{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting plotly\n", " Downloading plotly-5.11.0-py2.py3-none-any.whl (15.3 MB)\n", "\u001b[K |████████████████████████████████| 15.3 MB 453 kB/s eta 0:00:01\n", "\u001b[?25hCollecting tenacity>=6.2.0\n", " Downloading tenacity-8.1.0-py3-none-any.whl (23 kB)\n", "Installing collected packages: tenacity, plotly\n", "Successfully installed plotly-5.11.0 tenacity-8.1.0\n" ] } ], "source": [ "!pip install plotly" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Importing the basic librarires fot analysis\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "plt.style.use(\"ggplot\") #using style ggplot\n", "\n", "%matplotlib inline\n", "import plotly.graph_objects as go\n", "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('Subject6_smoking.csv',encoding='utf8')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
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" ], "text/plain": [ " Smoker Status Age\n", "0 Yes Alive 21.0\n", "1 Yes Alive 19.3\n", "2 No Dead 57.5\n", "3 No Alive 47.1\n", "4 Yes Alive 81.4\n", "5 No Alive 36.8\n", "6 No Alive 23.8\n", "7 Yes Dead 57.5\n", "8 Yes Alive 24.8\n", "9 Yes Alive 49.5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8Xq9GcmaK1+sdAkpTEWwG1QGjdqUsZXbbXLPDCCEyViHG/bOsMp0S7wWOGo7weDy1wIEZTTTz5gD62a4Ct+xYKIQ4iazbjmM6Jf594H88Hs8nAJvH4/kg8AgZfNRRk9tlBSoAVhY75RBkIcTJnGl2gOmacol7vd5fAncC7wd8wE3AXV6v9+EUZZsJVSRXmVbn22UoRQhxMmvMDjBd05mdUuz1en8H/O6Y5xd6vd5MHVJZBUQBXHZbuclZhBCZL+tKfDrDKS0ej8cx+QmPx7MU+PPMRppRNUAEoNhmLTM5ixAi81X4musqzQ4xHdMp8c3A7zwejw3A4/GsAJ4BvpeCXDOlBMACymm1lJgdRgiRFdxmB5iO6YyJr8fY+OrXHo/nTGAT8A2v1/vzVIWbAUUA8532Qtn0SggxRVVmB5iO6RbbrRhb0r4IfMnr9f5q5iPNqEKAcrutwOwgQoiskVUlfsIbmx6P5zkmbTmbZAdCwG0ej+c2AK/Xe1lq4p26JrfLAeQBCafVknWrsIQQpsmqMfGTzU7J5KGSk8kHrAD5FilxIcSUucwOMB0nLHGv1/tAuoKkwERxO6xyqr0QYsqyqi+mFTa5WvOjGAcsHAQe9Hq996ci2AywkRzzt8uVuDjGcCx+uDcc80V1PXbyzxaznabr1tXFBS/aLCpBlu0pPp3FPl/HWKX5A6ATWATc6fF45nu93kycZpgHKABN1zWTswgT6brOUCzRe3As2rknONbZ7g92dodjo2bnEhnFCXx7Y28gZHaQ6ZrOlfgtwBVer7dz/AmPx/ME8CyZOVd84kp8LKFFTc4i0kjTdd0fjR/qGot27g6OdW4eDPYMRuMJYBRjI7egyRFF5tFIru7ONtMp8UKg/5jnDmP8DZaJ4iRn1oSkxGe1hK4nBqPxbl8osn/nyFjni4PB/uF4IgGMYJT2XuAVYGBjb+DY2VZCZLXplPjjwMMej+erGNvPLsK4An8iFcFmQBTjb1dCcSnx2SSu6bGBaKzrQCjSuWN4rHPzYPBwWNM0IIBR2ruA1wC/lLaY7aZT4p/DOPH+FYzx5hjgBW5PQa6ZMFHiI/FExOQs4jTENC3SF4n5OkORzo7hsc62wWAgpusJwI9R2h3Ajo29gSFzkwqRftM57X4YuMnj8Xwc46CFgeQpP5kqSnI4pWssGtR0XZOl99khktDGeiOxA/tHI/tfHR498HJgdCShEwcGgR7gVWDXxt6AjG2LnDfdKYYFGKf7FAHLPB4PAF6vt3Xmo522CMkr8biua6GENlxks2bVJP5cMZbQgj3haOf+0Ujn1qHRA68OhUZ1457GAHAI47u/vRt7A2PmJhUi80xniuFNGMMpUWDy/0w6sHCGc522jb2BeJPbFSI5Q2UknvBLiWeG0Xhi6FA42rlvNNK5NTDq2zkyFsb4S3cA6Aa2Am9s7A3IMJgQJzGdK/F/Am7wer0bUxUmBUZIHuQ8FEv45+WzxOQ8OWkklhjsDkc79wbD+7f4gwffCEWiQBhjtpMPeBnwbewNpG3hTf2GTU7gIuBy4FKMG/VCTNbbtr6xwewQJzOdEo9i7B+eTYIkS7w/EhtYVZypsyFnj+TCmr7ucLTz9WC4s20w2H0wHI1jbJrWhzGz6WXg4MbeQCJdueo3bCoBLgYu03X9cqBOKSUrecWJZMXy++mEvAu42+PxfNvr9Q6kKtAMGwYWAPrrwXD3pXPkXIiZpum6HoglerrGIp17RsKdmwdHDg0cvbBmP8bwSM/G3kDaboTXb9hUDlyGUdqXAWuVUlYApVS6YojslhVTk6dT4ruB7wCfHb+hibGsXfd6vdaZDjZDXgfqgNGX/MHujy2qlBkqp0nTde1wNN7dNRbp3DUytn/zYLBvKJbQOLKw5nVgG9Cfzjna9Rs2zcUo7cuTpb1GJdtaSlucollX4g8CvwIe4egbm5ns9fEPRhNabCiW6Cuz2+TU+2lI6Hp8IBLrOhCKdu4cCXW+OBg8PJrQEsAQRmnvxpjyN5jm0l6IMZ59ua7rlymllo+/JqUtZkif2QGmYjolXgH8jdfrzaYVcMMYV4gWgJ5wtEtK/MRimh7tTy6s2TEc6nzRH/RHNT3BkdWQO4COjb2BQDpz1W/YtIIjwyOXK6UmZkRJaYsU2Wd2gKmYTonfj7ENbaYfyTZhY29Ab3K7Bkget7RrZOyN1SUFdSbHyihRTQv3hmMHOkORzu1Doc6XAsHh5MIaP0cvrBlJV6b6DZsUsAbjSvuy5JX2xF++UtoiTfaaHWAqplPi5wOfS25J2zv5hUw8nm2Sg8B8IP7swPDed80vz+lx8XBCG+0JRzv3hyKd2wIh37ah0aBmbKFwGKO0XwFeT+eWnPUbNlmBtRwZ075UKVU+/rqUtjDJrCvxf0s+sk0bxjzgYCCWiPSGY/vnOe1LzQ6VLqF4YvhQONb5xmi4c2tg9EDH0QtrxldD7tvYGwinK1P9hk15QD1HZo5crJSamDokpS0yxOwYTvF4POcBkfGj2jweTxVwD8a3u/8LfCmlCU/fIYyxcQvAruDYrtlc4sF4wt89Fu3cOxre/5J/tGvvaHjywpqDGHO0O01YWHMhR0r7QqVUAUhhi4w2a67E7wG+jTE2CnAfxvFs9wEfxFjJ+dmUpJsByXFxH7AY0J8fGN51RWXpdSbHmjFDsXh/95ixsKbdHzzoG4vGMWYP9QJdGKXdtbE3EE9XpvoNm4owFtaMT/erV0rZQUpbZI1A2/rGQbNDTMVUSnw18ByAx+NxAc3AGq/Xu9vj8fwBaCWDSzxpM8Z3DsF9o5Gh3nC0051vz7pl1rqxsKa3ayzSucdYDXmoNxJLYKxM7cM4Nu9l4FCaF9aUYQxZja+GPFcW1ogst9vsAFM1lRK3cWTS+4XAIa/XuxvA6/X6ksWe6XZjDCkA0O4ffal5XuaXuKbr2mDymDFjYc1Ib8BYWBPk6BNr+tI8R7uKSTNHgLNkYY2YZf5kdoCpmkqJvwa8H+MAiBuBp8Zf8Hg81RiLPjJackfDvcASQPtjj7+jyV16nd1iyTc722QJXU8MRGJdPqO0OzcPjgwE45rGkYU1e4DtwOE0l/YCjl5Ys3L8NSltMUs9aXaAqZpKiX8FeNTj8fwUSACXTHrtA8BfUhEsBTYCXwCCYwktvicY3r6mpKDezEBxTY/1R2K+A6FIZ8fIWOeLgyP+iKbHOVLaOzH+Eg2kubSXcfTCmsXjr0lpixwQInt67eQl7vV6n/d4PAuBFcBur9c7edFHC/CbVIWbYQcxptXlAzzZE2irLXbWp7OUopoW6UsurHl1ONS5xT86FDeOGRvEKO3xhTXDaQsF1G/YVMvRC2vmj78mpS1y0LNt6xuzZi97pevZtIr+9DS5XVcBV5EcH/9Wbc0HFhY4VqXq94sktFBPONa5PxTu3D4UOrA1MDqiGSfWHMYo7W3Ano29gdFUZThW/YZNFuAcjpT2pUqpOen6/YXIAl9qW994t9khpior9sudQc8DV47/pOWQ/7nPnDF3xkp8LJEYGV9Y80ogdOC14dCYbtwUHsBYDbkV45ixdC6ssWHs5Di+GvJipVTp+OtypS3Em2TNeDjk2JU4QJPbdQOwDmOpOd9ds/Aj8532M07l1wrGE4FDyRNrXg6Mdu0JhiMYqyGPXViTti0t6zdsygcu4MjMkYuUUoXp+v2FyHKH2tY3zj/5p2WOXLsSB3gMOG/8J4/3+J+9eYl7SiU+HIsPdI9FO18fDXdu8Y8e7AxFYhgLa/o4srDGl+aFNYVAA0cW1pyvlHKAXGULcQqy5R7fhJwr8Y29gVCT27UdY/FP/PnDIweudrv2LChwLJ/8ecljxnq7xqKdrwfHOl8cDHb3RGLHHjP2EsbCmnQeM+bCmCE0vrBmnVLKBlLaQpwmHfiJ2SGmK+dKPOl/MG7uxQEe6Rp48gvL5y8JRON9XWPRzt3Bsc7Ng8GewWh8fGFND/AGxph2X5pXQ1Zy9DFjZ6vkLoxS2kLMqKfa1jfuMTvEdOXcmPi4JrfrfRhj41EAh0UVRTRd583HjA2keY72fI5eWLM6Xb+3EDnu+rb1jf9tdojpytUrcYBHMfawjgG9EU3fhTFPO90La5Yy6WxIpdTEDotypS1E2nQBfzA7xKnI2StxgCa3y5bOm5AA9Rs2rebo1ZDV6fz9hRBv6Ztt6xu/Y3aIU5HLV+KkusCTC2vO4uiFNVXjr8uVthAZIU52HngD5HiJz7Tkwpp1HL2wpmz8dSltITLSw23rGw+ZHeJUSYmfhvoNmxwYZ4+OzxxpUEoVjb8upS1ExhsDvmF2iNMhJT4N9Rs2FWAsrBkv7QuUUvkghS1ElvpB2/rGLrNDnA4p8ROo37CphKMX1pynlMoDKW0hZoEe4B/NDnG6pMSPUb9h02Lg88kr7XPkmDEhZq2/aVvfGDQ7xOmSEk8qq22wATVWZ/EFC6///E1We3652ZmEECnzKvBLs0PMBIvZATJBWW1DFbAB+FxibGSNf9ufs2orSiHEtK1vW9+Ytj2PUklK3NCPsaFVEEgEtj+3KzLYkzWnXQshpuV3besbHzc7xEyREgf8Ha068CDJo9sA+v7yuz9q8VjaDm8QQqSeruuHgL8yO8dMkhJP8ne0DgAvAnaAyEDXkH/bn7NyLwUhxJvpuq4rpW5qW984YHaWmSQlfrQ/YOwXrgD8rzyzI9S9t93cSEKImaCUurttfeNTZueYaVLik/g7WmMYeyg4xp/r2fTrJ+Kh4V7zUgkhTpeu6y8D/8/sHKkgJX4Mf0drL9ACOAG0WDje+9x//oeeSMTMTSaEOBW6roeUUh9qW9+YtrNu00lK/K09j3EoRB7AWPfegcCOF/5obiQhxKlQSt3Rtr5xp9k5UkVK/C0kZ6v8CuPACAAOtz2+Ndzn225eKiHEKXigbX3jz8wOkUpS4sfh72iNYKzoco4/1/3Ug4/Ggv6D5qUSQkyVrusbmWXTCd+KlPgJ+DtaDwCbGB8fj4RiBx+//+H4WHBWTVESYrbRtcR2pdQNbesbZ/29LCnxk3sS6CC5ECg+Mjh26KkHH0pEwyPmxhJCvBVdSxxUFus1besbc+L/USnxk5i0mvMAyRudkYGDQ73PPPKgrOgUIrPomjasLNambD6pZ7qkxKfA39GqYcwf95Pc+TF0cE9//wt/+P+6lkjrQctCiLem61pMWSzvaFvfuMPsLOkkJT5FyYVAP8Y4zskCMPL6y77DW576ra5rmqnhhMhxuq5rSlk+3La+8Tmzs6Sb0nXd7AxZpay2oRhYD+jJBxV1V5/jOvPSdys5OSKr6VqCjns/i72kguU3/93E8z1/9tLV8jPO+eZ/kVdYetR7tFiUnT/9Ano8hq4lKDvrMqqv/jgAXX+8j6GdL1IwfxlLbvwqAIe3bCQ+Noz7khvS9nXNdrqmxVF42u+86r/NzmIGuRKfJn9H6whwL8nxcYDD7U++Mvjy07/VtcSs2J84V/U+/184qxYe9Vw00Mfwni3YXVVv+R5ly2PlrT9gzRf/jdov3MfwrjaCnR3Ex4IE97/Gmjt+jq5phA7tQ4tFGNjyBJUXvTsdX05O0BOJiK4l3p6rBQ5S4qckuePhTzH2WJnYLKv/hUcf0hLxiKnhxCmJBvoZ2rmZOee//ajnfY/+Kwvefisc55sspRRWh7GUQE/E0RNxUAqlLMbPdR0tHkFZbfQ88whVF78Xi1UO1JoJWjw2qidijVu+ds1Gs7OYSUr8FCXnkP8Q48/QAjC8u31/7zOP/LsWi2T9uX25xvfoj99U1oHXWskrmUPB/DNO+F5dS/DaP9/KK9+5gZIV51G0cDXW/AJcZ11Kxz2fwlE2F2t+IaNduyhbc3Gqv5ScoMWjAeCiLV9/e6vZWcwmJX4a/B2tPcD3MZbnWwFGD+zo6X7ygV8mwiG/qeHElAU6XsBWVEbhghUTzyWiYQ5tepj5yfHtE1EWK2u+eB9nf/0RRg/sZKznDQDmXXEja754HzXv/AzdT9xP9dUfp39zC3sf+g7dTz+Uqi9n1tNikT6lLOdt+do1sg0GcmNzRpTVNhQAnwNcQBQgr7SysPqaj3/EVlg619Rw4qS6Hvs5h1/aiLJY0WJRtEiIkpXnE3xjOxa7sSu8RliDAAAL8UlEQVRxdKgfe8kcVv/1j8krPv4Z2t0bf4XFns/cyz0Tz4UO7qGv9ffUvPs29vzia6z6zD3sffhvqb76E+RXLkj51zebJKLhA8pivWjL167pNjtLppDBuRng72gNldU23AN8CpgHRGND/aO+R39yf/W1n/yA3VW51OSI4gQWXHcLC667BYDhvVvp/bOXZTd966jP2fb3H2L17T950+yUWDCAstqwOYvQYhGG92xh7hU3HvU5B5+4n0U33IGeSDA+G1UpC1pMbp9MRzw0/IKtoOTatvWNw2ZnySQynDJD/B2tUeBfgb0kl+gnxoLRA7//0UPBzo7n5Due2SM6NMDuX3wNgNjIYXb97Eu8dvctdNz7WUqWn4er9qKJz/W/+jyFNauwl87B5iyiaFEtr919C6BOOtYuDLquExk89EtbQcnFUuBvJsMpM6ystkEB7wPqMY56A6B0TcOyinVXXW+x2Z3HfbMQ4ihaIh6N9Hd94dUf3PwTs7NkKinxFCmrbajDKPMIyUVBjjnVpXOvvPH9eUVl1aaGEyILxEMjPdGh/ubX7r7lJbOzZDIp8RQqq22YA9wKFJG84amseZa5jR+6unDB8gtMDSdEBgsPHHw26u9t3nXfl2W67klIiadYWW1DHvBhYDXGvivG8+dcWVt2zuXvslhtjuO+WYgcoyXi0bGDezYcevrhu5I7iIqTkBJPk7LahkuAdzBpeCXfvbjcfekN780rLpN5ZiLnRQN9r4cO7vnQnn+/q83sLNlESjyNymob5gG3YMxeMU4cURZVeeE76ouXr3ubxWqzm5lPCDNo8Wh4ZO8rv+lv/f2nk8ciimmQEk+zstoGB3AjsIZJs1ccFfNLqi65vtlRPnfFcd8sxCwTHji4M/Dq85/3tfzsSbOzZCspcZOU1TasBD6AcVUeHX++fG3jGteZF19nyXMUmhZOiBTTopHR4T3tDwy8+NiX/R2tYyd/hzgeKXETJW96vhs4H+Ompw5gK3Lluy973zVO9+K1ZuYTIhXG+g5sC2x/9tNdj//yBbOzzAZS4hmgrLahGvgoxt4rE+d2lqw8f0nFurc1W/MLK0wLJ8QMiQUDfcN7tvzCv/VP35ax75kjJZ4hymobLMDVwBUYwysagLLYLBX115xbsuzcKyz2/CITIwpxShKRseHhXW0bD7/89Nf8rz63x+w8s42UeIYpq20ow7gqrwFGx5+3OJx5lRe848LCxWsulrnlIhto8Vg4uG9b6+EtT96bCI/+MXlOrZhhUuIZKnnj8z1ABZNmsdiKy52VFzRfWlC97HxlsVpNCyjEcehaIhHq2v3S4Zee+nnU3/uQv6M1dPJ3iVMlJZ7BkptpnQdci7F0f2K83DGnunTO+W+/Mr9q4dlyQLPIBLquE+47sGPw5acfHju07yf+jtZBszPlAinxLFBW22AFLsMYL7djrPoEoKBmZVX52rdd4aiYt0rKXJhB17REuLdzR6DjL4+NHtj5o+TRhSJNpMSzSFltgx24BrgIYzrixBhjftXCsvK1V17onLtkrZKVnyINtHgsEjq4+5XBrc/8KTp46NfANtnvJP2kxLNQ8ji4dwJnY5ztOTHMYisszS9fd9V5hQtXX2C15xeblVHMXoloeDj4xvaX/Vv/9FQ8NPwY8LK/o1UzO1eukhLPYskl/FdiHEBRzKQboMqaZylfe8WZxWece5GtsETO+RSnLR4a7hvevaXdv/3ZJ/V4rAXYK1fe5pMSnwWSc8zXYRS6G2Nq4sS/2JKV9YtLV1/QYHe5l8m4uZgOXdMSkcFDe0b2vLRtaOfmJ4D/8Xe09pmdSxwhJT6LJGezLMGYzbIY4wZoYvx1R8X8EteZl6wtmL9srTW/oMyclCIbxEeHe0Z9O7b7X31+e3zEvxl4zN/ROmJ2LvFmUuKzVFltQznG/uUrARuTDqQARcmK8xYXL193bn7F/NXKasszJ6XIJFosGhrr3b99aMfmXaGuXR3AS8CzskQ+s0mJz3LJGS3nJx/zMK7O4+OvWxwFea41DasKF64+y+6qPEMpi8WkqMIEuq7pUX/fnpF9r+wY2rF5ux6P7gQ2+TtaD5mdTUyNlHgOKattqADeBqwCSjhm7DyvZE6Ba81Fa5zzl63JKy6rkUKfnXQtEY8O9b8x1r339aFdbXtjQwO7gOeB7f6O1sTJ3i8yi5R4DkqOnS/FWEC0FGNP89HJn2MrLM0vWVG3rGD+shX2cvcyi83uNCGqmCFaLBqKDB7aHeratWdoV3u3Fgn1AluB5/wdrXIYcRaTEs9xZbUNNoxThi4EFgAFGIV+ZN6vxaqKl55TU7ho9Yr8OQtW2AqKK00JK6YlER49HO7z7Qruf3XfyL5XetD1PmAf0Ar0yPTA2UFKXExILu9fjFHoi4AyjFWh0cmfl19Z4ypedu4Kp3vxcltJ+UI5GzQzaLFoKDY80Bk+3H1gZO/WrnDP/n6gB3gNaPN3tA6ZHFGkgJS4OK7kGPr5GDNc3BizXI4adsFiVYULVrgLqpfVOCrmL8wrmbPQ6nCWpD9t7klExoZiQ/2d4YGDB0YP7DwwdmhvGOPfz0FgC8YYt8wsmeWkxMWUlNU25AO1GIuK5mGsEFUcW+oY89ELF65emF9ZU2N3VS60FhS7lbLIIqPTFB8LDkQDfZ2Rft+B4P4OX+TwwTjGlgsDQDfGlMB9cnMyt0iJi1NSVttQCqzAKPYqjH3P8zh2PB2wOovshYtqq/Mr5rvzSuZU2YpKq6zO4koZhnlrWjwWSYRGemNBf190qL83MnCwN9S9158IDSuMrRX6AB/Gjckuf0dr/IS/oJjVpMTFjEju47IYOAuoBioBJ8aYevjN71A45lSXOt2Lquzlc6vyissr84pcVVZnUaWyWG3pS24eXde0xNjo4fjoUG9seKA3MtjTN9bzRm9k4GAQ4wZzAqO0e4H9GKXdK5tNicmkxEVKJPdzcWNcrS8CSjFulBZiXLFHmLQv+gRlUflVNS5H2dwyW3FZia2gpMTqLCqx5heWWh3OEovdWWKx5eWn7ys5dbqWSGjRyLAWHRtJhEcD8dBwIBYM+GPDhwPRwZ5A5PChYV2L5wMWjD+LYcCPUdq7MIZIRmQWiTgRKXGRVmW1DU6M4ZdlGFfsZYAL48ozD+Pq86hVpceyOAryHOVzS/JK55TkFZWVWJ1FRZY8h8NiszuULc9hseU5lC3Poax5DmW12ZXV5rBYbQ4sVvup7P+la1pCT8QjeiIW1uKxsJ6IRbRYLKzHo2EtHo1osUhYi4bDWjQSSYSDodjw4eFooG84Nnx4fFdJO8ZcfC35dY0Cg8nHXoyr7MMyli1OhZS4yAjJPdIrMK7e5wLlGMVegHE0nRNjdowNowjHN/ea+n/AyqKszkK7stgsoOvGP7o+8Uvok58D0NE1TdNjkeOVqzWZJw/jajqRzDaGUdQhYIQjY9gDQEAODBYzSUpcZIXk8EwRxnYBVRhFX4JxlTtepPbkj8c+LBgzaY6lJ58f/59ATXpOw5gfP/6IHfNxBKOoR4AhoB9jOCQoY9YinaTExayW3GLAilHm4/+xT/6P/njPJWQsWmQDKXEh0ih5KMdzwPd0XX8s+ZwHuFnX9WtNDSeykpS4EGmmlDoT+C1wLsZ3CVuBa3Vd32tqMJGVpMSFMIFS6p8wxtQLgRFd1/9WKfUx4DaMsf1W4HMY4/n3A2sxxurv03X9XnNSi0yUE4sqhMhA38ZYJh8F6pJX5+8FGnRdjyul7gNuxJiCOEfX9bMAlFIuswKLzCQlLoQJdF0fVUo9AgR1XY8opa4C6oH25Fx2J8a0xCeAlUqpHwJ/BJ40K7PITFLiQphH48g+Mwr4pa7rdx37SUqps4HrgNuBG4Bb05ZQZDw5fkuIzPAU4FFKzQFQSlUopRYqpSox7l39Fvgmxi6SQkyQK3EhMoCu69uVUt8GnlJKWTAWFH0aYxXoL5JTE3XgKybGFBlIZqcIIUQWk+EUIYTIYlLiQgiRxaTEhRAii0mJCyFEFpMSF0KILCYlLoQQWUxKXAghspiUuBBCZDEpcSGEyGJS4kIIkcWkxIUQIotJiQshRBb7P8NwOEhbVgDkAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# how much percentage Gender in the dataset\n", "\n", "df['Smoker'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',shadow=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# how much percentage Gender in the dataset\n", "\n", "df['Status'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',shadow=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# boxplot for show describe age \n", "\n", "plt.boxplot(df[\"Age\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeSmoker_NoSmoker_YesStatus_AliveStatus_Dead
021.00110
119.30110
257.51001
347.11010
481.40110
536.81010
623.81010
757.50101
824.80110
949.50110
\n", "
" ], "text/plain": [ " Age Smoker_No Smoker_Yes Status_Alive Status_Dead\n", "0 21.0 0 1 1 0\n", "1 19.3 0 1 1 0\n", "2 57.5 1 0 0 1\n", "3 47.1 1 0 1 0\n", "4 81.4 0 1 1 0\n", "5 36.8 1 0 1 0\n", "6 23.8 1 0 1 0\n", "7 57.5 0 1 0 1\n", "8 24.8 0 1 1 0\n", "9 49.5 0 1 1 0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfobject=pd.get_dummies(df)\n", "dfobject.head(10)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "df=dfobject.drop(['Smoker_No','Status_Alive'],axis=1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeSmoker_YesStatus_Dead
021.010
119.310
257.501
347.100
481.410
536.800
623.800
757.511
824.810
949.510
\n", "
" ], "text/plain": [ " Age Smoker_Yes Status_Dead\n", "0 21.0 1 0\n", "1 19.3 1 0\n", "2 57.5 0 1\n", "3 47.1 0 0\n", "4 81.4 1 0\n", "5 36.8 0 0\n", "6 23.8 0 0\n", "7 57.5 1 1\n", "8 24.8 1 0\n", "9 49.5 1 0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "df.rename(columns = {'Smoker_Yes':'smoker', 'Status_Dead':'status'}, inplace = True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Agesmokerstatus
021.010
119.310
257.501
347.100
481.410
\n", "
