{ "cells": [ { "cell_type": "markdown", "id": "0f96a260-a152-4502-9075-bb80394179b2", "metadata": {}, "source": [ "# Thyroid disease prediction simple ann\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "213842b2-f184-49a4-9e24-c18f640ee470", "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import cv2" ] }, { "cell_type": "markdown", "id": "f0e6f270-4ea9-4a81-829c-b4743cdfd408", "metadata": {}, "source": [ "The most common thyroid disorder is hypothyroidism. Hypo- means deficient or under(active), so hypothyroidism is a condition in which the thyroid gland is underperforming or producing too little thyroid hormone.. Recognizing the symptoms of hypothyroidism is extremely important." ] }, { "cell_type": "code", "execution_count": 2, "id": "b492cc45-ca7d-4a57-9720-93bdee610b3d", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 5, "id": "ed7fcb88-8a3e-4755-a606-1681dbf913b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatment...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
0041.01.00000000...12.50001125.011.1401109.00000001
1123.01.00000000...12.00001102.000.9950110.46964901
2246.00.00000000...02.01351109.010.9101120.00000001
3370.01.01000000...11.90001175.000.9950110.46964901
4470.01.00000000...11.2000161.010.870170.00000001
\n", "

5 rows × 29 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 age sex on thyroxine query on thyroxine \\\n", "0 0 41.0 1.0 0 0 \n", "1 1 23.0 1.0 0 0 \n", "2 2 46.0 0.0 0 0 \n", "3 3 70.0 1.0 1 0 \n", "4 4 70.0 1.0 0 0 \n", "\n", " on antithyroid medication sick pregnant thyroid surgery I131 treatment \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 0 \n", "\n", " ... T3 measured T3 TT4 measured TT4 T4U measured T4U \\\n", "0 ... 1 2.5000 1 125.0 1 1.140 \n", "1 ... 1 2.0000 1 102.0 0 0.995 \n", "2 ... 0 2.0135 1 109.0 1 0.910 \n", "3 ... 1 1.9000 1 175.0 0 0.995 \n", "4 ... 1 1.2000 1 61.0 1 0.870 \n", "\n", " FTI measured FTI TBG measured binaryClass \n", "0 1 109.000000 0 1 \n", "1 0 110.469649 0 1 \n", "2 1 120.000000 0 1 \n", "3 0 110.469649 0 1 \n", "4 1 70.000000 0 1 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data=pd.read_csv(\"thyroid_data.csv\")\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "c1a1bc9a-8815-46b2-aba1-5d9ab36d9a2c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatment...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
3766376730.01.00000000...02.01350108.31934500.9950110.46964901
3767376868.01.00000000...12.10001124.00000011.0801114.00000001
3768376974.01.00000000...11.80001112.00000011.0701105.00000001
3769377072.00.00000000...12.0000182.00000010.940187.00000001
3770377164.01.00000000...12.2000199.00000011.070192.00000001
\n", "

5 rows × 29 columns

\n", "
" ], "text/plain": [ " Unnamed: 0 age sex on thyroxine query on thyroxine \\\n", "3766 3767 30.0 1.0 0 0 \n", "3767 3768 68.0 1.0 0 0 \n", "3768 3769 74.0 1.0 0 0 \n", "3769 3770 72.0 0.0 0 0 \n", "3770 3771 64.0 1.0 0 0 \n", "\n", " on antithyroid medication sick pregnant thyroid surgery \\\n", "3766 0 0 0 0 \n", "3767 0 0 0 0 \n", "3768 0 0 0 0 \n", "3769 0 0 0 0 \n", "3770 0 0 0 0 \n", "\n", " I131 treatment ... T3 measured T3 TT4 measured TT4 \\\n", "3766 0 ... 0 2.0135 0 108.319345 \n", "3767 0 ... 1 2.1000 1 124.000000 \n", "3768 0 ... 1 1.8000 1 112.000000 \n", "3769 0 ... 1 2.0000 1 82.000000 \n", "3770 0 ... 1 2.2000 1 99.000000 \n", "\n", " T4U measured T4U FTI measured FTI TBG measured binaryClass \n", "3766 0 0.995 0 110.469649 0 1 \n", "3767 1 1.080 1 114.000000 0 1 \n", "3768 1 1.070 1 105.000000 0 1 \n", "3769 1 0.940 1 87.000000 0 1 \n", "3770 1 1.070 1 92.000000 0 1 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 9, "id": "e6aadc21-c873-4dc0-8100-e511da4c6242", "metadata": {}, "outputs": [], "source": [ "data.drop([\"Unnamed: 0\"], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "7e705edc-9c43-49c7-8ff2-1461a2a871c8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
041.01.000000000...12.50001125.011.1401109.00000001
123.01.000000000...12.00001102.000.9950110.46964901
246.00.000000000...02.01351109.010.9101120.00000001
370.01.010000000...11.90001175.000.9950110.46964901
470.01.000000000...11.2000161.010.870170.00000001
\n", "

5 rows × 28 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine query on thyroxine on antithyroid medication \\\n", "0 41.0 1.0 0 0 0 \n", "1 23.0 1.0 0 0 0 \n", "2 46.0 0.0 0 0 0 \n", "3 70.0 1.0 1 0 0 \n", "4 70.0 1.0 0 0 0 \n", "\n", " sick pregnant thyroid surgery I131 treatment query hypothyroid ... \\\n", "0 0 0 0 0 0 ... \n", "1 0 0 0 0 0 ... \n", "2 0 0 0 0 0 ... \n", "3 0 0 0 0 0 ... \n", "4 0 0 0 0 0 ... \n", "\n", " T3 measured T3 TT4 measured TT4 T4U measured T4U \\\n", "0 1 2.5000 1 125.0 1 1.140 \n", "1 1 2.0000 1 102.0 0 0.995 \n", "2 0 2.0135 1 109.0 1 0.910 \n", "3 1 1.9000 1 175.0 0 0.995 \n", "4 1 1.2000 1 61.0 1 0.870 \n", "\n", " FTI measured FTI TBG measured binaryClass \n", "0 1 109.000000 0 1 \n", "1 0 110.469649 0 1 \n", "2 1 120.000000 0 1 \n", "3 0 110.469649 0 1 \n", "4 1 70.000000 0 1 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b963fd57-099f-47da-8bc3-11fb632a5887", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3771, 28)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 12, "id": "4dc925f4-9e56-479f-897a-65e741d4d717", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
count3771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.000000...3771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.0000003771.03771.000000
mean51.6289410.6573850.1230440.0132590.0114030.0389820.0140550.0140550.0156460.062053...0.7960752.0135030.938743108.3167780.8973750.9949640.897905110.4713640.00.922832
std18.9804720.4746470.3285320.1143970.1061870.1935770.1177320.1177320.1241170.241283...0.4029670.7383600.23983334.5007260.3035090.1851680.30281331.3590680.00.266893
min1.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0500000.0000002.0000000.0000000.2500000.0000002.0000000.00.000000
25%36.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.0000001.7000001.00000089.0000001.0000000.8900001.00000094.0000000.01.000000
50%54.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.0000002.0135001.000000106.0000001.0000000.9950001.000000110.0000000.01.000000
75%67.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...1.0000002.2000001.000000123.0000001.0000001.0700001.000000121.5000000.01.000000
max94.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000...1.00000010.6000001.000000430.0000001.0000002.3200001.000000395.0000000.01.000000
\n", "

8 rows × 28 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine query on thyroxine \\\n", "count 3771.000000 3771.000000 3771.000000 3771.000000 \n", "mean 51.628941 0.657385 0.123044 0.013259 \n", "std 18.980472 0.474647 0.328532 0.114397 \n", "min 1.000000 0.000000 0.000000 0.000000 \n", "25% 36.000000 0.000000 0.000000 0.000000 \n", "50% 54.000000 1.000000 0.000000 0.000000 \n", "75% 67.000000 1.000000 0.000000 0.000000 \n", "max 94.000000 1.000000 1.000000 1.000000 \n", "\n", " on antithyroid medication sick pregnant thyroid surgery \\\n", "count 3771.000000 3771.000000 3771.000000 3771.000000 \n", "mean 0.011403 0.038982 0.014055 0.014055 \n", "std 0.106187 0.193577 0.117732 0.117732 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " I131 treatment query hypothyroid ... T3 measured T3 \\\n", "count 3771.000000 3771.000000 ... 3771.000000 3771.000000 \n", "mean 0.015646 0.062053 ... 0.796075 2.013503 \n", "std 0.124117 0.241283 ... 0.402967 0.738360 \n", "min 0.000000 0.000000 ... 0.000000 0.050000 \n", "25% 0.000000 0.000000 ... 1.000000 1.700000 \n", "50% 0.000000 0.000000 ... 1.000000 2.013500 \n", "75% 0.000000 0.000000 ... 1.000000 2.200000 \n", "max 1.000000 1.000000 ... 1.000000 10.600000 \n", "\n", " TT4 measured TT4 T4U measured T4U FTI measured \\\n", "count 3771.000000 3771.000000 3771.000000 3771.000000 3771.000000 \n", "mean 0.938743 108.316778 0.897375 0.994964 0.897905 \n", "std 0.239833 34.500726 0.303509 0.185168 0.302813 \n", "min 0.000000 2.000000 0.000000 0.250000 0.000000 \n", "25% 1.000000 89.000000 1.000000 0.890000 1.000000 \n", "50% 1.000000 106.000000 1.000000 0.995000 1.000000 \n", "75% 1.000000 123.000000 1.000000 1.070000 1.000000 \n", "max 1.000000 430.000000 1.000000 2.320000 1.000000 \n", "\n", " FTI TBG measured binaryClass \n", "count 3771.000000 3771.0 3771.000000 \n", "mean 110.471364 0.0 0.922832 \n", "std 31.359068 0.0 0.266893 \n", "min 2.000000 0.0 0.000000 \n", "25% 94.000000 0.0 1.000000 \n", "50% 110.000000 0.0 1.000000 \n", "75% 121.500000 0.0 1.000000 \n", "max 395.000000 0.0 1.000000 \n", "\n", "[8 rows x 28 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe(include=\"all\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "f9ab413c-77d5-441f-bd57-283bcd3d569d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 93\n", "sex 2\n", "on thyroxine 2\n", "query on thyroxine 2\n", "on antithyroid medication 2\n", "sick 2\n", "pregnant 2\n", "thyroid surgery 2\n", "I131 treatment 2\n", "query hypothyroid 2\n", "query hyperthyroid 2\n", "lithium 2\n", "goitre 2\n", "tumor 2\n", "hypopituitary 2\n", "psych 2\n", "TSH measured 2\n", "TSH 288\n", "T3 measured 2\n", "T3 70\n", "TT4 measured 2\n", "TT4 242\n", "T4U measured 2\n", "T4U 147\n", "FTI measured 2\n", "FTI 235\n", "TBG measured 1\n", "binaryClass 2\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.nunique()" ] }, { "cell_type": "code", "execution_count": 14, "id": "3de756d8-f00a-494a-b412-405fdc1b8a6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['age', 'sex', 'on thyroxine', 'query on thyroxine',\n", " 'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n", " 'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n", " 'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured', 'TSH',\n", " 'T3 measured', 'T3', 'TT4 measured', 'TT4', 'T4U measured', 'T4U',\n", " 'FTI measured', 'FTI', 'TBG measured', 'binaryClass'],\n", " dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 15, "id": "c974c769-24ab-4039-9ba0-e1db78a61a5a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "binaryClass\n", "1 3480\n", "0 291\n", "Name: count, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.binaryClass.value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "id": "f6742b57-40fe-4f0e-b0e7-c5b00cb81cd4", "metadata": {}, "outputs": [ { "ename": "IntCastingNaNError", "evalue": "Cannot convert non-finite values (NA or inf) to integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIntCastingNaNError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data\u001b[38;5;241m.\u001b[39mbinaryClass\u001b[38;5;241m=\u001b[39m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbinaryClass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mP\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\generic.py:6640\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 6634\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 6635\u001b[0m ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 6636\u001b[0m ]\n\u001b[0;32m 6638\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6639\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6640\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6641\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 6642\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 428\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m 361\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 363\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 364\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m 366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m 755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 756\u001b[0m values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[1;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m 762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 234\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 237\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m 239\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m 240\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[0;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m 179\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 182\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[0;32m 97\u001b[0m arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 98\u001b[0m )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_astype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\dtypes\\astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m--> 145\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[0;32m 146\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 147\u001b[0m )\n\u001b[0;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 149\u001b[0m \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n", "\u001b[1;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer" ] } ], "source": [ "data.binaryClass=data.binaryClass.map({\"P\":1,\"N\":0}).astype(int)" ] }, { "cell_type": "code", "execution_count": 17, "id": "a5a74c7f-520b-4b8d-b7c4-aeac12193f80", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sex\n", "1.0 2479\n", "0.0 1292\n", "Name: count, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.sex.value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "id": "e8afb7d5-4d7d-4234-a0fd-4db01be8c098", "metadata": {}, "outputs": [], "source": [ "data=data.replace({\"?\":np.NAN})" ] }, { "cell_type": "code", "execution_count": 19, "id": "aa1de903-b5d3-445d-bf10-8d696561954e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sex\n", "1.0 2479\n", "0.0 1292\n", "Name: count, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.sex.value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "id": "5ab509d8-85c5-4a23-a9d7-0e5364cdefe9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pregnant\n", "0 3718\n", "1 53\n", "Name: count, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.pregnant.value_counts()" ] }, { "cell_type": "code", "execution_count": 21, "id": "a7ea6734-047b-44b1-9dc4-c0bc8d783fcb", "metadata": {}, "outputs": [], "source": [ "data.pregnant=data.pregnant.replace({\"f\":0,\"t\":1})" ] }, { "cell_type": "code", "execution_count": 22, "id": "f34e4353-cb97-4cfc-90ae-a682ea5e5d1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "on thyroxine 0\n", "query on thyroxine 0\n", "on antithyroid medication 0\n", "sick 0\n", "pregnant 0\n", "thyroid surgery 0\n", "I131 treatment 0\n", "query hypothyroid 0\n", "query hyperthyroid 0\n", "lithium 0\n", "goitre 0\n", "tumor 0\n", "hypopituitary 0\n", "psych 0\n", "TSH measured 0\n", "TSH 0\n", "T3 measured 0\n", "T3 0\n", "TT4 measured 0\n", "TT4 0\n", "T4U measured 0\n", "T4U 0\n", "FTI measured 0\n", "FTI 0\n", "TBG measured 0\n", "binaryClass 0\n", "dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 23, "id": "5ecc5dd8-2a6f-43aa-b7b1-47d87cbcb9e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age float64\n", "sex float64\n", "on thyroxine int64\n", "query on thyroxine int64\n", "on antithyroid medication int64\n", "sick int64\n", "pregnant int64\n", "thyroid surgery int64\n", "I131 treatment int64\n", "query hypothyroid int64\n", "query hyperthyroid int64\n", "lithium int64\n", "goitre int64\n", "tumor int64\n", "hypopituitary int64\n", "psych int64\n", "TSH measured int64\n", "TSH float64\n", "T3 measured int64\n", "T3 float64\n", "TT4 measured int64\n", "TT4 float64\n", "T4U measured int64\n", "T4U float64\n", "FTI measured int64\n", "FTI float64\n", "TBG measured int64\n", "binaryClass int64\n", "dtype: object" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 24, "id": "ab5d681a-40aa-49ce-b205-e6b836539662", "metadata": {}, "outputs": [], "source": [ "data=data.replace({\"t\":1,\"f\":0})" ] }, { "cell_type": "code", "execution_count": 25, "id": "b91cf96b-0d02-4dc4-b6ff-af8ea5fce91a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
041.01.000000000...12.50001125.011.1401109.00000001
123.01.000000000...12.00001102.000.9950110.46964901
246.00.000000000...02.01351109.010.9101120.00000001
370.01.010000000...11.90001175.000.9950110.46964901
470.01.000000000...11.2000161.010.870170.00000001
\n", "

5 rows × 28 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine query on thyroxine on antithyroid medication \\\n", "0 41.0 1.0 0 0 0 \n", "1 23.0 1.0 0 0 0 \n", "2 46.0 0.0 0 0 0 \n", "3 70.0 1.0 1 0 0 \n", "4 70.0 1.0 0 0 0 \n", "\n", " sick pregnant thyroid surgery I131 treatment query hypothyroid ... \\\n", "0 0 0 0 0 0 ... \n", "1 0 0 0 0 0 ... \n", "2 0 0 0 0 0 ... \n", "3 0 0 0 0 0 ... \n", "4 0 0 0 0 0 ... \n", "\n", " T3 measured T3 TT4 measured TT4 T4U measured T4U \\\n", "0 1 2.5000 1 125.0 1 1.140 \n", "1 1 2.0000 1 102.0 0 0.995 \n", "2 0 2.0135 1 109.0 1 0.910 \n", "3 1 1.9000 1 175.0 0 0.995 \n", "4 1 1.2000 1 61.0 1 0.870 \n", "\n", " FTI measured FTI TBG measured binaryClass \n", "0 1 109.000000 0 1 \n", "1 0 110.469649 0 1 \n", "2 1 120.000000 0 1 \n", "3 0 110.469649 0 1 \n", "4 1 70.000000 0 1 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "a33dae09-66fb-4b30-9da5-1941890aaea6", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'TBG'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'TBG'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTBG\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalue_counts()\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4088\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 4089\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4090\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4091\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 4092\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", "\u001b[1;31mKeyError\u001b[0m: 'TBG'" ] } ], "source": [ "data[\"TBG\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 27, "id": "b4410799-8dcf-4ee6-9129-9173901a0791", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'referral source'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'referral source'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[27], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mreferral source\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalue_counts()\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4088\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 4089\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4090\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4091\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 4092\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", "File \u001b[1;32m~\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", "\u001b[1;31mKeyError\u001b[0m: 'referral source'" ] } ], "source": [ "data[\"referral source\"].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "id": "bc735b38-6e63-40d9-b2c0-6f7b37d503d9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "id": "d288f411-830f-4601-83d9-dbc76c47f442", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3771 entries, 0 to 3770\n", "Data columns (total 28 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 3771 non-null float64\n", " 1 sex 3771 non-null float64\n", " 2 on thyroxine 3771 non-null int64 \n", " 3 query on thyroxine 3771 non-null int64 \n", " 4 on antithyroid medication 3771 non-null int64 \n", " 5 sick 3771 non-null int64 \n", " 6 pregnant 3771 non-null int64 \n", " 7 thyroid surgery 3771 non-null int64 \n", " 8 I131 treatment 3771 non-null int64 \n", " 9 query hypothyroid 3771 non-null int64 \n", " 10 query hyperthyroid 3771 non-null int64 \n", " 11 lithium 3771 non-null int64 \n", " 12 goitre 3771 non-null int64 \n", " 13 tumor 3771 non-null int64 \n", " 14 hypopituitary 3771 non-null int64 \n", " 15 psych 3771 non-null int64 \n", " 16 TSH measured 3771 non-null int64 \n", " 17 TSH 3771 non-null float64\n", " 18 T3 measured 3771 non-null int64 \n", " 19 T3 3771 non-null float64\n", " 20 TT4 measured 3771 non-null int64 \n", " 21 TT4 3771 non-null float64\n", " 22 T4U measured 3771 non-null int64 \n", " 23 T4U 3771 non-null float64\n", " 24 FTI measured 3771 non-null int64 \n", " 25 FTI 3771 non-null float64\n", " 26 TBG measured 3771 non-null int64 \n", " 27 binaryClass 3771 non-null int64 \n", "dtypes: float64(7), int64(21)\n", "memory usage: 825.0 KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 29, "id": "a4c2585b-69e0-406b-a888-a25841b01428", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "T3 measured\n", "1 3002\n", "0 769\n", "Name: count, dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"T3 measured\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 30, "id": "d0c85e5a-41ef-4ef5-ab0d-839253240cd8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TBG measured\n", "0 3771\n", "Name: count, dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"TBG measured\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 31, "id": "61160dd2-9f2b-45e2-8c23-38515101cda5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "on thyroxine\n", "0 3307\n", "1 464\n", "Name: count, dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"on thyroxine\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 32, "id": "e078ec2a-52f3-4bbd-8e9f-b57548351009", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age float64\n", "sex float64\n", "on thyroxine int64\n", "query on thyroxine int64\n", "on antithyroid medication int64\n", "sick int64\n", "pregnant int64\n", "thyroid surgery int64\n", "I131 treatment int64\n", "query hypothyroid int64\n", "query hyperthyroid int64\n", "lithium int64\n", "goitre int64\n", "tumor int64\n", "hypopituitary int64\n", "psych int64\n", "TSH measured int64\n", "TSH float64\n", "T3 measured int64\n", "T3 float64\n", "TT4 measured int64\n", "TT4 float64\n", "T4U measured int64\n", "T4U float64\n", "FTI measured int64\n", "FTI float64\n", "TBG measured int64\n", "binaryClass int64\n", "dtype: object" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 33, "id": "6b5d65ac-f844-43a7-8a11-f9be130fd4ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "on thyroxine 0\n", "query on thyroxine 0\n", "on antithyroid medication 0\n", "sick 0\n", "pregnant 0\n", "thyroid surgery 0\n", "I131 treatment 0\n", "query hypothyroid 0\n", "query hyperthyroid 0\n", "lithium 0\n", "goitre 0\n", "tumor 0\n", "hypopituitary 0\n", "psych 0\n", "TSH measured 0\n", "TSH 0\n", "T3 measured 0\n", "T3 0\n", "TT4 measured 0\n", "TT4 0\n", "T4U measured 0\n", "T4U 0\n", "FTI measured 0\n", "FTI 0\n", "TBG measured 0\n", "binaryClass 0\n", "dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 36, "id": "dfb2ba5d-eb6c-4a91-b998-4f58b74e8a7f", "metadata": {}, "outputs": [], "source": [ "columns=data.columns[data.dtypes.eq(\"object\")]\n", "data[columns]=data[columns].apply(pd.to_numeric,errors=\"coerce\")" ] }, { "cell_type": "code", "execution_count": 37, "id": "35e0f773-394d-469b-90ec-ee030a34ee3d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age float64\n", "sex float64\n", "on thyroxine int64\n", "query on thyroxine int64\n", "on antithyroid medication int64\n", "sick int64\n", "pregnant int64\n", "thyroid surgery int64\n", "I131 treatment int64\n", "query hypothyroid int64\n", "query hyperthyroid int64\n", "lithium int64\n", "goitre int64\n", "tumor int64\n", "hypopituitary int64\n", "psych int64\n", "TSH measured int64\n", "TSH float64\n", "T3 measured int64\n", "T3 float64\n", "TT4 measured int64\n", "TT4 float64\n", "T4U measured int64\n", "T4U float64\n", "FTI measured int64\n", "FTI float64\n", "TBG measured int64\n", "binaryClass int64\n", "dtype: object" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 38, "id": "bbc8361e-eb05-4840-ac60-c9d47c59676a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "on thyroxine 0\n", "query on thyroxine 0\n", "on antithyroid medication 0\n", "sick 0\n", "pregnant 0\n", "thyroid surgery 0\n", "I131 treatment 0\n", "query hypothyroid 0\n", "query hyperthyroid 0\n", "lithium 0\n", "goitre 0\n", "tumor 0\n", "hypopituitary 0\n", "psych 0\n", "TSH measured 0\n", "TSH 0\n", "T3 measured 0\n", "T3 0\n", "TT4 measured 0\n", "TT4 0\n", "T4U measured 0\n", "T4U 0\n", "FTI measured 0\n", "FTI 0\n", "TBG measured 0\n", "binaryClass 0\n", "dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 39, "id": "12288a90-ad59-415a-93cf-ca8a22dfcb30", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
041.01.000000000...12.50001125.00000011.1401109.00000001
123.01.000000000...12.00001102.00000000.9950110.46964901
246.00.000000000...02.01351109.00000010.9101120.00000001
370.01.010000000...11.90001175.00000000.9950110.46964901
470.01.000000000...11.2000161.00000010.870170.00000001
..................................................................
376630.01.000000000...02.01350108.31934500.9950110.46964901
376768.01.000000000...12.10001124.00000011.0801114.00000001
376874.01.000000000...11.80001112.00000011.0701105.00000001
376972.00.000000000...12.0000182.00000010.940187.00000001
377064.01.000000000...12.2000199.00000011.070192.00000001
\n", "

3771 rows × 28 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine query on thyroxine on antithyroid medication \\\n", "0 41.0 1.0 0 0 0 \n", "1 23.0 1.0 0 0 0 \n", "2 46.0 0.0 0 0 0 \n", "3 70.0 1.0 1 0 0 \n", "4 70.0 1.0 0 0 0 \n", "... ... ... ... ... ... \n", "3766 30.0 1.0 0 0 0 \n", "3767 68.0 1.0 0 0 0 \n", "3768 74.0 1.0 0 0 0 \n", "3769 72.0 0.0 0 0 0 \n", "3770 64.0 1.0 0 0 0 \n", "\n", " sick pregnant thyroid surgery I131 treatment query hypothyroid ... \\\n", "0 0 0 0 0 0 ... \n", "1 0 0 0 0 0 ... \n", "2 0 0 0 0 0 ... \n", "3 0 0 0 0 0 ... \n", "4 0 0 0 0 0 ... \n", "... ... ... ... ... ... ... \n", "3766 0 0 0 0 0 ... \n", "3767 0 0 0 0 0 ... \n", "3768 0 0 0 0 0 ... \n", "3769 0 0 0 0 0 ... \n", "3770 0 0 0 0 0 ... \n", "\n", " T3 measured T3 TT4 measured TT4 T4U measured T4U \\\n", "0 1 2.5000 1 125.000000 1 1.140 \n", "1 1 2.0000 1 102.000000 0 0.995 \n", "2 0 2.0135 1 109.000000 1 0.910 \n", "3 1 1.9000 1 175.000000 0 0.995 \n", "4 1 1.2000 1 61.000000 1 0.870 \n", "... ... ... ... ... ... ... \n", "3766 0 2.0135 0 108.319345 0 0.995 \n", "3767 1 2.1000 1 124.000000 1 1.080 \n", "3768 1 1.8000 1 112.000000 1 1.070 \n", "3769 1 2.0000 1 82.000000 1 0.940 \n", "3770 1 2.2000 1 99.000000 1 1.070 \n", "\n", " FTI measured FTI TBG measured binaryClass \n", "0 1 109.000000 0 1 \n", "1 0 110.469649 0 1 \n", "2 1 120.000000 0 1 \n", "3 0 110.469649 0 1 \n", "4 1 70.000000 0 1 \n", "... ... ... ... ... \n", "3766 0 110.469649 0 1 \n", "3767 1 114.000000 0 1 \n", "3768 1 105.000000 0 1 \n", "3769 1 87.000000 0 1 \n", "3770 1 92.000000 0 1 \n", "\n", "[3771 rows x 28 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.fillna(data.mean())" ] }, { "cell_type": "code", "execution_count": 40, "id": "fcbffd18-464a-4def-83ca-da3605ab3f06", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age float64\n", "sex float64\n", "on thyroxine int64\n", "query on thyroxine int64\n", "on antithyroid medication int64\n", "sick int64\n", "pregnant int64\n", "thyroid surgery int64\n", "I131 treatment int64\n", "query hypothyroid int64\n", "query hyperthyroid int64\n", "lithium int64\n", "goitre int64\n", "tumor int64\n", "hypopituitary int64\n", "psych int64\n", "TSH measured int64\n", "TSH float64\n", "T3 measured int64\n", "T3 float64\n", "TT4 measured int64\n", "TT4 float64\n", "T4U measured int64\n", "T4U float64\n", "FTI measured int64\n", "FTI float64\n", "TBG measured int64\n", "binaryClass int64\n", "dtype: object" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 41, "id": "934f8d30-c896-4e6b-bcbd-5166f8b08a7c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\3250616897.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " data[\"age\"].fillna(data[\"age\"].mean(),inplace=True)\n" ] } ], "source": [ "data[\"age\"].fillna(data[\"age\"].mean(),inplace=True)" ] }, { "cell_type": "code", "execution_count": 42, "id": "bb518e23-09bf-4109-9550-0a108867f145", "metadata": {}, "outputs": [], "source": [ "data.sex=data.sex.replace({\"F\":1,\"M\":0})" ] }, { "cell_type": "code", "execution_count": 43, "id": "114da8f5-0510-4081-b8bc-3a325bf8e0d7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\1558594319.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " data[\"sex\"].fillna(data[\"sex\"].mode(),inplace=True)\n" ] } ], "source": [ "data[\"sex\"].fillna(data[\"sex\"].mode(),inplace=True)" ] }, { "cell_type": "code", "execution_count": 44, "id": "be3c05bc-f41e-481c-9224-f8bb4f9cacca", "metadata": {}, "outputs": [], "source": [ "from sklearn.impute import SimpleImputer\n", "imputer=SimpleImputer(strategy=\"mean\")" ] }, { "cell_type": "code", "execution_count": 45, "id": "1bb2d70f-8358-4d76-a313-ee7b68dd45aa", "metadata": {}, "outputs": [], "source": [ "data[\"TSH\"]=imputer.fit_transform(data[[\"TSH\"]])\n", "data[\"T3\"]=imputer.fit_transform(data[[\"T3\"]])\n", "data[\"TT4\"]=imputer.fit_transform(data[[\"TT4\"]])\n", "data[\"T4U\"]=imputer.fit_transform(data[[\"T4U\"]])\n", "data[\"FTI\"]=imputer.fit_transform(data[[\"FTI\"]])" ] }, { "cell_type": "code", "execution_count": 46, "id": "2ef9bcfb-94ed-47ef-b2a0-9d9b8f1f0b9f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "on thyroxine 0\n", "query on thyroxine 0\n", "on antithyroid medication 0\n", "sick 0\n", "pregnant 0\n", "thyroid surgery 0\n", "I131 treatment 0\n", "query hypothyroid 0\n", "query hyperthyroid 0\n", "lithium 0\n", "goitre 0\n", "tumor 0\n", "hypopituitary 0\n", "psych 0\n", "TSH measured 0\n", "TSH 0\n", "T3 measured 0\n", "T3 0\n", "TT4 measured 0\n", "TT4 0\n", "T4U measured 0\n", "T4U 0\n", "FTI measured 0\n", "FTI 0\n", "TBG measured 0\n", "binaryClass 0\n", "dtype: int64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "fb34d49c-7800-4d9e-ae8f-2ddf1f0dc6d0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 47, "id": "54e3db81-e603-4460-b79f-0862348957b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(data.isnull())" ] }, { "cell_type": "code", "execution_count": 48, "id": "1cc718a3-8c88-4dea-86e9-f21bb1b33bda", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1292., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 2479.]),\n", " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n", " )" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(data.sex)" ] }, { "cell_type": "code", "execution_count": 49, "id": "231bfaa7-812a-49a2-8345-3a52fc8e777f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(data[\"on thyroxine\"],data[\"pregnant\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "0f4482a1-1b59-4e23-8be6-2fa3b751fb1a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 50, "id": "5c5c3c12-7ce9-4851-b4f2-dd334cff4671", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\2784439929.py:1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(data[\"age\"])\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(data[\"age\"])" ] }, { "cell_type": "code", "execution_count": 51, "id": "b60ec718-8605-4d3c-a292-d28ed3b91467", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\213618458.py:1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(data.sex)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(data.sex)" ] }, { "cell_type": "code", "execution_count": 52, "id": "b7eb4a91-e080-4d88-88fb-d33f444fcabf", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\302326388.py:1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(data.T3)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(data.T3)\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "f9bb46dd-03ed-4bb2-91d1-82a923ba8b9f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_9036\\147263787.py:1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(data['FTI'])\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(data['FTI'])\n" ] }, { "cell_type": "code", "execution_count": 54, "id": "18c4d4b5-177c-4ce9-a101-a4c876559631", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x=\"pregnant\",y=\"age\",data=data,kind=\"scatter\")\n" ] }, { "cell_type": "code", "execution_count": 55, "id": "9dc5fd0a-712e-4f8a-a0fe-ef9411a513e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x=\"age\",y=\"pregnant\",data=data,kind=\"reg\")\n" ] }, { "cell_type": "code", "execution_count": 56, "id": "5514f599-7f8e-495d-9daf-e1e735c37f93", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x=\"age\",y=\"TT4\",data=data,kind=\"scatter\")\n" ] }, { "cell_type": "code", "execution_count": 57, "id": "d502f4fc-b1f3-448e-adec-c30290df8280", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"binaryClass\",data=data)\n" ] }, { "cell_type": "code", "execution_count": 58, "id": "6c42db19-cc35-44e8-9cdf-97520c4f8e8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"binaryClass\",hue=\"sex\",data=data)\n" ] }, { "cell_type": "code", "execution_count": 59, "id": "c2ee0c1a-c87e-42a2-8e8c-5c928e9b210d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"binaryClass\",y=\"age\",data=data)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "936aefcf-7b1a-44c9-b490-783d51b79e13", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 60, "id": "bc16db9f-8ea8-44ce-816b-a70a59a4cff9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3771, 28)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data1=data[data.age<=(data.age.mean()+3*data.age.std())]\n", "data1.shape" ] }, { "cell_type": "code", "execution_count": 61, "id": "65d51093-9812-4166-bd58-52a947a1bceb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"binaryClass\",y=\"age\",data=data1)" ] }, { "cell_type": "code", "execution_count": 62, "id": "4b7cdd68-8ef1-47c1-8553-71d5f6be9f79", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...T3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measuredbinaryClass
age1.0000000.0072010.017522-0.018257-0.0669940.085228-0.119454-0.0305460.0564810.043314...0.074516-0.2273280.069999-0.0413800.088424-0.1708110.0875540.054091NaN0.001729
sex0.0072011.0000000.098614-0.0384420.035431-0.0018350.0767000.0387270.0144720.051353...-0.0783140.055972-0.0632490.155830-0.0415980.219783-0.0422740.029194NaN-0.049627
on thyroxine0.0175220.0986141.0000000.005983-0.002212-0.0420740.0101400.0375720.0633620.094389...-0.1450190.0064830.0249900.2128350.0388870.0464450.0383200.185732NaN0.081091
query on thyroxine-0.018257-0.0384420.0059831.000000-0.0124490.0125880.0452440.005855-0.014614-0.029816...-0.033395-0.0064660.029611-0.0046940.0315610.0004610.031431-0.003556NaN0.007457
on antithyroid medication-0.0669940.035431-0.002212-0.0124491.000000-0.0216300.072047-0.0128230.006586-0.017271...0.0109640.079212-0.0246420.023819-0.0295230.060390-0.029779-0.016609NaN0.021697
sick0.085228-0.001835-0.0420740.012588-0.0216301.000000-0.024046-0.000769-0.0253910.027704...0.006722-0.076473-0.005686-0.0369910.009417-0.0390330.009086-0.021201NaN0.001764
pregnant-0.1194540.0767000.0101400.0452440.072047-0.0240461.000000-0.014255-0.015052-0.021372...0.0268820.1811460.0211050.1725010.0329530.3347500.032819-0.016705NaN0.034526
thyroid surgery-0.0305460.0387270.0375720.005855-0.012823-0.000769-0.0142551.0000000.003100-0.012034...-0.023438-0.0241460.030499-0.0217980.0106830.0279480.010498-0.031835NaN0.017642
I131 treatment0.0564810.0144720.063362-0.0146140.006586-0.025391-0.0150520.0031001.0000000.047288...0.0001670.0120560.032205-0.0088390.0285520.0094310.028397-0.016824NaN-0.003580
query hypothyroid0.0433140.0513530.094389-0.029816-0.0172710.027704-0.021372-0.0120340.0472881.000000...-0.063515-0.0474980.024450-0.0089940.0145400.0138830.014123-0.019734NaN-0.086264
query hyperthyroid-0.0388100.064927-0.023822-0.0109130.126560-0.0352200.1175980.0154930.0642040.019446...0.0280150.166231-0.0477630.127871-0.0204440.074325-0.0209440.102190NaN0.013466
lithium-0.0314890.017568-0.002516-0.008028-0.007438-0.013948-0.008269-0.008269-0.008731-0.001865...-0.0031450.0078740.017691-0.0139080.0234200.0151840.023353-0.025951NaN0.005609
goitre-0.054309-0.002075-0.0101080.037997-0.010244-0.0192110.012444-0.011388-0.012025-0.024534...-0.0352780.011791-0.010731-0.0189340.0045230.0350970.004366-0.039871NaN0.027583
tumor-0.0255830.077658-0.029789-0.004016-0.0173580.0109400.123724-0.004995-0.020376-0.034593...-0.0184830.097439-0.0499830.058651-0.0230120.090238-0.0233480.012099NaN-0.003734
hypopituitary-0.026285-0.022560-0.0061010.140499-0.001749-0.003280-0.001945-0.001945-0.002053-0.004189...0.008243-0.0157400.004160-0.0256440.0055080.0066010.005492-0.030891NaN0.004710
psych-0.104666-0.090671-0.073595-0.026254-0.024324-0.032896-0.016585-0.027041-0.028554-0.012335...0.0993560.0273150.057856-0.0003670.052255-0.0146490.0519790.010093NaN0.028593
TSH measured0.109394-0.0308890.041852-0.1178830.0017450.0156060.0014110.0393210.0415210.055114...0.444700-0.0387390.6714230.0037020.5062500.0119640.507991-0.000134NaN-0.095236
TSH-0.0584700.0261490.017122-0.009458-0.010673-0.022108-0.0196990.026225-0.0041310.025967...0.007473-0.147333-0.000930-0.261291-0.0059640.071067-0.006272-0.293035NaN-0.423951
T3 measured0.074516-0.078314-0.145019-0.0333950.0109640.0067220.026882-0.0234380.000167-0.063515...1.0000000.0000020.416888-0.0391690.2625990.0068160.264089-0.041781NaN-0.032906
T3-0.2273280.0559720.006483-0.0064660.079212-0.0764730.181146-0.0241460.012056-0.047498...0.0000021.000000-0.0078900.5090720.0016880.4072740.0008290.308838NaN0.177687
TT4 measured0.069999-0.0632490.0249900.029611-0.024642-0.0056860.0211050.0304990.0322050.024450...0.416888-0.0078901.000000-0.0000190.7480900.0019810.7466040.000240NaN-0.053149
TT4-0.0413800.1558300.212835-0.0046940.023819-0.0369910.172501-0.021798-0.008839-0.008994...-0.0391690.509072-0.0000191.0000000.0412180.4264640.0397310.779128NaN0.291661
T4U measured0.088424-0.0415980.0388870.031561-0.0295230.0094170.0329530.0106830.0285520.014540...0.2625990.0016880.7480900.0412181.000000-0.0000650.9971180.000630NaN-0.015927
T4U-0.1708110.2197830.0464450.0004610.060390-0.0390330.3347500.0279480.0094310.013883...0.0068160.4072740.0019810.426464-0.0000651.000000-0.000065-0.173978NaN-0.028396
FTI measured0.087554-0.0422740.0383200.031431-0.0297790.0090860.0328190.0104980.0283970.014123...0.2640890.0008290.7466040.0397310.997118-0.0000651.0000000.000018NaN-0.015457
FTI0.0540910.0291940.185732-0.003556-0.016609-0.021201-0.016705-0.031835-0.016824-0.019734...-0.0417810.3088380.0002400.7791280.000630-0.1739780.0000181.000000NaN0.313812
TBG measuredNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
binaryClass0.001729-0.0496270.0810910.0074570.0216970.0017640.0345260.017642-0.003580-0.086264...-0.0329060.177687-0.0531490.291661-0.015927-0.028396-0.0154570.313812NaN1.000000
\n", "

28 rows × 28 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine \\\n", "age 1.000000 0.007201 0.017522 \n", "sex 0.007201 1.000000 0.098614 \n", "on thyroxine 0.017522 0.098614 1.000000 \n", "query on thyroxine -0.018257 -0.038442 0.005983 \n", "on antithyroid medication -0.066994 0.035431 -0.002212 \n", "sick 0.085228 -0.001835 -0.042074 \n", "pregnant -0.119454 0.076700 0.010140 \n", "thyroid surgery -0.030546 0.038727 0.037572 \n", "I131 treatment 0.056481 0.014472 0.063362 \n", "query hypothyroid 0.043314 0.051353 0.094389 \n", "query hyperthyroid -0.038810 0.064927 -0.023822 \n", "lithium -0.031489 0.017568 -0.002516 \n", "goitre -0.054309 -0.002075 -0.010108 \n", "tumor -0.025583 0.077658 -0.029789 \n", "hypopituitary -0.026285 -0.022560 -0.006101 \n", "psych -0.104666 -0.090671 -0.073595 \n", "TSH measured 0.109394 -0.030889 0.041852 \n", "TSH -0.058470 0.026149 0.017122 \n", "T3 measured 0.074516 -0.078314 -0.145019 \n", "T3 -0.227328 0.055972 0.006483 \n", "TT4 measured 0.069999 -0.063249 0.024990 \n", "TT4 -0.041380 0.155830 0.212835 \n", "T4U measured 0.088424 -0.041598 0.038887 \n", "T4U -0.170811 0.219783 0.046445 \n", "FTI measured 0.087554 -0.042274 0.038320 \n", "FTI 0.054091 0.029194 0.185732 \n", "TBG measured NaN NaN NaN \n", "binaryClass 0.001729 -0.049627 0.081091 \n", "\n", " query on thyroxine on antithyroid medication \\\n", "age -0.018257 -0.066994 \n", "sex -0.038442 0.035431 \n", "on thyroxine 0.005983 -0.002212 \n", "query on thyroxine 1.000000 -0.012449 \n", "on antithyroid medication -0.012449 1.000000 \n", "sick 0.012588 -0.021630 \n", "pregnant 0.045244 0.072047 \n", "thyroid surgery 0.005855 -0.012823 \n", "I131 treatment -0.014614 0.006586 \n", "query hypothyroid -0.029816 -0.017271 \n", "query hyperthyroid -0.010913 0.126560 \n", "lithium -0.008028 -0.007438 \n", "goitre 0.037997 -0.010244 \n", "tumor -0.004016 -0.017358 \n", "hypopituitary 0.140499 -0.001749 \n", "psych -0.026254 -0.024324 \n", "TSH measured -0.117883 0.001745 \n", "TSH -0.009458 -0.010673 \n", "T3 measured -0.033395 0.010964 \n", "T3 -0.006466 0.079212 \n", "TT4 measured 0.029611 -0.024642 \n", "TT4 -0.004694 0.023819 \n", "T4U measured 0.031561 -0.029523 \n", "T4U 0.000461 0.060390 \n", "FTI measured 0.031431 -0.029779 \n", "FTI -0.003556 -0.016609 \n", "TBG measured NaN NaN \n", "binaryClass 0.007457 0.021697 \n", "\n", " sick pregnant thyroid surgery \\\n", "age 0.085228 -0.119454 -0.030546 \n", "sex -0.001835 0.076700 0.038727 \n", "on thyroxine -0.042074 0.010140 0.037572 \n", "query on thyroxine 0.012588 0.045244 0.005855 \n", "on antithyroid medication -0.021630 0.072047 -0.012823 \n", "sick 1.000000 -0.024046 -0.000769 \n", "pregnant -0.024046 1.000000 -0.014255 \n", "thyroid surgery -0.000769 -0.014255 1.000000 \n", "I131 treatment -0.025391 -0.015052 0.003100 \n", "query hypothyroid 0.027704 -0.021372 -0.012034 \n", "query hyperthyroid -0.035220 0.117598 0.015493 \n", "lithium -0.013948 -0.008269 -0.008269 \n", "goitre -0.019211 0.012444 -0.011388 \n", "tumor 0.010940 0.123724 -0.004995 \n", "hypopituitary -0.003280 -0.001945 -0.001945 \n", "psych -0.032896 -0.016585 -0.027041 \n", "TSH measured 0.015606 0.001411 0.039321 \n", "TSH -0.022108 -0.019699 0.026225 \n", "T3 measured 0.006722 0.026882 -0.023438 \n", "T3 -0.076473 0.181146 -0.024146 \n", "TT4 measured -0.005686 0.021105 0.030499 \n", "TT4 -0.036991 0.172501 -0.021798 \n", "T4U measured 0.009417 0.032953 0.010683 \n", "T4U -0.039033 0.334750 0.027948 \n", "FTI measured 0.009086 0.032819 0.010498 \n", "FTI -0.021201 -0.016705 -0.031835 \n", "TBG measured NaN NaN NaN \n", "binaryClass 0.001764 0.034526 0.017642 \n", "\n", " I131 treatment query hypothyroid ... \\\n", "age 0.056481 0.043314 ... \n", "sex 0.014472 0.051353 ... \n", "on thyroxine 0.063362 0.094389 ... \n", "query on thyroxine -0.014614 -0.029816 ... \n", "on antithyroid medication 0.006586 -0.017271 ... \n", "sick -0.025391 0.027704 ... \n", "pregnant -0.015052 -0.021372 ... \n", "thyroid surgery 0.003100 -0.012034 ... \n", "I131 treatment 1.000000 0.047288 ... \n", "query hypothyroid 0.047288 1.000000 ... \n", "query hyperthyroid 0.064204 0.019446 ... \n", "lithium -0.008731 -0.001865 ... \n", "goitre -0.012025 -0.024534 ... \n", "tumor -0.020376 -0.034593 ... \n", "hypopituitary -0.002053 -0.004189 ... \n", "psych -0.028554 -0.012335 ... \n", "TSH measured 0.041521 0.055114 ... \n", "TSH -0.004131 0.025967 ... \n", "T3 measured 0.000167 -0.063515 ... \n", "T3 0.012056 -0.047498 ... \n", "TT4 measured 0.032205 0.024450 ... \n", "TT4 -0.008839 -0.008994 ... \n", "T4U measured 0.028552 0.014540 ... \n", "T4U 0.009431 0.013883 ... \n", "FTI measured 0.028397 0.014123 ... \n", "FTI -0.016824 -0.019734 ... \n", "TBG measured NaN NaN ... \n", "binaryClass -0.003580 -0.086264 ... \n", "\n", " T3 measured T3 TT4 measured TT4 \\\n", "age 0.074516 -0.227328 0.069999 -0.041380 \n", "sex -0.078314 0.055972 -0.063249 0.155830 \n", "on thyroxine -0.145019 0.006483 0.024990 0.212835 \n", "query on thyroxine -0.033395 -0.006466 0.029611 -0.004694 \n", "on antithyroid medication 0.010964 0.079212 -0.024642 0.023819 \n", "sick 0.006722 -0.076473 -0.005686 -0.036991 \n", "pregnant 0.026882 0.181146 0.021105 0.172501 \n", "thyroid surgery -0.023438 -0.024146 0.030499 -0.021798 \n", "I131 treatment 0.000167 0.012056 0.032205 -0.008839 \n", "query hypothyroid -0.063515 -0.047498 0.024450 -0.008994 \n", "query hyperthyroid 0.028015 0.166231 -0.047763 0.127871 \n", "lithium -0.003145 0.007874 0.017691 -0.013908 \n", "goitre -0.035278 0.011791 -0.010731 -0.018934 \n", "tumor -0.018483 0.097439 -0.049983 0.058651 \n", "hypopituitary 0.008243 -0.015740 0.004160 -0.025644 \n", "psych 0.099356 0.027315 0.057856 -0.000367 \n", "TSH measured 0.444700 -0.038739 0.671423 0.003702 \n", "TSH 0.007473 -0.147333 -0.000930 -0.261291 \n", "T3 measured 1.000000 0.000002 0.416888 -0.039169 \n", "T3 0.000002 1.000000 -0.007890 0.509072 \n", "TT4 measured 0.416888 -0.007890 1.000000 -0.000019 \n", "TT4 -0.039169 0.509072 -0.000019 1.000000 \n", "T4U measured 0.262599 0.001688 0.748090 0.041218 \n", "T4U 0.006816 0.407274 0.001981 0.426464 \n", "FTI measured 0.264089 0.000829 0.746604 0.039731 \n", "FTI -0.041781 0.308838 0.000240 0.779128 \n", "TBG measured NaN NaN NaN NaN \n", "binaryClass -0.032906 0.177687 -0.053149 0.291661 \n", "\n", " T4U measured T4U FTI measured FTI \\\n", "age 0.088424 -0.170811 0.087554 0.054091 \n", "sex -0.041598 0.219783 -0.042274 0.029194 \n", "on thyroxine 0.038887 0.046445 0.038320 0.185732 \n", "query on thyroxine 0.031561 0.000461 0.031431 -0.003556 \n", "on antithyroid medication -0.029523 0.060390 -0.029779 -0.016609 \n", "sick 0.009417 -0.039033 0.009086 -0.021201 \n", "pregnant 0.032953 0.334750 0.032819 -0.016705 \n", "thyroid surgery 0.010683 0.027948 0.010498 -0.031835 \n", "I131 treatment 0.028552 0.009431 0.028397 -0.016824 \n", "query hypothyroid 0.014540 0.013883 0.014123 -0.019734 \n", "query hyperthyroid -0.020444 0.074325 -0.020944 0.102190 \n", "lithium 0.023420 0.015184 0.023353 -0.025951 \n", "goitre 0.004523 0.035097 0.004366 -0.039871 \n", "tumor -0.023012 0.090238 -0.023348 0.012099 \n", "hypopituitary 0.005508 0.006601 0.005492 -0.030891 \n", "psych 0.052255 -0.014649 0.051979 0.010093 \n", "TSH measured 0.506250 0.011964 0.507991 -0.000134 \n", "TSH -0.005964 0.071067 -0.006272 -0.293035 \n", "T3 measured 0.262599 0.006816 0.264089 -0.041781 \n", "T3 0.001688 0.407274 0.000829 0.308838 \n", "TT4 measured 0.748090 0.001981 0.746604 0.000240 \n", "TT4 0.041218 0.426464 0.039731 0.779128 \n", "T4U measured 1.000000 -0.000065 0.997118 0.000630 \n", "T4U -0.000065 1.000000 -0.000065 -0.173978 \n", "FTI measured 0.997118 -0.000065 1.000000 0.000018 \n", "FTI 0.000630 -0.173978 0.000018 1.000000 \n", "TBG measured NaN NaN NaN NaN \n", "binaryClass -0.015927 -0.028396 -0.015457 0.313812 \n", "\n", " TBG measured binaryClass \n", "age NaN 0.001729 \n", "sex NaN -0.049627 \n", "on thyroxine NaN 0.081091 \n", "query on thyroxine NaN 0.007457 \n", "on antithyroid medication NaN 0.021697 \n", "sick NaN 0.001764 \n", "pregnant NaN 0.034526 \n", "thyroid surgery NaN 0.017642 \n", "I131 treatment NaN -0.003580 \n", "query hypothyroid NaN -0.086264 \n", "query hyperthyroid NaN 0.013466 \n", "lithium NaN 0.005609 \n", "goitre NaN 0.027583 \n", "tumor NaN -0.003734 \n", "hypopituitary NaN 0.004710 \n", "psych NaN 0.028593 \n", "TSH measured NaN -0.095236 \n", "TSH NaN -0.423951 \n", "T3 measured NaN -0.032906 \n", "T3 NaN 0.177687 \n", "TT4 measured NaN -0.053149 \n", "TT4 NaN 0.291661 \n", "T4U measured NaN -0.015927 \n", "T4U NaN -0.028396 \n", "FTI measured NaN -0.015457 \n", "FTI NaN 0.313812 \n", "TBG measured NaN NaN \n", "binaryClass NaN 1.000000 \n", "\n", "[28 rows x 28 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data1.corr()" ] }, { "cell_type": "code", "execution_count": 63, "id": "cdded3cc-225a-4cec-9276-5e8058306035", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(data1.corr())" ] }, { "cell_type": "code", "execution_count": 64, "id": "9421d55c-b2d5-4917-b49e-02371a5e3ff2", "metadata": {}, "outputs": [], "source": [ "x=data1.drop(\"binaryClass\",axis=1)\n", "y=data1.binaryClass" ] }, { "cell_type": "code", "execution_count": 65, "id": "3752af47-0ced-4a0e-ae12-7673ae2c2565", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agesexon thyroxinequery on thyroxineon antithyroid medicationsickpregnantthyroid surgeryI131 treatmentquery hypothyroid...TSHT3 measuredT3TT4 measuredTT4T4U measuredT4UFTI measuredFTITBG measured
041.01.000000000...1.30000012.50001125.00000011.1401109.0000000
123.01.000000000...4.10000012.00001102.00000000.9950110.4696490
246.00.000000000...0.98000002.01351109.00000010.9101120.0000000
370.01.010000000...0.16000011.90001175.00000000.9950110.4696490
470.01.000000000...0.72000011.2000161.00000010.870170.0000000
..................................................................
376630.01.000000000...5.08676602.01350108.31934500.9950110.4696490
376768.01.000000000...1.00000012.10001124.00000011.0801114.0000000
376874.01.000000000...5.10000011.80001112.00000011.0701105.0000000
376972.00.000000000...0.70000012.0000182.00000010.940187.0000000
377064.01.000000000...1.00000012.2000199.00000011.070192.0000000
\n", "

3771 rows × 27 columns

\n", "
" ], "text/plain": [ " age sex on thyroxine query on thyroxine on antithyroid medication \\\n", "0 41.0 1.0 0 0 0 \n", "1 23.0 1.0 0 0 0 \n", "2 46.0 0.0 0 0 0 \n", "3 70.0 1.0 1 0 0 \n", "4 70.0 1.0 0 0 0 \n", "... ... ... ... ... ... \n", "3766 30.0 1.0 0 0 0 \n", "3767 68.0 1.0 0 0 0 \n", "3768 74.0 1.0 0 0 0 \n", "3769 72.0 0.0 0 0 0 \n", "3770 64.0 1.0 0 0 0 \n", "\n", " sick pregnant thyroid surgery I131 treatment query hypothyroid ... \\\n", "0 0 0 0 0 0 ... \n", "1 0 0 0 0 0 ... \n", "2 0 0 0 0 0 ... \n", "3 0 0 0 0 0 ... \n", "4 0 0 0 0 0 ... \n", "... ... ... ... ... ... ... \n", "3766 0 0 0 0 0 ... \n", "3767 0 0 0 0 0 ... \n", "3768 0 0 0 0 0 ... \n", "3769 0 0 0 0 0 ... \n", "3770 0 0 0 0 0 ... \n", "\n", " TSH T3 measured T3 TT4 measured TT4 T4U measured \\\n", "0 1.300000 1 2.5000 1 125.000000 1 \n", "1 4.100000 1 2.0000 1 102.000000 0 \n", "2 0.980000 0 2.0135 1 109.000000 1 \n", "3 0.160000 1 1.9000 1 175.000000 0 \n", "4 0.720000 1 1.2000 1 61.000000 1 \n", "... ... ... ... ... ... ... \n", "3766 5.086766 0 2.0135 0 108.319345 0 \n", "3767 1.000000 1 2.1000 1 124.000000 1 \n", "3768 5.100000 1 1.8000 1 112.000000 1 \n", "3769 0.700000 1 2.0000 1 82.000000 1 \n", "3770 1.000000 1 2.2000 1 99.000000 1 \n", "\n", " T4U FTI measured FTI TBG measured \n", "0 1.140 1 109.000000 0 \n", "1 0.995 0 110.469649 0 \n", "2 0.910 1 120.000000 0 \n", "3 0.995 0 110.469649 0 \n", "4 0.870 1 70.000000 0 \n", "... ... ... ... ... \n", "3766 0.995 0 110.469649 0 \n", "3767 1.080 1 114.000000 0 \n", "3768 1.070 1 105.000000 0 \n", "3769 0.940 1 87.000000 0 \n", "3770 1.070 1 92.000000 0 \n", "\n", "[3771 rows x 27 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 66, "id": "d234838f-a9c2-4bad-b971-21606d63355e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3771 entries, 0 to 3770\n", "Data columns (total 27 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 3771 non-null float64\n", " 1 sex 3771 non-null float64\n", " 2 on thyroxine 3771 non-null int64 \n", " 3 query on thyroxine 3771 non-null int64 \n", " 4 on antithyroid medication 3771 non-null int64 \n", " 5 sick 3771 non-null int64 \n", " 6 pregnant 3771 non-null int64 \n", " 7 thyroid surgery 3771 non-null int64 \n", " 8 I131 treatment 3771 non-null int64 \n", " 9 query hypothyroid 3771 non-null int64 \n", " 10 query hyperthyroid 3771 non-null int64 \n", " 11 lithium 3771 non-null int64 \n", " 12 goitre 3771 non-null int64 \n", " 13 tumor 3771 non-null int64 \n", " 14 hypopituitary 3771 non-null int64 \n", " 15 psych 3771 non-null int64 \n", " 16 TSH measured 3771 non-null int64 \n", " 17 TSH 3771 non-null float64\n", " 18 T3 measured 3771 non-null int64 \n", " 19 T3 3771 non-null float64\n", " 20 TT4 measured 3771 non-null int64 \n", " 21 TT4 3771 non-null float64\n", " 22 T4U measured 3771 non-null int64 \n", " 23 T4U 3771 non-null float64\n", " 24 FTI measured 3771 non-null int64 \n", " 25 FTI 3771 non-null float64\n", " 26 TBG measured 3771 non-null int64 \n", "dtypes: float64(7), int64(20)\n", "memory usage: 795.6 KB\n" ] } ], "source": [ "x.info()" ] }, { "cell_type": "code", "execution_count": 67, "id": "a55ce479-b54d-4d24-b5af-ffa34e13a865", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "on thyroxine 0\n", "query on thyroxine 0\n", "on antithyroid medication 0\n", "sick 0\n", "pregnant 0\n", "thyroid surgery 0\n", "I131 treatment 0\n", "query hypothyroid 0\n", "query hyperthyroid 0\n", "lithium 0\n", "goitre 0\n", "tumor 0\n", "hypopituitary 0\n", "psych 0\n", "TSH measured 0\n", "TSH 0\n", "T3 measured 0\n", "T3 0\n", "TT4 measured 0\n", "TT4 0\n", "T4U measured 0\n", "T4U 0\n", "FTI measured 0\n", "FTI 0\n", "TBG measured 0\n", "dtype: int64" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.isna().sum()" ] }, { "cell_type": "code", "execution_count": 68, "id": "35d11544-6405-499a-a4af-6d0122ffe660", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", " ..\n", "3766 1\n", "3767 1\n", "3768 1\n", "3769 1\n", "3770 1\n", "Name: binaryClass, Length: 3771, dtype: int64" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 70, "id": "4489737c-379b-423b-93eb-f7bf3ac74c9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting statsmodels\n", " Downloading statsmodels-0.14.1-cp310-cp310-win_amd64.whl.metadata (9.8 kB)\n", "Requirement already satisfied: numpy<2,>=1.18 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from statsmodels) (1.26.4)\n", "Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from statsmodels) (1.12.0)\n", "Requirement already satisfied: pandas!=2.1.0,>=1.0 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from statsmodels) (2.2.1)\n", "Collecting patsy>=0.5.4 (from statsmodels)\n", " Downloading patsy-0.5.6-py2.py3-none-any.whl.metadata (3.5 kB)\n", "Requirement already satisfied: packaging>=21.3 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from statsmodels) (24.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2024.1)\n", "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2024.1)\n", "Requirement already satisfied: six in c:\\users\\prajw\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n", "Downloading statsmodels-0.14.1-cp310-cp310-win_amd64.whl (9.8 MB)\n", " ---------------------------------------- 0.0/9.8 MB ? eta -:--:--\n", " ---------------------------------------- 0.0/9.8 MB 1.3 MB/s eta 0:00:08\n", " - -------------------------------------- 0.3/9.8 MB 3.9 MB/s eta 0:00:03\n", " --- ------------------------------------ 1.0/9.8 MB 8.6 MB/s eta 0:00:02\n", " ------- -------------------------------- 1.8/9.8 MB 11.5 MB/s eta 0:00:01\n", " -------- ------------------------------- 2.1/9.8 MB 9.6 MB/s eta 0:00:01\n", " --------- ------------------------------ 2.4/9.8 MB 9.0 MB/s eta 0:00:01\n", " ----------- ---------------------------- 2.8/9.8 MB 8.9 MB/s eta 0:00:01\n", " ------------ --------------------------- 3.0/9.8 MB 8.1 MB/s eta 0:00:01\n", " ------------- -------------------------- 3.4/9.8 MB 8.2 MB/s eta 0:00:01\n", " -------------- ------------------------- 3.6/9.8 MB 7.7 MB/s eta 0:00:01\n", " --------------- ------------------------ 3.7/9.8 MB 7.2 MB/s eta 0:00:01\n", " --------------- ------------------------ 3.9/9.8 MB 6.9 MB/s eta 0:00:01\n", " ---------------- ----------------------- 4.1/9.8 MB 6.6 MB/s eta 0:00:01\n", " ----------------- ---------------------- 4.2/9.8 MB 6.4 MB/s eta 0:00:01\n", " ------------------ --------------------- 4.5/9.8 MB 6.2 MB/s eta 0:00:01\n", " ------------------- -------------------- 4.7/9.8 MB 6.1 MB/s eta 0:00:01\n", " ------------------- -------------------- 4.9/9.8 MB 6.0 MB/s eta 0:00:01\n", " -------------------- ------------------- 5.1/9.8 MB 6.0 MB/s eta 0:00:01\n", " --------------------- ------------------ 5.3/9.8 MB 6.0 MB/s eta 0:00:01\n", " ---------------------- ----------------- 5.6/9.8 MB 5.9 MB/s eta 0:00:01\n", " ----------------------- ---------------- 5.9/9.8 MB 6.0 MB/s eta 0:00:01\n", " ------------------------ --------------- 6.1/9.8 MB 5.9 MB/s eta 0:00:01\n", " ------------------------- -------------- 6.2/9.8 MB 5.8 MB/s eta 0:00:01\n", " -------------------------- ------------- 6.5/9.8 MB 5.8 MB/s eta 0:00:01\n", " --------------------------- ------------ 6.9/9.8 MB 5.8 MB/s eta 0:00:01\n", " ---------------------------- ----------- 7.1/9.8 MB 5.8 MB/s eta 0:00:01\n", " ------------------------------ --------- 7.5/9.8 MB 5.9 MB/s eta 0:00:01\n", " ------------------------------- -------- 7.8/9.8 MB 5.9 MB/s eta 0:00:01\n", " --------------------------------- ------ 8.2/9.8 MB 6.0 MB/s eta 0:00:01\n", " ---------------------------------- ----- 8.5/9.8 MB 6.1 MB/s eta 0:00:01\n", " ------------------------------------ --- 8.9/9.8 MB 6.1 MB/s eta 0:00:01\n", " -------------------------------------- - 9.4/9.8 MB 6.2 MB/s eta 0:00:01\n", " --------------------------------------- 9.8/9.8 MB 6.3 MB/s eta 0:00:01\n", " ---------------------------------------- 9.8/9.8 MB 6.2 MB/s eta 0:00:00\n", "Downloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n", " ---------------------------------------- 0.0/233.9 kB ? eta -:--:--\n", " --------------------------------------- 233.9/233.9 kB 14.0 MB/s eta 0:00:00\n", "Installing collected packages: patsy, statsmodels\n", "Successfully installed patsy-0.5.6 statsmodels-0.14.1\n" ] } ], "source": [ "!pip install statsmodels" ] }, { "cell_type": "code", "execution_count": 71, "id": "9e2058d0-781d-4208-a5f4-2415421fb028", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: binaryClass R-squared: 0.252
Model: OLS Adj. R-squared: 0.247
Method: Least Squares F-statistic: 48.50
Date: Tue, 26 Mar 2024 Prob (F-statistic): 2.74e-213
Time: 11:04:41 Log-Likelihood: 178.13
No. Observations: 3771 AIC: -302.3
Df Residuals: 3744 BIC: -133.9
Df Model: 26
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
const 0.9665 0.058 16.677 0.000 0.853 1.080
age 3.114e-05 0.000 0.147 0.883 -0.000 0.000
sex -0.0316 0.008 -3.803 0.000 -0.048 -0.015
on thyroxine 0.0530 0.012 4.339 0.000 0.029 0.077
query on thyroxine -0.0370 0.034 -1.087 0.277 -0.104 0.030
on antithyroid medication 0.0475 0.036 1.316 0.188 -0.023 0.118
sick 0.0134 0.020 0.679 0.497 -0.025 0.052
pregnant 0.0569 0.035 1.643 0.100 -0.011 0.125
thyroid surgery 0.0964 0.032 2.988 0.003 0.033 0.160
I131 treatment 0.0079 0.031 0.256 0.798 -0.052 0.068
query hypothyroid -0.0715 0.016 -4.501 0.000 -0.103 -0.040
query hyperthyroid -0.0256 0.016 -1.585 0.113 -0.057 0.006
lithium 0.0439 0.055 0.800 0.424 -0.064 0.152
goitre 0.0688 0.040 1.713 0.087 -0.010 0.148
tumor -0.0326 0.024 -1.338 0.181 -0.080 0.015
hypopituitary 0.2031 0.234 0.866 0.386 -0.256 0.663
psych 0.0232 0.018 1.296 0.195 -0.012 0.058
TSH measured -0.1060 0.018 -5.802 0.000 -0.142 -0.070
TSH -0.0040 0.000 -22.858 0.000 -0.004 -0.004
T3 measured 0.0196 0.011 1.782 0.075 -0.002 0.041
T3 0.0215 0.006 3.332 0.001 0.009 0.034
TT4 measured -0.0190 0.029 -0.663 0.507 -0.075 0.037
TT4 0.0016 0.000 3.640 0.000 0.001 0.002
T4U measured -0.1157 0.165 -0.701 0.483 -0.439 0.208
T4U -0.1618 0.055 -2.949 0.003 -0.269 -0.054
FTI measured 0.1438 0.165 0.873 0.383 -0.179 0.467
FTI 5.182e-05 0.000 0.116 0.908 -0.001 0.001
TBG measured 0 0 nan nan 0 0
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 2080.221 Durbin-Watson: 2.009
Prob(Omnibus): 0.000 Jarque-Bera (JB): 14894.830
Skew: -2.605 Prob(JB): 0.00
Kurtosis: 11.226 Cond. No. 1.03e+16


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1e-24. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular." ], "text/latex": [ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", "\\textbf{Dep. Variable:} & binaryClass & \\textbf{ R-squared: } & 0.252 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.247 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 48.50 \\\\\n", "\\textbf{Date:} & Tue, 26 Mar 2024 & \\textbf{ Prob (F-statistic):} & 2.74e-213 \\\\\n", "\\textbf{Time:} & 11:04:41 & \\textbf{ Log-Likelihood: } & 178.13 \\\\\n", "\\textbf{No. Observations:} & 3771 & \\textbf{ AIC: } & -302.3 \\\\\n", "\\textbf{Df Residuals:} & 3744 & \\textbf{ BIC: } & -133.9 \\\\\n", "\\textbf{Df Model:} & 26 & \\textbf{ } & \\\\\n", "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", "\\textbf{const} & 0.9665 & 0.058 & 16.677 & 0.000 & 0.853 & 1.080 \\\\\n", "\\textbf{age} & 3.114e-05 & 0.000 & 0.147 & 0.883 & -0.000 & 0.000 \\\\\n", "\\textbf{sex} & -0.0316 & 0.008 & -3.803 & 0.000 & -0.048 & -0.015 \\\\\n", "\\textbf{on thyroxine} & 0.0530 & 0.012 & 4.339 & 0.000 & 0.029 & 0.077 \\\\\n", "\\textbf{query on thyroxine} & -0.0370 & 0.034 & -1.087 & 0.277 & -0.104 & 0.030 \\\\\n", "\\textbf{on antithyroid medication} & 0.0475 & 0.036 & 1.316 & 0.188 & -0.023 & 0.118 \\\\\n", "\\textbf{sick} & 0.0134 & 0.020 & 0.679 & 0.497 & -0.025 & 0.052 \\\\\n", "\\textbf{pregnant} & 0.0569 & 0.035 & 1.643 & 0.100 & -0.011 & 0.125 \\\\\n", "\\textbf{thyroid surgery} & 0.0964 & 0.032 & 2.988 & 0.003 & 0.033 & 0.160 \\\\\n", "\\textbf{I131 treatment} & 0.0079 & 0.031 & 0.256 & 0.798 & -0.052 & 0.068 \\\\\n", "\\textbf{query hypothyroid} & -0.0715 & 0.016 & -4.501 & 0.000 & -0.103 & -0.040 \\\\\n", "\\textbf{query hyperthyroid} & -0.0256 & 0.016 & -1.585 & 0.113 & -0.057 & 0.006 \\\\\n", "\\textbf{lithium} & 0.0439 & 0.055 & 0.800 & 0.424 & -0.064 & 0.152 \\\\\n", "\\textbf{goitre} & 0.0688 & 0.040 & 1.713 & 0.087 & -0.010 & 0.148 \\\\\n", "\\textbf{tumor} & -0.0326 & 0.024 & -1.338 & 0.181 & -0.080 & 0.015 \\\\\n", "\\textbf{hypopituitary} & 0.2031 & 0.234 & 0.866 & 0.386 & -0.256 & 0.663 \\\\\n", "\\textbf{psych} & 0.0232 & 0.018 & 1.296 & 0.195 & -0.012 & 0.058 \\\\\n", "\\textbf{TSH measured} & -0.1060 & 0.018 & -5.802 & 0.000 & -0.142 & -0.070 \\\\\n", "\\textbf{TSH} & -0.0040 & 0.000 & -22.858 & 0.000 & -0.004 & -0.004 \\\\\n", "\\textbf{T3 measured} & 0.0196 & 0.011 & 1.782 & 0.075 & -0.002 & 0.041 \\\\\n", "\\textbf{T3} & 0.0215 & 0.006 & 3.332 & 0.001 & 0.009 & 0.034 \\\\\n", "\\textbf{TT4 measured} & -0.0190 & 0.029 & -0.663 & 0.507 & -0.075 & 0.037 \\\\\n", "\\textbf{TT4} & 0.0016 & 0.000 & 3.640 & 0.000 & 0.001 & 0.002 \\\\\n", "\\textbf{T4U measured} & -0.1157 & 0.165 & -0.701 & 0.483 & -0.439 & 0.208 \\\\\n", "\\textbf{T4U} & -0.1618 & 0.055 & -2.949 & 0.003 & -0.269 & -0.054 \\\\\n", "\\textbf{FTI measured} & 0.1438 & 0.165 & 0.873 & 0.383 & -0.179 & 0.467 \\\\\n", "\\textbf{FTI} & 5.182e-05 & 0.000 & 0.116 & 0.908 & -0.001 & 0.001 \\\\\n", "\\textbf{TBG measured} & 0 & 0 & nan & nan & 0 & 0 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", "\\textbf{Omnibus:} & 2080.221 & \\textbf{ Durbin-Watson: } & 2.009 \\\\\n", "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 14894.830 \\\\\n", "\\textbf{Skew:} & -2.605 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", "\\textbf{Kurtosis:} & 11.226 & \\textbf{ Cond. No. } & 1.03e+16 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{OLS Regression Results}\n", "\\end{center}\n", "\n", "Notes: \\newline\n", " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n", " [2] The smallest eigenvalue is 1e-24. This might indicate that there are \\newline\n", " strong multicollinearity problems or that the design matrix is singular." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: binaryClass R-squared: 0.252\n", "Model: OLS Adj. R-squared: 0.247\n", "Method: Least Squares F-statistic: 48.50\n", "Date: Tue, 26 Mar 2024 Prob (F-statistic): 2.74e-213\n", "Time: 11:04:41 Log-Likelihood: 178.13\n", "No. Observations: 3771 AIC: -302.3\n", "Df Residuals: 3744 BIC: -133.9\n", "Df Model: 26 \n", "Covariance Type: nonrobust \n", "=============================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------------------\n", "const 0.9665 0.058 16.677 0.000 0.853 1.080\n", "age 3.114e-05 0.000 0.147 0.883 -0.000 0.000\n", "sex -0.0316 0.008 -3.803 0.000 -0.048 -0.015\n", "on thyroxine 0.0530 0.012 4.339 0.000 0.029 0.077\n", "query on thyroxine -0.0370 0.034 -1.087 0.277 -0.104 0.030\n", "on antithyroid medication 0.0475 0.036 1.316 0.188 -0.023 0.118\n", "sick 0.0134 0.020 0.679 0.497 -0.025 0.052\n", "pregnant 0.0569 0.035 1.643 0.100 -0.011 0.125\n", "thyroid surgery 0.0964 0.032 2.988 0.003 0.033 0.160\n", "I131 treatment 0.0079 0.031 0.256 0.798 -0.052 0.068\n", "query hypothyroid -0.0715 0.016 -4.501 0.000 -0.103 -0.040\n", "query hyperthyroid -0.0256 0.016 -1.585 0.113 -0.057 0.006\n", "lithium 0.0439 0.055 0.800 0.424 -0.064 0.152\n", "goitre 0.0688 0.040 1.713 0.087 -0.010 0.148\n", "tumor -0.0326 0.024 -1.338 0.181 -0.080 0.015\n", "hypopituitary 0.2031 0.234 0.866 0.386 -0.256 0.663\n", "psych 0.0232 0.018 1.296 0.195 -0.012 0.058\n", "TSH measured -0.1060 0.018 -5.802 0.000 -0.142 -0.070\n", "TSH -0.0040 0.000 -22.858 0.000 -0.004 -0.004\n", "T3 measured 0.0196 0.011 1.782 0.075 -0.002 0.041\n", "T3 0.0215 0.006 3.332 0.001 0.009 0.034\n", "TT4 measured -0.0190 0.029 -0.663 0.507 -0.075 0.037\n", "TT4 0.0016 0.000 3.640 0.000 0.001 0.002\n", "T4U measured -0.1157 0.165 -0.701 0.483 -0.439 0.208\n", "T4U -0.1618 0.055 -2.949 0.003 -0.269 -0.054\n", "FTI measured 0.1438 0.165 0.873 0.383 -0.179 0.467\n", "FTI 5.182e-05 0.000 0.116 0.908 -0.001 0.001\n", "TBG measured 0 0 nan nan 0 0\n", "==============================================================================\n", "Omnibus: 2080.221 Durbin-Watson: 2.009\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 14894.830\n", "Skew: -2.605 Prob(JB): 0.00\n", "Kurtosis: 11.226 Cond. No. 1.03e+16\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The smallest eigenvalue is 1e-24. This might indicate that there are\n", "strong multicollinearity problems or that the design matrix is singular.\n", "\"\"\"" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import statsmodels.api as sm\n", "x=sm.add_constant(x)\n", "result=sm.OLS(y,x).fit()\n", "result.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "169c353e-7784-4865-94a4-bcab5627ed2b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "4e37b0c4-5253-45fa-9e1f-8a9238d5a3c5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "faa0313b-bd38-4848-acd8-e2b5300f2b4a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0482108e-b54b-47bc-a2b3-291a9fb78e52", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "fca1b75c-9f8b-4f9d-84c0-8c3af35e4bed", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c961af67-ae9d-4bbf-a94a-8fc88c4981ad", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }