif (val < boundaries[2]):\n", + " return 'c'\n", + " else:\n", + " return 'd'\n", + "\n", + "def toLabel(df, old_feature_name):\n", + " second = df[old_feature_name].mean()\n", + " minimum = df[old_feature_name].min()\n", + " first = (minimum + second)/2\n", + " maximum = df[old_feature_name].max()\n", + " third = (maximum + second)/2\n", + " return df[old_feature_name].apply(label, args= (first, second, third))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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