" ], "text/plain": [ " Age smoker status\n", "0 21.0 1 0\n", "1 19.3 1 0\n", "2 57.5 0 1\n", "3 47.1 0 0\n", "4 81.4 1 0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "smoker\n", "0 230\n", "1 139\n", "Name: status, dtype: uint8" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary=df.groupby([\"smoker\"])[\"status\"].sum().round(0)\n", "summary" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "df.loc[df['Age'].between(18,34), 'age_group'] = '18-34'\n", "df.loc[df['Age'].between(34,54), 'age_group'] = '34-54'\n", "df.loc[df['Age'].between(55,64), 'age_group'] = '55-64'\n", "df.loc[df['Age']>65, 'age_group'] = '+ 65'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Agesmokerstatusage_group
021.01018-34
119.31018-34
257.50155-64
347.10034-54
481.410+ 65
\n", "
" ], "text/plain": [ " Age smoker status age_group\n", "0 21.0 1 0 18-34\n", "1 19.3 1 0 18-34\n", "2 57.5 0 1 55-64\n", "3 47.1 0 0 34-54\n", "4 81.4 1 0 + 65" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from os import getcwd, path\n", "import plotly.express as px\n", "import plotly.offline as pyo\n", "pyo.init_notebook_mode()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "status\n", "0 AxesSubplot(0.125,0.125;0.775x0.755)\n", "1 AxesSubplot(0.125,0.125;0.775x0.755)\n", "Name: Age, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6RP/13sQH8S4jTTuhtBzl8+Zw4qmoUz6MOvJ47P2/x776/PQnJMFbLf2sKMtOykKcPqX4zMFzue4DCzi4Ko+GriF6h0b40PJifnPWopQkFHCWkmnpDdPRH07J/URipaqmMtFPwPg2hCnLaK3zgD8Blxtj4trCTmt9MU6TGsYYSktjW9oiEAjEXNZN6RInQEdrE8HKauZ4PN7JPlN7+ffouLqJkTt+zpzrbiWQwp0r23qHaOoZ5mMHVe+OLRnf+2NK4ZgV8xJ6TYg91kMWZ3DHyy20hYPsU5qYiZXxSKefJy/Gmqqk0gCM/V9aA+yMtYzWOgMnofzeGHPfmDLNo01kWutKoGWimxtjbgFuiT61bW1tMQVdWlpKrGXdlC5xAkRaGqFynufjneoztZ+/Avujr9P+w3/F9+2foXL3fCHFeDyz3flban6O3R1bOn3vY4212Ocsy/9qXQv75Mc/239vzcTPNBGqqmIbXJOq5q+1wDKt9SKtdRA4F3hgXJkHgAu01kprfSTQFU0WCrgdWG+MuX6Ccy6MPr4QuD95b0HsLTs4gN0V8uTyLPFQxaX4vngltLcSueWn2Eji9iaZyvqWfoJ+xeI5M3tUVE6Gn4q8DLZ2yLDidJSSpGKMCQOXAX8H1juHzDqt9SVa60uixR4EtgKbgVuBS6PHjwbOB07UWr8a/To9+tq1wCla603AKdHnwqs6Wp1/0zypAKilK1Gf/AK89Qr2vrtScs/1rf3sU5I16bbBM8mS4iy2xjHLX3hHyuapGGMexEkcY4/dPOaxBb40wXlPM3F/C8aYduCkxEYqkqbdaZ1UMyCpAPje/0Ei9duwf/9fIjWL8B15fNLuNTwSoTY0wIeWp76PwQ2L52TxzPZueoZGyAum36CO2Uxm1IuUsW3RLq8ZklQA1Cf+BfZZhb3zl0ldyqUuNEQ4AksTuNaWly0udiZXbpPaStqRpCJSp6PFWfK+aI7bkSSMCgScPe7LKon8+ofY7VuScp/NHc6kx9kyy3x0oUrpV0k/klRE6rS34k/TOSpTUbn5+C6/BnJyidxwzTubkCXQ5vYB8oM+5iZgM6x0UJQVYE6Wn9qQ1FTSjSQVkTK2vQVfWYXbYSSFKi7Fd/m/QWSEyH9cjW3akdDrb+4YYElJciY9etX8oky2h4bcDkPESZKKSJ32FvxzZ2ZSAVCVNfi+8UMIDxP56VXYHXUJue5gOML20OCs6U8ZNb8wk/quQSIe2yRNTE2SikgJOzwMXZ3402ghyT2hahbh++aPwecj8tNvYze9Nf1J06gNDTJiZ09/yqj5RZkMjlhae725pbOYmCQVkRqdbWAt/hna/DWWqqzBd8W1kFdA5PqriTz/xF5db3O7068w22oq8wqdRTOlCSy9SFIRqRFy9kH3FafX6sR7SpVV4LvqJ7B4X+xtPyPy17v3eMn8zR0DFGb5Kc1J2bQyT5gX3bNle5eMAEsnklREStguZ+80f7G3Fr9LJmdU2L+hjjwBe///YH97AzYcf1POlo4BlhZnzapOeoC8oJ+S7IAklTQzu/70Ee4JdQDgKy6FgdnTnKEyMuCzl8PcSuwD/4PtbMP3xStRObEtQjk8EqGha5DDqlOzaKXXzCtyOutF+pCaikiNUAcEMlC5+W5HknJKKXxnnYv6zOWw6S0i134L2z7hgtrvUd81xIiFhUXJ2b7X6+YXBqnvGpIRYGlEkopIjVAHFBXPuiacsXxHnYjvq9+DUAeRH38Tu33rtOfUhpy/0hfNma1JJZOhEUtzj4wASxeSVERK2C4nqcx2asVqfFdeB34/kZ9dja3dNGX52s4Bgn5FZYK3D04X84uksz7dSFIRqdHVAYUzZ82vvaGq5uP713+H7Bwi1/8/7JYNk5atDQ0yrzATv2921vCqo8l0567Z0w+X7iSpiNQIdaCKStyOwjNUWYUzSTKvgMjPvzdpYqkNDc7api+AvEw/hZl+dnZLUkkXklRE0tmBfhjoh0Jp/hpLlZThu+LHUDiHyC+vwTbUvuv1UH+YroGRWdtJP6qqICg1lTQiSUUkX3SOivSpvJcqKsH3tWsgmEnkF9/Htjbtfm1btJN+wWxPKvlBdnRLR326kKQiki86R0VJn8qEVGm5s8Lx8BCRn38X290FOJ30AAtn+J7006kqCNLZH6ZveMTtUEQMJKmIpLNdTlKRmsrkVPV8fF/5rjPc+OZrseFhakODlGQHKMicWfvPxGu0s75RaitpQZKKSL6QJJVYqCXLURd+GTauw959K7WdgyycxZ30o6oKnKSyQ/pV0oIkFZF8XR2QEYTsXLcj8TzfEcehTvsoQ0/+g4bQwKzvpAeoyMtAgYwASxOy9pdIPplNHxf14U+zc3sLYRQLfP1uh+O6zICPstyAjABLE1JTEUlnuzplOHEclM9P3UnnAbDgkf/CjkgHdVV+UGoqaUKSiki+UAdK+lPiUjccJKAsVZtexP7zIbfDcd3oXJU93ZNGpI4kFZF8IVmiJV61oUHmF2XhX7Eae/9/Y3d1uh2Sq6ryg/QOR+galFqb10lSEUllB/pgsF9GfsWptnOAhXOy8J13MQwNYe/7L7dDclV1gawBli4kqYjkCsls+niFBsJ0RpdnURXVqONPwz73GLZlp9uhuaYqX4YVpwtJKiK5ukZn00tSiVVddHmW0Tkq6tSPgj+A/ds9boblqrLcDPwKmmRfFc+TpCKSysrEx7jVdkaTSnSOiioqRh13KnbN4+9aG2w28fsUZbkZNPVITcXrJKmI5BpdokVqKjGrDQ0wJztAYdY708jUB84BwD4xe0eCVeQHaZKlWjxPkopIrlAHBIOQneN2JGmjtnPwPSsTqzklqIPeh33qH9jB2bkLYmWe1FTSQcpm1GutTwVuAPzAbcaYa8e9rqKvnw70ARcZY16OvnYHcCbQYoxZNeac7wOfB1qjh75tjHkwyW9FxCPUAYUymz5WIxHL9q4hzqp475I26sQzsC89g33xadTRJ7kQnbvK8zLoGYrQMzhC3ixfZNPLUlJT0Vr7gRuB04CVwHla65Xjip0GLIt+XQzcNOa13wGnTnL5nxtjDox+SULxGNvVKf0pcdjRPUQ4YideSHLZfjC3EvvcY6kPzAMqoiPApLPe21LV/HU4sNkYs9UYMwTcDZw9rszZwF3GGGuMWQMUaa0rAYwxTwIdKYpVJJJsIxyX8Z30YymlUEccDxvfxHa0vuf1ma4yLwNAmsA8LlVJpRqoH/O8IXos3jITuUxr/brW+g6ttUzb9poumU0fj7rQIH4F1QUTr06sjjwerMU+/2RqA/OA8rxoTUU66z0tVX0qEzWoj1/EJ5Yy490E/CBa7gfAz4DPji+ktb4Yp0kNYwylpaXTxQtAIBCIuaybvBpnpL+X1sEBcqvmkRuNz6uxjudWnDt7m1lYnENlednEBUpLaV+2El5/gZLzv5A2nyck5jOdk72NzrAvqe95tn2miRZzUtFafwh40BgT3oP7NADzxjyvAcZPD46lzLsYY5rHxHcr8NdJyt0C3BJ9atva2mIKurS0lFjLusmrcdqmBgB6MzLpj8bn1VjHcyvOjS3drJqbM+W9I/sdjP3zf9O6+W3Klu6bFp8nJOYznZsboK6tO6nvOV3+j0JqY62qqoqpXDzNXz8AGrXWv9ZaHxFnPGuBZVrrRVrrIHAu8MC4Mg8AF2itldb6SKDLGNM41UVH+1yizgHejDMukUyyN31cegZHaOsLs2Ca3R7Vgc6Pn31tbSrC8pTKvAyaZAl8T4s5qRhjVgMnA/3An7TWb2utr9ZaL4zh3DBwGfB3YL1zyKzTWl+itb4kWuxBYCuwGbgVuHT0fK31H4DngH211g1a689FX/qJ1voNrfXrwAnA12J9PyL53plNLx31sdi9PMt0uz1WzYeyCuxrL6QgKm+pyM+grS/M8EjE7VDEJOLqUzHGvAa8prW+AjgJpw/jGq31M8B/An8wxkz43Y4O931w3LGbxzy2wJcmOfe8SY6fH0/8IsW6ZDHJeNTuXvMra8pySinU6iOw/3yQSH9fKkLzjPK8IBZo7h2mZpLBDMJdcY/+0lovAb6L00meFX18K05N5N6ERifSW6gDMrMgK9vtSNJCbWiAgkw/c7Kmn9inDjgUwsMMv/VqCiLzjt3DimUEmGfF01H/JeB8YClggPOj80lGX/8T0JLwCEX6ig4nltn0santHHSWu4/l81qyHAIZDL3xEizYJ/nBecToBMhmmQDpWfHUVE7Dae6qMsZcOjahABhj+oCPJDI4kd5sV4c0fcUoYi11ocFpO+lHqWAmLFnO0OsvJjkybynK8pPpVzTKBEjPiiep/NMYc090RvxuWuuvjz42xjySsMhE+gt1yD4qMWrqHmZwxE7fST+GWrGa8LZN2O5dSYzMW5RSVOTJasVeFk9S+e4kx69ORCBiZrHW7l5MUkyvNjQAwMKiqTvpx1LLD3AebHwjGSF5VkW+rFbsZdP2qWitTxwtq7U+gXfPfF8MdCcjMJHmBvphaFCav2JUGxrEp2BeYTD2kxYuQ2XnYNe/hjrk6OQF5zEVeRm80tiLtVb66zwolo7626P/ZgJ3jDlugSbgy4kOSswAsuNjXGo7B6nKD5IZiL3xQPn9BJbvz9Dm9UmMzHvK84IMjVg6+sOU5GS4HY4YZ9qkYoxZBKC1vssYc0HyQxIzQqgdkNn0sdrSMcDKufFvZJax7/4MvfoCtq8XlfPePVhmosp8J5E09wxLUvGgeGbUS0IRMbNdUlOJVddAmLa+MEuK45/Ml7F8f7AWajcmITJv2r1asQwr9qQpaypa6/XGmBXRx/VMsmqwMWZ+EmIT6Uxm08dsS4fTSb94mpn0E8lYthKUD7t5A2rlQYkOzZPm5gZQQLN01nvSdM1fnx/z+NPJDETMMKEOyMxGZcne9NPZ2uEsz7K4OP6k4svJher52C0bEh2WZ2X4fZTmBGRYsUdNmVSMMU+PefxE8sMRM0ZINueK1ZbOASryMsgL7tm+62rJcuwLT2IjEZQvVfvuuas8PyjNXx4VzzItXwceM8a8Gl2a3gBh4FPGmOeSFaBITzKbPnZbOgZYuge1lN2WrIAnHobGeqhekLjAPKwiL4OXdvS4HYaYQDx/1nwN2BZ9/GPgeuBHwC8SHZSYAUIdKEkq0+oZHKG5Z3iPmr5GqYVLAbB1mxMUlfdV5GXQOTDCQFiWwPeaeJJKoTGmS2udD6wGfmWMuR3YNzmhiXRlrZW96WO0tdPppN+rmkp5FWRmQ92WBEXlfaMjwGRhSe+JJ6nUa62Pwtm18UljzIjWugAYSU5oIm3198HQkDR/xWDz7pFfe743iPL5Yf6iWVdTAWS5Fg+KZ5Oub+LslzIEfDR67Exg9m0/J6Y2OkdF1v2a1taOAcpyAhRkxbVf3nuoBUuxTz6MHRlB+feswz+djCYVqal4T8z/k6M7N1aNO3xP9EuId4zuTS/bCE9rY/sAS0v2oulr1IIlTu2wqWFWdNbnZ/rJyfDJfvUeFNefR1rrQpw+lLxxLz2WsIhE2tu9N730qUwp1B+muWeY0/cp2utrqQVLsTid9WoWJBWlFOV5GTKs2IPiGVJ8EXAj0AOM3Rjb4qxWLIRj9xItklSmsqGtH4B9SxOw3XJ5lbN1c90WOOqkvb9eGqjIy6C+S2oqXhNPTeVHwMeMMQ8lKxgxQ4Q6IEtm00/n7bZ+Aj5Ysjcjv6KUzw/zFs+yzvogL+7oJWItPlkC3zPiGf0VAGRnRzE92ZwrJhta+1k8J4ugPzGz4NWCJVC/DRuZHQMyy/MyGI44S+AL74jnf/N1wNVa69mxDoTYY7arU4YTTyMcsWzuGGDfsgQ0fY2at9jZGK2lKXHX9LCK/OhcFVkDzFPiaf76GlABXKG1bh/7gqxSLN6lqwO1SObETmVb5wBDI5bliehPiVI1C51lxHfUQkV1wq7rVWPnquxXLk2tXhFPUpFVisW0du9NL530U3o72km/PJE1lap5zjL4DbWzYnvhstwMfEr2VfGaeOapyCrFYnr9vTA8JH0q03izuZ+5uQFKE7hzocoIQkU1tqE2Ydf0soBPUZojw4q9Jp4hxZnAd4HzgBJjTKHW+gPAPsaYXycrQJFmZG8TIqx/AAAgAElEQVT6aUWs5c3mXg6vyU/4tVXNQuy22bMLZEVehmzW5THxdLr/HFgFfIp3doBcB3wx0UGJNLZ7Nr0klcnUdg7SPRThgIok9ANUL4C2Zmx/3/RlZwCZAOk98SSVc4BPRvdOiQAYY3YAM79HUMTsndn0klQm83pzLwD7J6FzWdUsch7sqEv4tb2oIj9I18AIfcOzYxh1OognqQwxrrlMa10GtE9cXMxKo3vTyxItk3q9qY/qgiAlCexP2a1mIcCs6VcZHQHWIrUVz4gnqdwD3Km1XgSgta4Efg3cnYzARJrq6oDsHFRWAkc1zSDDI5Z1Lf1JqaUAUFwK2bnOsOJZoHz3sGJJKl4Rz5Dib+NMgHwDyAE2AbcB18Rystb6VOAGwA/cZoy5dtzrKvr66Thri11kjHk5+todOMvstxhjVo05pxj4I7AQqAW0MaYzjvckEsyG2qWWMoV1LX0MhCMcUpWblOsrpaBmwaypqVTKZl2eE09NZSmwAfh34HvA+4wxlxtjph16obX24yxGeRqwEjhPa71yXLHTgGXRr4uBm8a89jvg1AkufSXwqDFmGfBo9LlwU1en9KdM4YUdPQT9itUVyUkq4IwAo6HWmTM0w+Vl+skN+miUJfA9Y9qkorVW0ZrCGzi1lbOAzwOvaK1/G61hTOdwYLMxZms0Cd0NnD2uzNnAXcYYa4xZAxRFm9gwxjwJdExw3bOBO6OP7wQ+HEMsIplCHShJKhOy1rK2oYfVFTlkBpK42lHNQhjoh/aW5N3DQ5xhxVJT8YpY/mdfDBwPHGmMWWCMeV90WZb3AccCX4jhGtVA/ZjnDbx31FgsZcYrN8Y0AkT/nRtDLCJJds+mnyNJZSK1oUFaeoc5rDrx81PGUtULnQezpAmsPC8ofSoeEkufyvnAV4wxa8ceNMas1VpfDlwF3DzNNSaqzYyvm8dSZo9orS/GSY4YYygtLY3pvEAgEHNZN3klzsiuEK3hYfKq55MzSTxeiXU6yYjTbNiG36c448AFFGUnZuTXRHFGcg+mFcjubCHPQ591sr73i8t6WLtjB3OKS/D79n4J/HT5PwrejDWWpLISmGyJlieA/4rhGg3AvDHPa4Cde1BmvGatdaUxpjHaVDZhfd8YcwtwS/SpbWtriyFkKC0tJdaybvJKnLZ+GwC9wSz6JonHK7FOJ9FxRqzl4fXNHFSRQ7i3i7bexFx30jhLy+nbuJ4BD33WyfreF/jDDI9YNtY3UZa798k6Xf6PQmpjraoav5v8xGJp/vIbY7oneiF6PJZrrAWWaa0Xaa2DwLnAA+PKPABcEO3DORLoGm3amsIDwIXRxxcC98cQi0iW3Uu0yN70473Z3Ed7X5jjFhWm5obVC7CzZAJk+ZjVioX7YqmpZGitT2Di5qmYrmGMCWutLwP+jjOk+A5jzDqt9SXR128GHsQZTrwZZ0jxZ0bP11r/Aadfp1Rr3QB8zxhzO3AtYLTWnwO2Ax+P4f2IJLGh6DxYWaLlPR7cGCIv6OOImryU3E9VL8S+8SJ2eBiVkYRJlh5Sme+8v+aeYfYvdzkYEVNSaQHumOb1aRljHsRJHGOP3TzmsQW+NMm5501yvB2YHRtyp4POaFKReSrv0tIzzPMN3Xx4RXFyR32NVbMQIhForIf5i1NzT5eU5kSXwJfNujwhllrGwhTEIWaCUDvkF6ICM/sv43g9sMFpFjx9n9QlW1WzAAvYHXWoGZ5U/D7F3NwMaf7yCNkaWCSMDXVI09c4zT1DPLSpkxMXFyakEzlmc6sgkDGLhhXLasVeIUlFJE6oXTrpx7DW8tuXW/EpxScPSO2wT+X3Q2UNdpasAVaRF5QJkB4hSUUkTqgDNUeSyqjHt+3iufpuPrGqNDkrEk9DVS+cPUvg52Wwa1CWwPcCSSoiIezwMHR3zciaSs/gCLWdA2xq76e5Z4iRyPRzcl/a0cONzzexqjyHc1a61CRYswBCHdjeCWcEzCjl0RFg0lnvvnhWKRZicruii0PPkD6VnsEw96/v4NGtXdSFBt/1WtCvWFKcxT4lWexbls3y0mxKcjKw1tLWF+Zvb3dy/4YOFhRlctWx1QmZ5b0nVLXTWU9DHey7arriaW3sasWLi7NcjmZ2k6QiEiM6nFileU3FWsvTdd3c9vIWQv3DrCjL5lOrS6kuCJLhU+waHKE2NMimtgEe3Bji/g1OMg36FX6l6A9HADhpcSGfP7Sc7AwXGwOia4DZHbWoGZ5UZAKkd0hSEYkxOvExjReTHIlYbn2xmYc2hVhRnsfVx1WxrGTyzcaGRyzbOgd4u62f9r4wwxFLRV4Gh1bnUZkfTGHkkygqhpy8WdGvkhv0kx/0yQgwD5CkIhJi92z6wvSsqYxELD95egdr6nv48IpivnbyCkIdU++UneFX7FOazT6l3tzlcveGXbMgqYCzX73sq+I+6agXiRHqcOZF5CV3WfdkiFjLr9Y0sqa+h88dMpfPHDyXgEv9IIk2OgJsNmzYVZUfZOcuSSpuk6QiEqPTmfioVPr9Mr53XTuPb9vFJw8o5UPL07f5bkI1C2bNhl3VBUFa+8IMRvu1hDskqYiEsGk68fG1pl7+8Hob719QgF6VfvFPZ/eGXbOgCawq2o8lTWDukqQiEiPUgUqz4cRdA2F+9vROqvKDXHpERVrWsqZVPR8AOwuWa6kucJLKDkkqrpKkIvaas41w+tVUbn2xmd7hEa44ttrdob9JpLJyoGTu7KipjCYV6Vdx1cz8SRKp1dcDQ4NQ7K1tTaeypr6bp+qcJVQWFGW6HU5y1SycFTWVrICPkpyAdNa7TJKK2HvtrQCo4jKXA4nNQDjCLWubWTQnk4/sl161qz2hqhdA8w5nKZ0Zrjo/KDUVl0lSEXuvw0kqpElS+dO6dtr7w3zhsPIZM3R4StULnA27mhrcjiTpqguC7OgemhVDqL1KkorYa3Y0qZR4v/mrpWeYP6/v4P0LClhRluN2OCmhxizXMtNVFQTpHYqwa1BWK3aLJBWx99pboxMfC92OZFp3vurM17jgoPSoVSVEeRX4A87CkjNcdXRYsfSruEeSith7Ha1QXIbyefu/U23nAE/XOXvFp3QXRpepQAAq582K5VqqZFix67z9W0CkBdvRCiXe/8vfvNlOdsA382bNx0DVLJgVw4rn5mYQ8MmwYjdJUhF7r6MV5fHhxPVdgzy7vZsz9p1Dfqbf7XBSr3oBdLZhe3vcjiSp/D5FRZ6MAHOTJBWxV2x4GLo6PT/y65432wn6FR9aPsftUFwxm5ZrqSkM0iBJxTWSVMTe6WwHaz2dVHbuGuKpul2cts8cCrNm6W4P1QsAZkW/yvzCTBq7hxgakYUl3SBJReydjjbA2xMf713XTsCn+PCK2deXstucEmfDrlkws35+YSYRK/0qbpGkIvaK9fjEx+aeIR7f1sUHlhYxJ3uW1lKIbtg1fzF2+xa3Q0m6+dFld7aHBl2OZHaSpCL2zug+HR7tqP/Tug58SvGRlbO4lhKlFiyBhm1OP9gMVpUfxK9ge5fUVNwgSUXsnbZmKJyDCnpvUcbW3mEe3RrilCWFlOTMnnkpk1qwDMJh2LHd7UiSKsOvqMwPsr1LaipukKQi9optbYKyCrfDmND/vtWOtfCRlTN/0chYqIVLAbB1m9wNJAUWFGVK85dLJKmIvdPWhCr1XlLp6A/zyOYuTlhcyNw8qaUAUFrudNbXbnY7kqSbX5hJc8+wbC3sAkkqYo/Z4WFnSLEHayr/+1Y7I9bysVmwtH2slFKwcCm2buZ31s8rCmJB5qu4IGXDYbTWpwI3AH7gNmPMteNeV9HXTwf6gIuMMS9Pda7W+vvA54HoECS+bYx5MPnvRgDQ3uzMUfFYUgkNhHl4U4jjFhZQGV1gUDjUgqXYR/6MHR5CZczcz2Z+odPHVxcaZElxlsvRzC4pqalorf3AjcBpwErgPK31ynHFTgOWRb8uBm6K8dyfG2MOjH5JQkml1iYAlMeSyv3rOxgesXxsldRSxlMLlsJIeMavWFyZHyTgU9Kv4oJUNX8dDmw2xmw1xgwBdwNnjytzNnCXMcYaY9YARVrryhjPFS6w0aTipZrKrsERHtzYybELCqgp8N6INNeNdtbXzuzO+oBPsaAoyNbOAbdDmXVSlVSqgfoxzxuix2IpM925l2mtX9da36G1np0LO7mltQkys6CgyO1IdvvLhg4GwpaPSy1lYsVlUDgHtqx3O5KkWzwni62dg7ILZIqlqk9loj1bx3+nJysz1bk3AT+IPv8B8DPgs+MLa60vxmlSwxhDaWlsE/UCgUDMZd3kVpydXR1EKqopKYt9Nn0yY+0eDPO3jZs4fmkJBy8d/zdLfGby9z608kCGt2xI+ftL9Wd6wLxh/rGli5HMfCoKYu9XSZfvPXgz1lQllQZg3pjnNcDOGMsEJzvXGNM8elBrfSvw14luboy5Bbgl+tS2tbXFFHRpaSmxlnWTW3GO7NgOcyvjuncyY737jTZ6h0Y4e1n+Xt9jJn/vI/MXY597nNZNb6PmpK5Gl+rPtDwzDMBLWxo5Yl5+zOely/ceUhtrVVVVTOVS1fy1FlimtV6ktQ4C5wIPjCvzAHCB1lpprY8EuowxjVOdG+1zGXUO8Gay34hwWGudOSoe6U/pGRrhgQ0dHFGTx2IZ7TMltdQZ52I3z+wmsIVFmfgUbJF+lZRKSVIxxoSBy4C/A+udQ2ad1voSrfUl0WIPAluBzcCtwKVTnRs95yda6ze01q8DJwBfS8X7ETjzU4aGYG7l9GVT4K9vd9I7FOHc/b3VFOBJNYsgmAmb33I7kqTKDPioLgiytUOSSiqlbJ5KdLjvg+OO3TzmsQW+FOu50ePnJzhMEasmZ+yEqpw3TcHk6xka4YH1UkuJlQoEYNE+M76mAk5n/ZvNfW6HMavIjHqxR2zjDudBRY27gQB/3dBJ77DUUuKhlu0H9duwfTN7e+ElxVm094cJDYTdDmXWkKQi9kxTPeTkuj6cWPpS9oxaeSDYCGx43e1QkmpxsTNXSZrAUkeSitgjtrEBKmqc9aRcdP/6Dqml7IlF+0BWNnbdq25HklRLirPwKXi7rd/tUGYNSSpizzQ1oCrdbfpq7xvm/vUdHLMgX2opcVKBACw/ALvu5Rk9OTAnw8/8wkw2tElNJVUkqYi42b4e6Op0vT/l7jfaGLGWT6/25lbGXqdWHuTs3Nna6HYoSbW8LJuNbf2MRGZu8vQSSSoifo0NACgXk8r2rkH+b0sXpy6bIysR7yG134EA2HWvuBxJcu1bmk3fcIR62QkyJSSpiLjZHbXOg5qFrsVw1ystZAV8aFnja8+VVUJ5NfaVNW5HklQryrIBeFuawFJCkoqIX/02yM6Fkrmu3P6Fhm7W7ujl4/uVUJiVsqlWM45SCnXIUfD2G9juXW6HkzQVeRkUZvrZ0CbzVVJBkoqIm63fBvMWujLyazAc4dYXW5hXGOSs5cUpv/9Mow4+CiIR7Kszt7ailGJ5WTZvtcgIsFSQpCLiYiMj0FCLmrfYlfvfu66dlt5hvnBYORl+d4czzwjzF0NpOXbtU25HklT7l+fQ1DNMc49sL5xsklREfFqaYHAA5i1K+a3ruwa5760OjltYwP7luSm//0yklEK97wTY8Dq2rXn6E9LU6krn/8vrTdIElmySVER8GrYBoFKcVEYill8820h2ho/PHuxOX85MpY4+GQD7zKMuR5I88wqCzMkO8FpTr9uhzHiSVERcbO1m8Aegcn5K7/unde1s7hjgi4eVU5QtnfOJpErmwn4HYZ/+BzY87HY4SaGUYnV5Dq839RGZwZM9vUCSioiL3boBFixBZWSk7J6b2we4+402jl2Qz9ELClJ239nEd9JZEGrHPve426EkzQEVOXQNjlAXkvkqySRJRcTMhoehdjNq8fKU3bNncITrntrBnOwAFx/mjQ3BZqT9DoYFS7EP3YsdGXE7mqQ4qCoPgLU7ZvbKzG6TpCJiV78NhodQS1OTVKy13LCmkY7+Ya44tpqCTH9K7jsbKaXwnamhtQn7xENuh5MUxdkB9i3NYk29JJVkkqQiYma3RDd1WpKapPLHN9p5oaGHiw6ay76l2Sm556y2+ghYeSD2z/+N7ep0O5qkOLImny0dA7T2zsy+Iy+QpCJiZjevh+IyVFHyl0Z5fGsXf3ijjRMXF3DmvnOSfj8Rra2c9wUYHiZy+/XOnKQZ5oh5+QA839DtciQzlyQVERMbGYH1r6NWHJD0e73R3Muvn29k//IcLj280vU9W2YTVVGN+tQlsP417B9vn3HL4lcXBJlXGOSZOkkqySJJRcRm2ybo63E6dJNoQ2s/P/znDiryglx5bLXMmneB75hTUCefjX3sr9jf3zTjhhkfv7CQt1r7aeyW2fXJIElFxMSuewWUD7ViddLusbGtn2ser2dOtp9/O2keedIx7xqlP4v64EewTzxM5Lorsds2uR1SwpywuACfgke3dLkdyowkSUXExL71CixcispLzjyRdc19fP+xegoy/fzw5PmU5KRuHox4L6UUvo9dhO+Sb0F7C5F//wYjv/w37KtrsMPpXXMpycngoMpcHtvaJRt3JYFMTRbTsp3tsPVt1IfOS8r1n6nbxfXPNlKRl8H3T5xHqSQUz1CHHI1vv4Owj9yPffLvRG78dwhmwvIDUCsOQM1fCvMXo7KmHp1nIxFGOtudGk9nG7azDTraINQBGRlQUAg1i1CL90WVlif9fZ28pJDrntrJ2h09HBntvBeJIUlFTMu+9DRYizr02MRe11ru39DB715uZXlZNt85roZ8afLyHJWVg/rQedgzNLz1Cvb1F7FvvoR9fS0WQCmYUwJzSqFwDsofAL8f298HPbucxBHqoG0k/O4LBzKgqBjCYejugpGwc735i1FHnoA65hRUdk5S3tMRNfnMzc3gvrfaOaImTwaDJJAkFTEtu+YJ5we9ojph1+wbHuHXa5p4Zns3R83P5/L3VZIZkNZYL1N+P+x/KGr/QwGwoQ6o24LdvgVaG7EdbdDY4MzIj4xAdg7kFaCWroDiUvJqFtIbzHKST3Gp81r0l7kNh2HnduyG17EvPo01t2P/cjfqhDNQp34k4cnF71N8eEUxt7zYzJstfbLqdQJJUhFTsrWboG4z6tzPJ+yatZ0D/PTpnezsHuLCg8o4Z0Wx/KWYhlRRMRQVo1YfFlP5nNJS+traJr5WIOD84TJ/MXzgw9htm4g8fC/2QYN9+hHURy5Ave9ElC9xf3icvKSQe9a1c+crrfzkgzn45P9gQsifhmJK9p8PQjAT9b4T9/paIxHLnS/U842Ha+kZGuGaE+fxkZUlklDEe6hFy/B/8Sp83/4ZlFVgf/dLItdega3bnLB7ZAZ8nL+6lE3tAzxZO3O3U041SSpiUra1Cbvmn6ijT0bl7F3zwKb2fr71SB23PFfHETX5/OqMRRxQIU0OYmpq0TJ837oO9bmvOaPQfvQNIr+/GdubmPW7TlhcyNLiLG5/qYVQf3j6E8S0JKmISdn7f+/MTTn9Y3t8jdBAmF+taeSbD9fR2jvMNaft6ywOmSUtryI2Sil8R56A7wc3oU4805k78/++SOTZR/d6xr9PKb56VCX9wxF+uaZRhhgngCQVMSG77hXs80+gPnjOHq311TUQ5s5XWrj4z1t4fGsXZ68o5jdnLebkfcqSEK2YDVROLr5zP4/v6uthbiX2tzcQ+clV2OhupHtqfmEmnztkLi/t7OW3L7fMuKVpUk3+XBTvYTtaidx+PVTUoM7QcZ1bFxrkoY2dPL6ti8Gw5diFBXxi/xJqCjKTFK2YbdT8xfiuuBb77KPYP91J5AdfQ514JurMc1G5eXt0zdP2mcOO7iH+sqGTrOytnLeiAL9P+vr2RMqSitb6VOAGwA/cZoy5dtzrKvr66UAfcJEx5uWpztVaFwN/BBYCtYA2xszMNbtTxLY1E/n592B4CN+lV6EygtOe09kf5rn6bp6q3cVbrf1k+BTHLsznoytLqCmUZCIST/l8qGNOwR50JPa+/8I++hfsU4+gjv0A6qSz9mgC5WcPngvAPa828tbOEF99XyXledP//xfvlpKkorX2AzcCpwANwFqt9QPGmLfGFDsNWBb9OgK4CThimnOvBB41xlyrtb4y+vxbqXhPM40Nh52//O79HQC+r34fVTlvwrK9QyNsbB/gzeY+3mjuZWPbABaoKQhy4YFlnLykUPpMREqo3HzU+ZdiTzgN+/B9ziKY//cALFuJOvz9qFWHxJxgfErxuYPncsC8Un72+BYu/cs2PrisiA8uLWJBkfxxFKtU/eQfDmw2xmwF0FrfDZwNjE0qZwN3GWMssEZrXaS1rsSphUx27tnA8dHz7wT+iSSVadlIxFlxuCuE3VELm9djX3mOcFeIwSWrGPzkpQwUlLCrpY/2vjCd/WHa+4Zp2DXE9tAgrX3OKBm/gqUl2ej9Szh6fgHzC4MyPFi4QtUsQv3LN7DnXIB97jHsC09if3+zM0O/uAy1eF+orHGadOeUQn4B5BdCdu675r4opTh9ZTmLciP88Y02Ht7Yyd/e7qSmIMiKsmyWlmRRkRdkbm4GeZl+cjJ8BKSZ7F1SlVSqgfoxzxtwaiPTlame5txyY0wjgDGmUWs9N5FBjxX5693YF56C3Z14dvc//1F9GnWZpe8q/04pNfUx9e5j73QRKsbdCcaUseN+eU98PzXmHmOub9+5L+RjOYLw6mMYVtElUp7cBbx73H7Qr6guCLJybg7zizJZPCeTFWU5ZGfIWA/hHaqkDHXmJ5wlZXbWY99+HbvxTWd+y0vPgo3wnm74QMBZMiYjCIEMWjMyKI5E+KJSnOfP5pnC5bzSvYBnO6v4hz/rPfcMRobJjITxYfHbCD4svtF/sagEdPxPmrYUvPcNvdcx3RvR7Wvxnf8l1LKVex3PVFKVVCb6TMZ/FJOVieXcKWmtLwYuBjDGUFpaOs0ZjkAgsLtsX/V8hhYti0aq3hVxtS+Aj6HdgY57edJjo8/VmMdjC7zntTHXeeeYih6zu48qLGr01feUB18gAMFMfJmZqPxC/HkFBDMCZAd9ZGf4yc7wk5PhJz8rQFlekLK8TPKC/oTUQsZ+pl4mcSZeymMtK4PV7+z/Y4cGCTc2EOlsI9IVItLVie3rcVZdHh7CDg1hhwdR1jq1eWupAD7KCB+1Wxixm2kLZ9JMNq1k0UMGffjpJcCg8hNBMaIUEd75Gpk8HcRsyl92MSaVuYW5ZOYvJbe8nIwkfw9SlVQagLEN9DXAzhjLBKc4t1lrXRmtpVQCLRPd3BhzC3BL9Kltm2SpiPFKS0vZXfago5yvCXw6pqslz7viTKgRsP0MdvczmKArJi/WxJI4E88TseYWOl81kxeZKs7i6NeKpAQXv3g+0zDQBbCH34OqqqqYyqUqqawFlmmtFwE7gHOBT44r8wBwWbTP5AigK5osWqc49wHgQuDa6L/3J/2dCCGEmFRKGsSNMWHgMuDvwHrnkFmntb5Ea31JtNiDwFZgM3ArcOlU50bPuRY4RWu9CWd02LuGKQshhEgtNQtnj9qdO8e3vE3ME9X1GKRLnJA+sUqciZcusaZLnJDaWKPNX9N2EsnQHSGEEAkjSUUIIUTCSFIRQgiRMJJUhBBCJIwkFSGEEAkzK0d/uR2AEEKkKRn9NQEV65fW+qV4yrv1lS5xplOsEufsjTVd4nQp1mnNxqQihBAiSSSpCCGESBhJKlO7ZfoinpAucUL6xCpxJl66xJoucYIHY52NHfVCCCGSRGoqQgghEkY2Eh9Ha/1T4CxgCNgCfMYYE4q+dhXwOWAE+Iox5u+uBerEcypwA+AHbjPGeGaVZq31POAuoAKIALcYY27QWhcDfwQWArWANsZ0uhXnKK21H3gR2GGMOdPDcRYBtwGrcIbHfxZ4G4/FqrX+GvAvODG+AXwGyMEDcWqt7wDOBFqMMauixyb9frv1cz9JnJ7//SQ1lff6B7DKGHMAsBG4CkBrvRJnL5f9gFOB30R/Ebkieu8bgdOAlcB50Ri9Igx8wxizAjgS+FI0viuBR40xy4BHo8+94Ks4WyuM8mqcNwAPG2OWA6txYvZUrFrrauArwKHRX4Z+nJ8dr8T5O5yf4bEmjM3ln/uJ4vT87ydJKuMYYx6J7uECsIZ39og7G7jbGDNojNmGs+/L4W7EGHU4sNkYs9UYMwTcHY3RE4wxjcaYl6OPu3F++VXjxHhntNidwIfdifAdWusa4AycGsAoL8ZZALwfuB3AGDMU/SvVc7HitIJka60DODWUnXgkTmPMk0DHuMOTxebaz/1EcabD7ydJKlP7LPBQ9HE1UD/mtYboMbd4LZ5Jaa0XAgcBzwPlxphGcBIPMNfF0Eb9ArgCp5lulBfjXAy0Ar/VWr+itb5Na52Lx2I1xuwA/gPYDjTi7OL6CB6Lc5zJYvPyz5knfz/Nyj4VrfX/4bT1j/cdY8z90TLfwWnC+X30tYlmk7o5dM5r8UxIa50H/Am43BizS2vtdkjvorUebbN+SWt9vNvxTCMAHAx82RjzvNb6BrzTLLeb1noOzl/Oi4AQcI/W+tPuRrXHPPlz5uXfT7MyqRhjTp7qda31hTgdZCcZY0a/MQ3AvDHFanCq9G7xWjzvobXOwEkovzfG3Bc93Ky1rjTGNGqtK4EW9yIE4GjgQ1rr04EsoEBr/d94L05wvucNxpjno8/vxUkqXov1ZGCbMaYVQGt9H3AU3otzrMli89zPmdd/P0nz1zjREVXfAj5kjOkb89IDwLla60yt9SJgGfCCGzFGrQWWaa0Xaa2DOJ10D7gYz7torRVO2/96Y8z1Y156ALgw+vhC4P5UxzaWMeYqY0yNMWYhzmf4mDHm03gsTgBjTBNQr7XeN3roJOAtvBfrduBIrXVO9P/BSTh9al6Lc6zJYvPUz306/H6SyY/jaK03A5lAexj0jMwAAADMSURBVPTQGmPMJdHXvoPTjhnGac55aOKrpEb0r+tf4IyuucMY8yM34xlLa30M8BTOcNLRvopv4/SrGGA+zi+fjxtjxneauiLa/PWv0SHFJXgwTq31gTgDCoLAVpyhuj48FqvW+hrgEzg/K6/gDC/OwwNxaq3/ABwPlALNwPeAP08Wm1s/95PEeRUe//0kSUUIIUTCSPOXEEKIhJGkIoQQImEkqQghhEgYSSpCCCESRpKKEEKIhJGkIoQQImEkqQghhEgYSSpCCCES5v8D37ujgV2I3aQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.groupby('status').Age.plot(kind='kde')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df=df.groupby(['age_group','status']).size()\n", "df=df.unstack()\n", "df.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "#split dataset in features and target variable\n", "feature_cols = ['Age','smoker']\n", "X = df[feature_cols] # Features\n", "y = df.status # Target variable" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "# split X and y into training and testing sets\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=16)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn import utils\n", "lab_enc = preprocessing.LabelEncoder()\n", "y_train = lab_enc.fit_transform(y_train)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#REG LOG\n", "from sklearn.linear_model import LogisticRegression\n", "classifier = LogisticRegression(random_state = 0)\n", "classifier.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "log_reg=LogisticRegression()\n", "log_model=log_reg.fit(X_train,y_train)\n", "ypred_lr_test=log_model.predict(X_test)\n", "ypred_lr_train=log_model.predict(X_train)\n", "ypred_lr_probability=log_model.predict_proba(X_test)[:,1]" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix form Logistic Regression Model : \n", "------------------------------------------------ \n", "\n", "[[220 27]\n", " [ 28 54]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix,accuracy_score,roc_auc_score,roc_curve,log_loss,classification_report\n", "confusion_mat=confusion_matrix(y_test,ypred_lr_test)\n", "tn = confusion_mat[0,0]\n", "tp = confusion_mat[1,1]\n", "fp = confusion_mat[0,1]\n", "fn = confusion_mat[1,0]\n", "print('Confusion Matrix form Logistic Regression Model : ')\n", "print('------------------------------------------------','\\n')\n", "print(confusion_mat)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sensitivity of the Logistic Regression Model: \n", "--------------------------------------------- \n", "\n", "0.6585365853658537\n", "\n", "\n", "Specificity of the Logistic Regression Model: \n", "--------------------------------------------- \n", "\n", "0.8906882591093117\n" ] } ], "source": [ "Sensitivity_Logistic=(tp/(tp+fn))\n", "print('Sensitivity of the Logistic Regression Model: ')\n", "print('---------------------------------------------','\\n')\n", "print(Sensitivity_Logistic)\n", "print('\\n')\n", "Specificity_Logistic=(tn/(tn+fp))\n", "print('Specificity of the Logistic Regression Model: ')\n", "print('---------------------------------------------','\\n')\n", "print(Specificity_Logistic)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Accuracy score of the test data for Logistic Regression model : \n", "0.8328267477203647 \n", "\n", "The Accuracy score of the train data for Logistic Regression model : \n", "0.8527918781725888\n" ] } ], "source": [ "print('The Accuracy score of the test data for Logistic Regression model : ')\n", "print(accuracy_score(y_test,ypred_lr_test),'\\n')\n", "print('The Accuracy score of the train data for Logistic Regression model : ')\n", "print(accuracy_score(y_train,ypred_lr_train))" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logit model classification report: \n", "------------------------------------ \n", "\n", " precision recall f1-score support\n", "\n", " 0 0.89 0.89 0.89 247\n", " 1 0.67 0.66 0.66 82\n", "\n", "avg / total 0.83 0.83 0.83 329\n", "\n" ] } ], "source": [ "# Computing the classification report:\n", "\n", "logistic_report=classification_report(y_test,ypred_lr_test)\n", "print('Logit model classification report: ')\n", "print('------------------------------------','\\n')\n", "print(logistic_report)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fpr,tpr,th=roc_curve(y_test,ypred_lr_probability)\n", "plt.figure(figsize=(10,8))\n", "plt.plot(fpr,tpr,color='green')\n", "plt.xlim([-0.05,1.05])\n", "plt.ylim([-0.05,1.05])\n", "plt.xlabel('FPR (1-Specificity)')\n", "plt.ylabel('TPR (Sensitivity)')\n", "plt.title('ROC curve for Logistic Regression Model')\n", "plt.grid()\n", "plt.plot([-0.05,1.05],[-0.05,1.05],'r--',linewidth=2)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }