{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plot\n",
"import seaborn as sns\n",
"from sklearn.utils import resample\n",
"from imblearn.over_sampling import SMOTENC,RandomOverSampler,KMeansSMOTE\n",
"from sklearn.impute import KNNImputer\n",
"from sklearn.preprocessing import LabelEncoder\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('hypothyroid.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3772, 30)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
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" <th>query_hypothyroid</th>\n",
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" <th>TT4</th>\n",
" <th>T4U_measured</th>\n",
" <th>T4U</th>\n",
" <th>FTI_measured</th>\n",
" <th>FTI</th>\n",
" <th>TBG_measured</th>\n",
" <th>TBG</th>\n",
" <th>referral_source</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>41</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
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" <td>t</td>\n",
" <td>109</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>SVHC</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>23</td>\n",
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" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70</td>\n",
" <td>F</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
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" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>...</td>\n",
" <td>t</td>\n",
" <td>175</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>70</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
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" <td>f</td>\n",
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" <td>f</td>\n",
" <td>?</td>\n",
" <td>SVI</td>\n",
" <td>negative</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick \\\n",
"0 41 F f f f f \n",
"1 23 F f f f f \n",
"2 46 M f f f f \n",
"3 70 F t f f f \n",
"4 70 F f f f f \n",
"\n",
" pregnant thyroid_surgery I131_treatment query_hypothyroid ... TT4_measured \\\n",
"0 f f f f ... t \n",
"1 f f f f ... t \n",
"2 f f f f ... t \n",
"3 f f f f ... t \n",
"4 f f f f ... t \n",
"\n",
" TT4 T4U_measured T4U FTI_measured FTI TBG_measured TBG referral_source \\\n",
"0 125 t 1.14 t 109 f ? SVHC \n",
"1 102 f ? f ? f ? other \n",
"2 109 t 0.91 t 120 f ? other \n",
"3 175 f ? f ? f ? other \n",
"4 61 t 0.87 t 70 f ? SVI \n",
"\n",
" Class \n",
"0 negative \n",
"1 negative \n",
"2 negative \n",
"3 negative \n",
"4 negative \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Problem Statement : To build a classification methodology to predict the type of Thyroid a person has ,based on the below features.\n",
"\n",
"age - Age of the person\n",
"\n",
"sex - Male or Female\n",
"\n",
"on_thyroxine - true or false\n",
"\n",
"on_antithyroid_medication - true or false\n",
"\n",
"sick - true or false\n",
"\n",
"pregnant - true or false\n",
"\n",
"thyroid_surgery - true or false\n",
"\n",
"I131_treatment - true or false\n",
"\n",
"query_hypothyroid - true or false\n",
"\n",
"query_hyperthyroid -true or false\n",
"\n",
"lithium - true or false\n",
"\n",
"goitre - true or false\n",
"\n",
"tumor - true or false\n",
"\n",
"hypopituitary- true or false\n",
"\n",
"psych - true or false\n",
"\n",
"TSH_measured - true or false\n",
"\n",
"TSH - thyroid stimulating hormone floating value\n",
"\n",
"T3_measured - true or false\n",
"\n",
"T3 - triiodothyronine value\n",
"\n",
"TT4_measured- true or false\n",
"\n",
"TT4 - Thyroxine value\n",
"\n",
"T4U_measured- true or false\n",
"\n",
"T4U - numerical value\n",
"\n",
"FTI_measured- true or false\n",
"\n",
"FTI -Free Thyroxine Index\n",
"\n",
"TBG_measured- true or false\n",
"\n",
"TBG -Thyroid-Binding Globulin value\n",
"\n",
"referral_source - different sources of referals\n",
"\n",
"Class - different types of thyroid"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th>referral_source</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3772</td>\n",
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" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>95</td>\n",
" <td>2480</td>\n",
" <td>3308</td>\n",
" <td>3722</td>\n",
" <td>3729</td>\n",
" <td>3625</td>\n",
" <td>3719</td>\n",
" <td>3719</td>\n",
" <td>3713</td>\n",
" <td>3538</td>\n",
" <td>...</td>\n",
" <td>3541</td>\n",
" <td>231</td>\n",
" <td>3385</td>\n",
" <td>387</td>\n",
" <td>3387</td>\n",
" <td>385</td>\n",
" <td>3772</td>\n",
" <td>3772</td>\n",
" <td>2201</td>\n",
" <td>3481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication \\\n",
"count 3772 3772 3772 3772 3772 \n",
"unique 94 3 2 2 2 \n",
"top 59 F f f f \n",
"freq 95 2480 3308 3722 3729 \n",
"\n",
" sick pregnant thyroid_surgery I131_treatment query_hypothyroid ... \\\n",
"count 3772 3772 3772 3772 3772 ... \n",
"unique 2 2 2 2 2 ... \n",
"top f f f f f ... \n",
"freq 3625 3719 3719 3713 3538 ... \n",
"\n",
" TT4_measured TT4 T4U_measured T4U FTI_measured FTI TBG_measured \\\n",
"count 3772 3772 3772 3772 3772 3772 3772 \n",
"unique 2 242 2 147 2 235 1 \n",
"top t ? t ? t ? f \n",
"freq 3541 231 3385 387 3387 385 3772 \n",
"\n",
" TBG referral_source Class \n",
"count 3772 3772 3772 \n",
"unique 1 5 4 \n",
"top ? other negative \n",
"freq 3772 2201 3481 \n",
"\n",
"[4 rows x 30 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see from the data description that there are no missing values. But if you check the dataset the missing values are replaced with invalid values like '?'. Let's replace such values with 'nan' and check for missing values again."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age 1\n",
"sex 150\n",
"TSH 369\n",
"T3 769\n",
"TT4 231\n",
"T4U 387\n",
"FTI 385\n",
"TBG 3772\n"
]
}
],
"source": [
"for column in data.columns:\n",
" count = data[column][data[column]=='?'].count()\n",
" if count!=0:\n",
" print(column, data[column][data[column]=='?'].count())\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So these are the columns which have missing values but missing values are replaced with '?'. We will replace these values with 'nan' and then do imputation of these missing values. \n",
"\n",
"Also, we can see thatfor column 'TBG' all the values are missing. So we will drop this column as it is of no use to us."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data = data.drop(['TBG'],axis =1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, looking to the dataset, we can see that some columns are with true and false value are just the indication that whether the next column has values or not. Let's see an example:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <th>T4U</th>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>f</td>\n",
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" <td>t</td>\n",
" <td>0.87</td>\n",
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" <th>5</th>\n",
" <td>t</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>t</td>\n",
" <td>0.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>t</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
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" <th>8</th>\n",
" <td>t</td>\n",
" <td>0.93</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>t</td>\n",
" <td>0.89</td>\n",
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" <th>10</th>\n",
" <td>t</td>\n",
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" <td>t</td>\n",
" <td>0.99</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>t</td>\n",
" <td>1.13</td>\n",
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" <th>13</th>\n",
" <td>t</td>\n",
" <td>0.91</td>\n",
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" <td>0.91</td>\n",
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" <td>t</td>\n",
" <td>1.14</td>\n",
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" <th>16</th>\n",
" <td>t</td>\n",
" <td>0.86</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>t</td>\n",
" <td>0.96</td>\n",
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" <th>18</th>\n",
" <td>t</td>\n",
" <td>0.95</td>\n",
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" <th>19</th>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
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" <th>20</th>\n",
" <td>t</td>\n",
" <td>0.86</td>\n",
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" <th>21</th>\n",
" <td>t</td>\n",
" <td>0.91</td>\n",
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" <th>22</th>\n",
" <td>t</td>\n",
" <td>0.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>t</td>\n",
" <td>0.9</td>\n",
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" <tr>\n",
" <th>24</th>\n",
" <td>t</td>\n",
" <td>1.02</td>\n",
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" <tr>\n",
" <th>25</th>\n",
" <td>t</td>\n",
" <td>1.05</td>\n",
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" <th>26</th>\n",
" <td>t</td>\n",
" <td>0.62</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3743</th>\n",
" <td>t</td>\n",
" <td>0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3744</th>\n",
" <td>t</td>\n",
" <td>0.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3745</th>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3746</th>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3747</th>\n",
" <td>t</td>\n",
" <td>1.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3748</th>\n",
" <td>t</td>\n",
" <td>0.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3749</th>\n",
" <td>t</td>\n",
" <td>0.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3750</th>\n",
" <td>t</td>\n",
" <td>1.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3751</th>\n",
" <td>t</td>\n",
" <td>0.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3752</th>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3753</th>\n",
" <td>t</td>\n",
" <td>1.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3754</th>\n",
" <td>t</td>\n",
" <td>0.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3755</th>\n",
" <td>t</td>\n",
" <td>0.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3756</th>\n",
" <td>t</td>\n",
" <td>1.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3757</th>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3758</th>\n",
" <td>t</td>\n",
" <td>0.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3759</th>\n",
" <td>t</td>\n",
" <td>1.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3760</th>\n",
" <td>t</td>\n",
" <td>0.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3761</th>\n",
" <td>t</td>\n",
" <td>0.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3762</th>\n",
" <td>t</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3763</th>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3764</th>\n",
" <td>t</td>\n",
" <td>0.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3765</th>\n",
" <td>t</td>\n",
" <td>1.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3766</th>\n",
" <td>t</td>\n",
" <td>1.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3767</th>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3768</th>\n",
" <td>t</td>\n",
" <td>1.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3769</th>\n",
" <td>t</td>\n",
" <td>1.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3770</th>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3771</th>\n",
" <td>t</td>\n",
" <td>1.07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3772 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" T4U_measured T4U\n",
"0 t 1.14\n",
"1 f ?\n",
"2 t 0.91\n",
"3 f ?\n",
"4 t 0.87\n",
"5 t 1.3\n",
"6 t 0.92\n",
"7 t 0.7\n",
"8 t 0.93\n",
"9 t 0.89\n",
"10 t 0.95\n",
"11 t 0.99\n",
"12 t 1.13\n",
"13 t 0.91\n",
"14 t 0.91\n",
"15 t 1.14\n",
"16 t 0.86\n",
"17 t 0.96\n",
"18 t 0.95\n",
"19 t 0.94\n",
"20 t 0.86\n",
"21 t 0.91\n",
"22 t 0.96\n",
"23 t 0.9\n",
"24 t 1.02\n",
"25 t 1.05\n",
"26 t 0.62\n",
"27 f ?\n",
"28 t 1.06\n",
"29 t 0.95\n",
"... ... ...\n",
"3742 t 1.2\n",
"3743 t 0.87\n",
"3744 t 0.75\n",
"3745 f ?\n",
"3746 f ?\n",
"3747 t 1.17\n",
"3748 t 0.89\n",
"3749 t 0.93\n",
"3750 t 1.08\n",
"3751 t 0.86\n",
"3752 f ?\n",
"3753 t 1.01\n",
"3754 t 0.95\n",
"3755 t 0.99\n",
"3756 t 1.14\n",
"3757 t 0.94\n",
"3758 t 0.93\n",
"3759 t 1.01\n",
"3760 t 0.95\n",
"3761 t 0.98\n",
"3762 t 0.7\n",
"3763 f ?\n",
"3764 t 0.85\n",
"3765 t 1.13\n",
"3766 t 1.11\n",
"3767 f ?\n",
"3768 t 1.08\n",
"3769 t 1.07\n",
"3770 t 0.94\n",
"3771 t 1.07\n",
"\n",
"[3772 rows x 2 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['T4U_measured','T4U']] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since, we are any ways going to handle the missing values, there is no point of having such columns in our dataset.\n",
"\n",
"Let's drop such columns as well."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data = data.drop(['TSH_measured','T3_measured','TT4_measured','T4U_measured','FTI_measured','TBG_measured'],axis =1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Now let's replace the '?' values with numpy nan\n",
"for column in data.columns:\n",
" count = data[column][data[column]=='?'].count()\n",
" if count!=0:\n",
" data[column] = data[column].replace('?',np.nan) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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 0\n",
"T3 0\n",
"TT4 0\n",
"T4U 0\n",
"FTI 0\n",
"referral_source 0\n",
"Class 0\n"
]
}
],
"source": [
"for column in data.columns:\n",
" count = data[column][data[column]=='?'].count()\n",
" if count==0:\n",
" print(column, data[column][data[column]=='?'].count()) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great!! Now that we have replaced all such values with 'nan'. Let's deal with these missing values now."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 1\n",
"sex 150\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 369\n",
"T3 769\n",
"TT4 231\n",
"T4U 387\n",
"FTI 385\n",
"referral_source 0\n",
"Class 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the values are categorical, we have to change them to numerical before we use any imputation techniques.\n",
"\n",
"We can use get dummies but since most of the columns have only two distinct categories we will use mapping for them. Why? Because since there are only two categories then the two columns formed after get dummies will both have very high correaltion since they both explain the same thing. So in anyway we will have to drop one of the columns. That's why let's use mapping for such columns.\n",
"For columns with more than two categories we will use get dummies."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# for column in data.columns:\n",
"# print(column, (data[column].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# We can map the categorical values like below:\n",
"data['sex'] = data['sex'].map({'F' : 0, 'M' : 1})\n",
"\n",
"# except for 'Sex' column all the other columns with two categorical data have same value 'f' and 't'.\n",
"# so instead of mapping indvidually, let's do a smarter work\n",
"for column in data.columns:\n",
" if len(data[column].unique())==2:\n",
" data[column] = data[column].map({'f' : 0, 't' : 1})\n",
" \n",
"# this will map all the rest of the columns as we require. Now there are handful of column left with more than 2 categories. \n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['SVHC', 'other', 'SVI', 'STMW', 'SVHD'], dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['referral_source'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# we will use get_dummies with that.\n",
"data = pd.get_dummies(data, columns=['referral_source'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now our ouptut class also has 4 distinct categories. There is no sense of using get dummies with our Output class, so we will just map them.\n",
"Let's use LabelEncoder function for this."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['negative', 'compensated_hypothyroid', 'primary_hypothyroid',\n",
" 'secondary_hypothyroid'], dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Class'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"lblEn = LabelEncoder()\n",
"\n",
"data['Class'] =lblEn.fit_transform(data['Class'])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>...</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_STMW</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>41</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.5</td>\n",
" <td>125</td>\n",
" <td>1.14</td>\n",
" <td>109</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>23</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>102</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>46</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
" <td>109</td>\n",
" <td>0.91</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.9</td>\n",
" <td>175</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>70</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.2</td>\n",
" <td>61</td>\n",
" <td>0.87</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick \\\n",
"0 41 0.0 0 0 0 0 \n",
"1 23 0.0 0 0 0 0 \n",
"2 46 1.0 0 0 0 0 \n",
"3 70 0.0 1 0 0 0 \n",
"4 70 0.0 0 0 0 0 \n",
"\n",
" pregnant thyroid_surgery I131_treatment query_hypothyroid ... T3 \\\n",
"0 0 0 0 0 ... 2.5 \n",
"1 0 0 0 0 ... 2 \n",
"2 0 0 0 0 ... NaN \n",
"3 0 0 0 0 ... 1.9 \n",
"4 0 0 0 0 ... 1.2 \n",
"\n",
" TT4 T4U FTI Class referral_source_STMW referral_source_SVHC \\\n",
"0 125 1.14 109 1 0 1 \n",
"1 102 NaN NaN 1 0 0 \n",
"2 109 0.91 120 1 0 0 \n",
"3 175 NaN NaN 1 0 0 \n",
"4 61 0.87 70 1 0 0 \n",
"\n",
" referral_source_SVHD referral_source_SVI referral_source_other \n",
"0 0 0 0 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 1 0 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_STMW</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>3771</td>\n",
" <td>3622.000000</td>\n",
" <td>3772.000000</td>\n",
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" <td>3003</td>\n",
" <td>3541</td>\n",
" <td>3385</td>\n",
" <td>3387</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>unique</td>\n",
" <td>93</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>69</td>\n",
" <td>241</td>\n",
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" <td>234</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>top</td>\n",
" <td>59</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>101</td>\n",
" <td>0.99</td>\n",
" <td>100</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>freq</td>\n",
" <td>95</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>238</td>\n",
" <td>71</td>\n",
" <td>95</td>\n",
" <td>73</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>NaN</td>\n",
" <td>0.315295</td>\n",
" <td>0.123012</td>\n",
" <td>0.013256</td>\n",
" <td>0.011400</td>\n",
" <td>0.038971</td>\n",
" <td>0.014051</td>\n",
" <td>0.014051</td>\n",
" <td>0.015642</td>\n",
" <td>0.062036</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.974814</td>\n",
" <td>0.029692</td>\n",
" <td>0.102333</td>\n",
" <td>0.010339</td>\n",
" <td>0.274125</td>\n",
" <td>0.583510</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>NaN</td>\n",
" <td>0.464698</td>\n",
" <td>0.328494</td>\n",
" <td>0.114382</td>\n",
" <td>0.106174</td>\n",
" <td>0.193552</td>\n",
" <td>0.117716</td>\n",
" <td>0.117716</td>\n",
" <td>0.124101</td>\n",
" <td>0.241253</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.279508</td>\n",
" <td>0.169760</td>\n",
" <td>0.303126</td>\n",
" <td>0.101169</td>\n",
" <td>0.446131</td>\n",
" <td>0.493042</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>11 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine \\\n",
"count 3771 3622.000000 3772.000000 3772.000000 \n",
"unique 93 NaN NaN NaN \n",
"top 59 NaN NaN NaN \n",
"freq 95 NaN NaN NaN \n",
"mean NaN 0.315295 0.123012 0.013256 \n",
"std NaN 0.464698 0.328494 0.114382 \n",
"min NaN 0.000000 0.000000 0.000000 \n",
"25% NaN 0.000000 0.000000 0.000000 \n",
"50% NaN 0.000000 0.000000 0.000000 \n",
"75% NaN 1.000000 0.000000 0.000000 \n",
"max NaN 1.000000 1.000000 1.000000 \n",
"\n",
" on_antithyroid_medication sick pregnant thyroid_surgery \\\n",
"count 3772.000000 3772.000000 3772.000000 3772.000000 \n",
"unique NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN \n",
"mean 0.011400 0.038971 0.014051 0.014051 \n",
"std 0.106174 0.193552 0.117716 0.117716 \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 TT4 T4U FTI \\\n",
"count 3772.000000 3772.000000 ... 3003 3541 3385 3387 \n",
"unique NaN NaN ... 69 241 146 234 \n",
"top NaN NaN ... 2 101 0.99 100 \n",
"freq NaN NaN ... 238 71 95 73 \n",
"mean 0.015642 0.062036 ... NaN NaN NaN NaN \n",
"std 0.124101 0.241253 ... NaN NaN NaN NaN \n",
"min 0.000000 0.000000 ... NaN NaN NaN NaN \n",
"25% 0.000000 0.000000 ... NaN NaN NaN NaN \n",
"50% 0.000000 0.000000 ... NaN NaN NaN NaN \n",
"75% 0.000000 0.000000 ... NaN NaN NaN NaN \n",
"max 1.000000 1.000000 ... NaN NaN NaN NaN \n",
"\n",
" Class referral_source_STMW referral_source_SVHC \\\n",
"count 3772.000000 3772.000000 3772.000000 \n",
"unique NaN NaN NaN \n",
"top NaN NaN NaN \n",
"freq NaN NaN NaN \n",
"mean 0.974814 0.029692 0.102333 \n",
"std 0.279508 0.169760 0.303126 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 1.000000 0.000000 0.000000 \n",
"50% 1.000000 0.000000 0.000000 \n",
"75% 1.000000 0.000000 0.000000 \n",
"max 3.000000 1.000000 1.000000 \n",
"\n",
" referral_source_SVHD referral_source_SVI referral_source_other \n",
"count 3772.000000 3772.000000 3772.000000 \n",
"unique NaN NaN NaN \n",
"top NaN NaN NaN \n",
"freq NaN NaN NaN \n",
"mean 0.010339 0.274125 0.583510 \n",
"std 0.101169 0.446131 0.493042 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 1.000000 \n",
"75% 0.000000 1.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
"[11 rows x 27 columns]"
]
},
"execution_count": 450,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 451,
"metadata": {},
"outputs": [],
"source": [
"# for column in data.columns:\n",
"# print(column, (data[column].unique()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Now that we have encoded all our Categorical values. Let's start with imputing the missing values."
]
},
{
"cell_type": "code",
"execution_count": 452,
"metadata": {},
"outputs": [],
"source": [
"imputer=KNNImputer(n_neighbors=3, weights='uniform',missing_values=np.nan)\n",
"new_array=imputer.fit_transform(data) # impute the missing values\n",
" # convert the nd-array returned in the step above to a Dataframe\n",
"new_data=pd.DataFrame(data=np.round(new_array), columns=data.columns)"
]
},
{
"cell_type": "code",
"execution_count": 453,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>...</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_STMW</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
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" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" <td>3772.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>51.737275</td>\n",
" <td>0.307529</td>\n",
" <td>0.123012</td>\n",
" <td>0.013256</td>\n",
" <td>0.011400</td>\n",
" <td>0.038971</td>\n",
" <td>0.014051</td>\n",
" <td>0.014051</td>\n",
" <td>0.015642</td>\n",
" <td>0.062036</td>\n",
" <td>...</td>\n",
" <td>2.027306</td>\n",
" <td>108.459438</td>\n",
" <td>1.020944</td>\n",
" <td>110.301166</td>\n",
" <td>0.974814</td>\n",
" <td>0.029692</td>\n",
" <td>0.102333</td>\n",
" <td>0.010339</td>\n",
" <td>0.274125</td>\n",
" <td>0.583510</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>20.082478</td>\n",
" <td>0.461532</td>\n",
" <td>0.328494</td>\n",
" <td>0.114382</td>\n",
" <td>0.106174</td>\n",
" <td>0.193552</td>\n",
" <td>0.117716</td>\n",
" <td>0.117716</td>\n",
" <td>0.124101</td>\n",
" <td>0.241253</td>\n",
" <td>...</td>\n",
" <td>0.785068</td>\n",
" <td>34.838114</td>\n",
" <td>0.165546</td>\n",
" <td>32.145618</td>\n",
" <td>0.279508</td>\n",
" <td>0.169760</td>\n",
" <td>0.303126</td>\n",
" <td>0.101169</td>\n",
" <td>0.446131</td>\n",
" <td>0.493042</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>36.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>88.000000</td>\n",
" <td>1.000000</td>\n",
" <td>93.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>54.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>104.000000</td>\n",
" <td>1.000000</td>\n",
" <td>107.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>67.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>124.000000</td>\n",
" <td>1.000000</td>\n",
" <td>124.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>455.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>11.000000</td>\n",
" <td>430.000000</td>\n",
" <td>2.000000</td>\n",
" <td>395.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine \\\n",
"count 3772.000000 3772.000000 3772.000000 3772.000000 \n",
"mean 51.737275 0.307529 0.123012 0.013256 \n",
"std 20.082478 0.461532 0.328494 0.114382 \n",
"min 1.000000 0.000000 0.000000 0.000000 \n",
"25% 36.000000 0.000000 0.000000 0.000000 \n",
"50% 54.000000 0.000000 0.000000 0.000000 \n",
"75% 67.000000 1.000000 0.000000 0.000000 \n",
"max 455.000000 1.000000 1.000000 1.000000 \n",
"\n",
" on_antithyroid_medication sick pregnant thyroid_surgery \\\n",
"count 3772.000000 3772.000000 3772.000000 3772.000000 \n",
"mean 0.011400 0.038971 0.014051 0.014051 \n",
"std 0.106174 0.193552 0.117716 0.117716 \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 TT4 \\\n",
"count 3772.000000 3772.000000 ... 3772.000000 3772.000000 \n",
"mean 0.015642 0.062036 ... 2.027306 108.459438 \n",
"std 0.124101 0.241253 ... 0.785068 34.838114 \n",
"min 0.000000 0.000000 ... 0.000000 2.000000 \n",
"25% 0.000000 0.000000 ... 2.000000 88.000000 \n",
"50% 0.000000 0.000000 ... 2.000000 104.000000 \n",
"75% 0.000000 0.000000 ... 2.000000 124.000000 \n",
"max 1.000000 1.000000 ... 11.000000 430.000000 \n",
"\n",
" T4U FTI Class referral_source_STMW \\\n",
"count 3772.000000 3772.000000 3772.000000 3772.000000 \n",
"mean 1.020944 110.301166 0.974814 0.029692 \n",
"std 0.165546 32.145618 0.279508 0.169760 \n",
"min 0.000000 2.000000 0.000000 0.000000 \n",
"25% 1.000000 93.000000 1.000000 0.000000 \n",
"50% 1.000000 107.000000 1.000000 0.000000 \n",
"75% 1.000000 124.000000 1.000000 0.000000 \n",
"max 2.000000 395.000000 3.000000 1.000000 \n",
"\n",
" referral_source_SVHC referral_source_SVHD referral_source_SVI \\\n",
"count 3772.000000 3772.000000 3772.000000 \n",
"mean 0.102333 0.010339 0.274125 \n",
"std 0.303126 0.101169 0.446131 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" referral_source_other \n",
"count 3772.000000 \n",
"mean 0.583510 \n",
"std 0.493042 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
"75% 1.000000 \n",
"max 1.000000 \n",
"\n",
"[8 rows x 27 columns]"
]
},
"execution_count": 453,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 454,
"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 0\n",
"T3 0\n",
"TT4 0\n",
"T4U 0\n",
"FTI 0\n",
"Class 0\n",
"referral_source_STMW 0\n",
"referral_source_SVHC 0\n",
"referral_source_SVHD 0\n",
"referral_source_SVI 0\n",
"referral_source_other 0\n",
"dtype: int64"
]
},
"execution_count": 454,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_data.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Now there are no missing values in our new dataset. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check the distribution for our continous data in the dataset:"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x1080 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"columns = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plot.figure(figsize=(10,15),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for column in columns:\n",
" ax = plot.subplot(3,2,plotnumber)\n",
" sns.distplot(new_data[column])\n",
" plot.xlabel(column,fontsize=10)\n",
" plotnumber+=1\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The graphs for age, TSH and T3 looks heavely skewed towards left. Let's do some transformations to the data and see if it improves the plot.\n",
"\n",
"Before doing log transformation , let's add 1 to each valuue in the column to handle exception when we try to find log of '0'."
]
},
{
"cell_type": "code",
"execution_count": 470,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x1080 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"columns = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plot.figure(figsize=(10,15),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for column in columns:\n",
" new_data[column]+=1\n",
" ax = plot.subplot(3,2,plotnumber)\n",
" sns.distplot(np.log(new_data[column]))\n",
" plot.xlabel(column,fontsize=10)\n",
" plotnumber+=1\n",
"plot.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After log transformation, rest of the columns look fine but 'TSH' has a weird trend.\n",
"\n",
"It won't give much of information so let's drop this column."
]
},
{
"cell_type": "code",
"execution_count": 471,
"metadata": {},
"outputs": [],
"source": [
"new_data = new_data.drop(['TSH'],axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"#### let's see how balanced our dataset in terms of given target classes:"
]
},
{
"cell_type": "code",
"execution_count": 473,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1dec93964c8>"
]
},
"execution_count": 473,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEPCAYAAACHuClZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHZdJREFUeJzt3W9wVPXd9/HPJptEMbkuhGuXMIFJp8hUG26Ig0Vwxk0Z625ksxMMtEKQaK2AqKFlLDQkGSKOQm7MmDEjwbZSWpCbklJJNBMXGOpkRoMF01YaJ1KKSVoS3GxCwATJ3z33Ay+3RhSSHDZ/8P16kpzvOWfzPb8H+9nzO3tOLIZhGAIAwISwkW4AADD2ESYAANMIEwCAaYQJAMA0wgQAYBphAgAwjTABAJhGmAAATCNMAACmESYAANMIEwCAaYQJAMA0wgQAYJp1pBsItba2iwoEeDAyAAxEWJhFN99806D3u+7DJBAwCBMACDGmuQAAphEmAADTQhomL774ohYsWCC3262dO3dKkjZs2CCn06nU1FSlpqbq8OHDkqSqqip5PB45nU4VFhYGX6O2tlZpaWlyuVzKyclRb29vKFsGAAxByK6ZHDt2TO+++65ef/119fb2asGCBUpKSlJNTY1effVV2e324LadnZ3Kzs7W7t27NXnyZK1atUqVlZVKSkrSunXr9OyzzyoxMVHZ2dkqKSlRenp6qNoGAAxByM5M5syZo127dslqtaq1tVV9fX264YYb1NTUpOzsbHk8HhUVFSkQCOjEiROKj4/X1KlTZbVa5fF45PV61djYqM7OTiUmJkqS0tLS5PV6Q9UyAGCIQjrNFRERoaKiIrndbs2bN0+9vb2aO3euNm/erJKSEr333nvav3+/mpubZbPZgvvZ7Xb5fL7L6jabTT6fL5QtAwCGIORfDV6zZo1WrFihxx57TEePHtW2bduC65YvX67S0lK5XC5ZLJZg3TAMWSwWBQKBr6wPxsSJ0eYPAgBwRSELk9OnT6u7u1u33XabbrzxRjmdTlVUVGj8+PFyuVySPgsHq9Wq2NhY+f3+4L5+v192u/2yektLS79rLQPR2trBfSbD5Ob/jpQ1Mmqk2xgVeru71Hahe6TbAAYtLMwypA/hIQuTM2fOqKioSHv37pUkHTlyRN/73ve0efNmzZ07V+PGjdO+fft0//33a9asWaqrq1NDQ4OmTJmi8vJyLVq0SHFxcYqKilJ1dbVmz56tsrIyORyOULUMk6yRUare+uhItzEqzF7/iiTCBN8cIQuTpKQknThxQgsXLlR4eLicTqeefPJJ3XzzzVq6dKl6e3vldDqVkpIiScrPz1dmZqa6urqUlJSk5ORkSVJBQYFyc3PV0dGhhIQEZWRkhKplAMAQWQzDuK7ngJjmGj42WwxnJv9r9vpX5Pe3j3QbwKANdZqLO+ABAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgWkjD5MUXX9SCBQvkdru1c+dOSVJVVZU8Ho+cTqcKCwuD29bW1iotLU0ul0s5OTnq7e2VJDU1NWnZsmVKTk7W6tWrdfHixVC2DAAYgpCFybFjx/Tuu+/q9ddf1x//+Eft3r1bH374obKzs1VcXKyKigrV1NSosrJSkrRu3Tpt3LhRBw8elGEYKikpkSRt2rRJ6enp8nq9mjFjhoqLi0PVMgBgiEIWJnPmzNGuXbtktVrV2tqqvr4+ffLJJ4qPj9fUqVNltVrl8Xjk9XrV2Niozs5OJSYmSpLS0tLk9XrV09Oj48ePy+Vy9asDAEaXkE5zRUREqKioSG63W/PmzVNzc7NsNltwvd1ul8/nu6xus9nk8/nU1tam6OhoWa3WfnUAwOhiDfUfWLNmjVasWKHHHntM9fX1slgswXWGYchisSgQCHxl/fOfX/Tl5auZODHa3AEAQ2SzxYx0C8CwCVmYnD59Wt3d3brtttt04403yul0yuv1Kjw8PLiN3++X3W5XbGys/H5/sN7S0iK73a4JEyaovb1dfX19Cg8PD24/GK2tHQoEjGt2XPh6vHn25/e3j3QLwKCFhVmG9CE8ZNNcZ86cUW5urrq7u9Xd3a0jR45oyZIlqqurU0NDg/r6+lReXi6Hw6G4uDhFRUWpurpaklRWViaHw6GIiAjdcccdqqiokCSVlpbK4XCEqmUAwBCF7MwkKSlJJ06c0MKFCxUeHi6n0ym3260JEyYoMzNTXV1dSkpKUnJysiSpoKBAubm56ujoUEJCgjIyMiRJeXl5ysrK0vbt2zV58mS98MILoWoZADBEFsMwrus5IKa5ho/NFqPqrY+OdBujwuz1rzDNhTFp1E1zAQC+OQgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADAtpGHy0ksvye12y+12a+vWrZKkDRs2yOl0KjU1VampqTp8+LAkqaqqSh6PR06nU4WFhcHXqK2tVVpamlwul3JyctTb2xvKlgEAQxCyMKmqqtLbb7+tAwcOqLS0VB988IEOHz6smpoavfrqqyorK1NZWZnuvfdedXZ2Kjs7W8XFxaqoqFBNTY0qKyslSevWrdPGjRt18OBBGYahkpKSULUMABiikIWJzWZTVlaWIiMjFRERoWnTpqmpqUlNTU3Kzs6Wx+NRUVGRAoGATpw4ofj4eE2dOlVWq1Uej0der1eNjY3q7OxUYmKiJCktLU1erzdULQMAhsgaqheePn168Pf6+nq9+eab2rNnj44dO6a8vDzFxMRo1apV2r9/v8aNGyebzRbc3m63y+fzqbm5uV/dZrPJ5/OFqmUAwBCFLEw+d+rUKa1atUrr16/Xt7/9bW3bti24bvny5SotLZXL5ZLFYgnWDcOQxWJRIBD4yvpgTJwYbf4ggCGw2WJGugVg2IQ0TKqrq7VmzRplZ2fL7Xbr5MmTqq+vl8vlkvRZOFitVsXGxsrv9wf38/v9stvtl9VbWlpkt9sH1UNra4cCAePaHBCuiDfP/vz+9pFuARi0sDDLkD6Eh+yaydmzZ/XEE0+ooKBAbrdb0mfhsXnzZl24cEE9PT3at2+f7r33Xs2aNUt1dXVqaGhQX1+fysvL5XA4FBcXp6ioKFVXV0uSysrK5HA4QtUyAGCIQnZmsmPHDnV1dSk/Pz9YW7JkiVauXKmlS5eqt7dXTqdTKSkpkqT8/HxlZmaqq6tLSUlJSk5OliQVFBQoNzdXHR0dSkhIUEZGRqhaBgAMkcUwjOt6DohpruFjs8WoeuujI93GqDB7/StMc2FMGnXTXACAbw7CBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMI0wAAKYRJgAA0wgTAIBphAkAwDTCBABgGmECADCNMAEAmEaYAABMC2mYvPTSS3K73XK73dq6daskqaqqSh6PR06nU4WFhcFta2trlZaWJpfLpZycHPX29kqSmpqatGzZMiUnJ2v16tW6ePFiKFsGAAzBgMLE5/NdVvvnP/95xX2qqqr09ttv68CBAyotLdUHH3yg8vJyZWdnq7i4WBUVFaqpqVFlZaUkad26ddq4caMOHjwowzBUUlIiSdq0aZPS09Pl9Xo1Y8YMFRcXD/YYAQAhdsUwOX/+vM6fP68VK1bowoULweWWlhY9+eSTV3xhm82mrKwsRUZGKiIiQtOmTVN9fb3i4+M1depUWa1WeTweeb1eNTY2qrOzU4mJiZKktLQ0eb1e9fT06Pjx43K5XP3qAIDRxXqllU899ZTeeecdSdKdd975n52s1uAb/NeZPn168Pf6+nq9+eabevDBB2Wz2YJ1u90un8+n5ubmfnWbzSafz6e2tjZFR0fLarX2qw/GxInRg9oeuFZstpiRbgEYNlcMkx07dkiSNmzYoC1btgzpD5w6dUqrVq3S+vXrFR4ervr6+uA6wzBksVgUCARksVguq3/+84u+vHw1ra0dCgSMIfWOweHNsz+/v32kWwAGLSzMMqQP4VcMk89t2bJFjY2NunDhggzjP2/MCQkJV9yvurpaa9asUXZ2ttxut44dOya/3x9c7/f7ZbfbFRsb26/e0tIiu92uCRMmqL29XX19fQoPDw9uDwAYXQYUJkVFRdqxY4cmTpwYrFksFh05cuRr9zl79qyeeOIJFRYWat68eZKkWbNmqa6uTg0NDZoyZYrKy8u1aNEixcXFKSoqStXV1Zo9e7bKysrkcDgUERGhO+64QxUVFfJ4PCotLZXD4TB5yACAa21AYVJaWqpDhw5p0qRJA37hHTt2qKurS/n5+cHakiVLlJ+fr8zMTHV1dSkpKUnJycmSpIKCAuXm5qqjo0MJCQnKyMiQJOXl5SkrK0vbt2/X5MmT9cILLwzm+AAAw8BifHHe6mssW7ZMe/bsGY5+rjmumQwfmy1G1VsfHek2RoXZ61/hmgnGpJBeM5k3b562bt2qe+65RzfccEOwfrVrJgCAb4YBhclrr70mSf3u8bjaNRMAwDfHgMLkT3/6U6j7AACMYQMKk507d35l/cc//vE1bQYAMDYNKEz+8Y9/BH/v7u7W8ePHg1/3BQBgwDctfpHP51NOTk5IGgIAjD1DegT9pEmT1NjYeK17AQCMUYO+ZmIYhmpqavrdDQ8A+GYb9DUTSZo8ebLWr18fkoYAAGPPoK6ZNDY2qre3V/Hx8SFtCgAwtgwoTBoaGvT444+rublZgUBAN998s375y19q2rRpoe4PADAGDOgC/DPPPKNHH31Ux48fV3V1tVavXq1NmzaFujcAwBgxoDBpbW3V/fffH1xetGiR2traQtYUAGBsGVCY9PX16fz588Hlc+fOhawhAMDYM6BrJg8++KAeeOAB3XfffbJYLKqoqNBDDz0U6t4AAGPEgM5MkpKSJEk9PT06ffq0fD6f7r333pA2BgAYOwZ0ZpKVlaVly5YpIyNDXV1d2rt3r7Kzs/XrX/861P0BAMaAAZ2ZtLW1Bf+NblRUlB5++GH5/f6QNgYAGDsGfAHe5/MFl1taWjSA//YLAPiGGNA018MPP6yFCxfq7rvvlsViUVVVFY9TAQAEDejMZPHixdq5c6e++93vasaMGdqxY4c8Hs+A/kBHR4dSUlJ05swZSdKGDRvkdDqVmpqq1NRUHT58WJJUVVUlj8cjp9OpwsLC4P61tbVKS0uTy+VSTk6Oent7B3uMAIAQG9CZiSTdeuutuvXWWwf14u+//75yc3NVX18frNXU1OjVV1+V3W4P1jo7O5Wdna3du3dr8uTJWrVqlSorK5WUlKR169bp2WefVWJiorKzs1VSUqL09PRB9QEACK0h/T+TgSopKVFeXl4wOC5duqSmpiZlZ2fL4/GoqKhIgUBAJ06cUHx8vKZOnSqr1SqPxyOv16vGxkZ1dnYqMTFRkpSWliav1xvKlgEAQzDgM5OheO655/ott7S0aO7cucrLy1NMTIxWrVql/fv3a9y4cbLZbMHt7Ha7fD6fmpub+9VtNlu/LwIAAEaHkIbJl02dOlXbtm0LLi9fvlylpaVyuVyyWCzBumEYslgsCgQCX1kfjIkTo803DgyBzRYz0i0Aw2ZYw+TkyZOqr6+Xy+WS9Fk4WK1WxcbG9rtvxe/3y263X1ZvaWnpd61lIFpbOxQI8DXm4cCbZ39+f/tItwAMWliYZUgfwkN6zeTLDMPQ5s2bdeHCBfX09Gjfvn269957NWvWLNXV1amhoUF9fX0qLy+Xw+FQXFycoqKiVF1dLUkqKyuTw+EYzpYBAAMwrGcmt956q1auXKmlS5eqt7dXTqdTKSkpkqT8/HxlZmaqq6tLSUlJSk5OliQVFBQoNzdXHR0dSkhICN6JDwAYPSzGdX4rO9Ncw8dmi1H11kdHuo1RYfb6V5jmwpg0Jqa5AADXJ8IEAGAaYQIAMI0wAQCYRpgAAEwjTAAAphEmAADTCBMAgGmECQDANMIEAGAaYQIAMI0wAQCYRpgAAEwjTAAAphEmAADTCBMAgGmECQDANMIEAGAaYQIAMI0wAQCYRpgAAEwLaZh0dHQoJSVFZ86ckSRVVVXJ4/HI6XSqsLAwuF1tba3S0tLkcrmUk5Oj3t5eSVJTU5OWLVum5ORkrV69WhcvXgxluwCAIQpZmLz//vtaunSp6uvrJUmdnZ3Kzs5WcXGxKioqVFNTo8rKSknSunXrtHHjRh08eFCGYaikpESStGnTJqWnp8vr9WrGjBkqLi4OVbsAABNCFiYlJSXKy8uT3W6XJJ04cULx8fGaOnWqrFarPB6PvF6vGhsb1dnZqcTERElSWlqavF6venp6dPz4cblcrn51AMDoYw3VCz/33HP9lpubm2Wz2YLLdrtdPp/vsrrNZpPP51NbW5uio6NltVr71QEAo0/IwuTLAoGALBZLcNkwDFkslq+tf/7zi768PBATJ0YPvWnABJstZqRbAIbNsIVJbGys/H5/cNnv98tut19Wb2lpkd1u14QJE9Te3q6+vj6Fh4cHtx+s1tYOBQLGNTkGXBlvnv35/e0j3QIwaGFhliF9CB+2rwbPmjVLdXV1amhoUF9fn8rLy+VwOBQXF6eoqChVV1dLksrKyuRwOBQREaE77rhDFRUVkqTS0lI5HI7hahcAMAjDdmYSFRWl/Px8ZWZmqqurS0lJSUpOTpYkFRQUKDc3Vx0dHUpISFBGRoYkKS8vT1lZWdq+fbsmT56sF154YbjaBQAMgsUwjOt6DohpruFjs8WoeuujI93GqDB7/StMc2FMGvXTXACA6xdhAgAwjTABAJhGmAAATCNMAACmESYAANMIEwCAaYQJAMA0wgQAYBphAgAwjTABAJhGmAAATCNMAACmESYAANMIEwCAaYQJAMA0wgQAYBphAgAwjTABAJhGmAAATCNMAACmWUfijy5fvlznzp2T1frZn3/mmWf0r3/9S9u3b1dvb68eeughLVu2TJJUVVWlLVu2qKurS/fdd5/Wrl07Ei0DAK5g2MPEMAzV19frrbfeCoaJz+fT2rVr9dprrykyMlJLlizRnXfeqSlTpig7O1u7d+/W5MmTtWrVKlVWViopKWm42wYAXMGwh8lHH30kSXrkkUd0/vx5/ehHP9JNN92kuXPnavz48ZIkl8slr9erOXPmKD4+XlOnTpUkeTweeb1ewgQARplhv2byySefaN68edq2bZt++9vf6ve//72amppks9mC29jtdvl8PjU3N39lHQAwugz7mcntt9+u22+/Pbi8ePFibdmyRatXrw7WDMOQxWJRIBCQxWK5rD4YEydGm28aGAKbLWakWwCGzbCHyXvvvaeenh7NmzdP0mcBERcXJ7/fH9zG7/fLbrcrNjb2K+uD0draoUDAuDbN44p48+zP728f6RaAQQsLswzpQ/iwT3O1t7dr69at6urqUkdHhw4cOKDnn39eR48e1blz53Tp0iUdOnRIDodDs2bNUl1dnRoaGtTX16fy8nI5HI7hbhkAcBXDfmYyf/58vf/++1q4cKECgYDS09M1e/ZsrV27VhkZGerp6dHixYs1c+ZMSVJ+fr4yMzPV1dWlpKQkJScnD3fLAICrsBiGcV3PATHNNXxsthhVb310pNsYFWavf4VpLoxJY2aaCwBw/SFMAACmESYAANMIEwCAaYQJAMA0wgQAYBphAgAwjTABAJhGmAAATCNMAACmESYAANMIEwCAaYQJAMC0YX8E/WgT81836IaoiJFuY1To7OpR+yedI90GgDHoGx8mN0RFKH39npFuY1T4f1uXqV2ECYDBY5oLAGAaYQIAMI0wAQCYRpgAAEz7xl+AB0ar//rvKEVFRo50G6NCV3e3PrnQNdJt4AoIE2CUioqM1MM7fzrSbYwKv/3xi5IIk9FsTExzvfHGG1qwYIGcTqf27OFrvAAw2oz6MxOfz6fCwkK99tprioyM1JIlS3TnnXfqlltuGenWAAD/a9SHSVVVlebOnavx48dLklwul7xer5588skB7R8WZrnqNv9z802meryeDGS8riTyvyZeo07GPrNjKUn/Ez3hGnRyfbgW44mrG+o4j/owaW5uls1mCy7b7XadOHFiwPvfPICgKNqwcEi9XY8mTow2tf//eez/XqNOxj6zYylJBT/MuwadXB+uxXgidEb9NZNAICCL5T9JaRhGv2UAwMgb9WESGxsrv98fXPb7/bLb7SPYEQDgy0Z9mNx11106evSozp07p0uXLunQoUNyOBwj3RYA4AtG/TWTSZMmae3atcrIyFBPT48WL16smTNnjnRbAIAvsBiGYYx0EwCAsW3UT3MBAEY/wgQAYBphAgAwjTABAJhGmAyjqz2wsra2VmlpaXK5XMrJyVFvb+8IdDl2dHR0KCUlRWfOnLlsHWM5OC+99JLcbrfcbre2bt162XrGc3BefPFFLViwQG63Wzt37rxs/XU5ngaGxccff2zMnz/faGtrMy5evGh4PB7j1KlT/bZxu93GX//6V8MwDGPDhg3Gnj17RqLVMeFvf/ubkZKSYiQkJBj//ve/L1vPWA7cO++8YzzwwANGV1eX0d3dbWRkZBiHDh3qtw3jOXB//vOfjSVLlhg9PT3GpUuXjPnz5xunT5/ut831OJ6cmQyTLz6wcty4ccEHVn6usbFRnZ2dSkxMlCSlpaX1W4/+SkpKlJeX95VPQ2AsB8dmsykrK0uRkZGKiIjQtGnT1NTUFFzPeA7OnDlztGvXLlmtVrW2tqqvr0/jxo0Lrr9ex3PU37R4vbjaAyu/vN5ms8nn8w1rj2PJc88997XrGMvBmT59evD3+vp6vfnmm9q7d2+wxngOXkREhIqKivSb3/xGycnJmjRpUnDd9TqenJkMk6s9sJIHWl47jOXQnDp1So888ojWr1+vb33rW8E64zk0a9as0dGjR3X27FmVlJQE69freBImw+RqD6z88vqWlhYeaDlEjOXgVVdX6+GHH9ZTTz2l+++/v986xnNwTp8+rdraWknSjTfeKKfTqZMnTwbXX6/jSZgMk6s9sDIuLk5RUVGqrq6WJJWVlfFAyyFiLAfn7NmzeuKJJ1RQUCC3233ZesZzcM6cOaPc3Fx1d3eru7tbR44c0ezZs4Prr9fxJEyGyRcfWLlw4UKlpKRo5syZWrFihf7+979LkgoKCrRlyxYlJyfr008/VUZGxgh3PbYwlkOzY8cOdXV1KT8/X6mpqUpNTdXevXsZzyFKSkrS97//fS1cuFCLFi3S7bffLrfbfd2PJw96BACYxpkJAMA0wgQAYBphAgAwjTABAJhGmAAATONxKoBJfX192rVrl9544w319fWpp6dH8+fP109/+lNt3LhR06dP109+8pORbhMIKcIEMOnpp5/WhQsX9Lvf/U4xMTH69NNP9fOf/1w5OTkKDw8f6faAYUGYACacOXNGb7zxht5++21FR0dLksaNG6dNmzbpL3/5i956663gtvv379e+ffvU09OjCxcuaMWKFUpPT5ff79cvfvELtbW1Sfrspref/exnX1sHRiOumQAmfPDBB7rllluCQfI5m80ml8sVXL548aL+8Ic/6Fe/+pVKS0tVWFio559/XtJnj9OfMmWKDhw4oD179qihoUHt7e1fWwdGI85MABPCwsIUCASuut1NN92kl19+WZWVlaqvr9eHH36oTz/9VJJ09913a+XKlTp79qzuuusuPfXUU4qJifnaOjAacWYCmDBz5kx99NFH6ujo6Ff3+XxauXKlOjs7JUkff/yxFi5cqMbGRs2ePbvfdNXMmTN15MgRPfDAA2psbNQPf/hD1dTUfG0dGI04MwFMmDRpkjwej7Kzs7V582ZFR0ero6NDTz/9tMaPH6+wsM8+r9XU1GjChAl6/PHHJUkvv/yypM++CVZYWCjDMLRu3Trdc889OnnypE6dOiWv1/uV9RkzZozY8QJfhwc9Aib19vaquLhYhw4dUnh4uLq7u/WDH/xAmZmZwa8Gp6ena+3ataqrq5PFYtGcOXN0+PBh7dmzRzExMcrKypLP51NkZKS+853vaNOmTbpw4cJX1iMjI0f6kIHLECYAANO4ZgIAMI0wAQCYRpgAAEwjTAAAphEmAADTCBMAgGmECQDANMIEAGDa/weqXGeeSp0vVgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# let's see how data is distributed for every column for every individual id\n",
"# the graph plot below is for individual ids. Press 1 in the input box below the graph to continue viewing graph for each id!!\n",
"\n",
"# plot.figure(figsize=(20,25), facecolor='white')\n",
"# plotnumber = 1\n",
"# plt_data = data.drop(['age'], axis =1)\n",
"\n",
"# for column in plt_data:\n",
"# ax = plot.subplot(6,5,plotnumber)\n",
"# sns.countplot(plt_data[column])\n",
"# plot.xlabel(column,fontsize=10)\n",
"# plotnumber+=1\n",
"# plot.show()\n",
"\n",
"\n",
"sns.countplot(new_data['Class'])\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can clerly see that the dataset is highly imbalanced. \n",
"\n",
"We will use a python library known as imbalanced-learn to deal with imbalanced data.\n",
"Imbalanced learn has an algorithm called RandomOverSampler. We will use different techniques in another projects. \n",
"You can study more about different techniques below.\n",
"\n",
"Note: https://pypi.org/project/imbalanced-learn/\n",
"\n",
"https://github.com/scikit-learn-contrib/imbalanced-learn\n",
"\n",
"\n",
"Also, ensemble techniques are well versed in handling such imbalanced data. But for the sake of learning we will see how such issues are dealt with."
]
},
{
"cell_type": "code",
"execution_count": 474,
"metadata": {},
"outputs": [],
"source": [
"# cat = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
"# sm = SMOTENC(categorical_features = cat,sampling_strategy='minority',k_neighbors=3)"
]
},
{
"cell_type": "code",
"execution_count": 422,
"metadata": {},
"outputs": [],
"source": [
"# kmsmote=KMeansSMOTE()"
]
},
{
"cell_type": "code",
"execution_count": 475,
"metadata": {},
"outputs": [],
"source": [
"x = new_data.drop(['Class'],axis=1)\n",
"y = new_data['Class']\n",
"rdsmple = RandomOverSampler()\n",
"x_sampled,y_sampled = rdsmple.fit_sample(x,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# x_sampled,y_sampled = kmsmote.fit_sample(x,np.asarray(y))"
]
},
{
"cell_type": "code",
"execution_count": 476,
"metadata": {},
"outputs": [],
"source": [
"rdsmple = RandomOverSampler()\n",
"x_sampled,y_sampled = rdsmple.fit_sample(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 477,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13924, 25)"
]
},
"execution_count": 477,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_sampled.shape"
]
},
{
"cell_type": "code",
"execution_count": 478,
"metadata": {},
"outputs": [],
"source": [
"x_sampled = pd.DataFrame(data = x_sampled, columns = x.columns)"
]
},
{
"cell_type": "code",
"execution_count": 479,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>...</th>\n",
" <th>psych</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>referral_source_STMW</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
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"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication \\\n",
"0 42.0 0.0 0.0 0.0 0.0 \n",
"1 24.0 0.0 0.0 0.0 0.0 \n",
"2 47.0 1.0 0.0 0.0 0.0 \n",
"3 71.0 0.0 1.0 0.0 0.0 \n",
"4 71.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... \n",
"13919 47.0 0.0 0.0 0.0 0.0 \n",
"13920 47.0 0.0 0.0 0.0 0.0 \n",
"13921 47.0 0.0 0.0 0.0 0.0 \n",
"13922 42.0 1.0 0.0 0.0 0.0 \n",
"13923 42.0 1.0 0.0 0.0 0.0 \n",
"\n",
" sick pregnant thyroid_surgery I131_treatment query_hypothyroid \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... \n",
"13919 0.0 0.0 0.0 0.0 0.0 \n",
"13920 0.0 0.0 0.0 0.0 0.0 \n",
"13921 0.0 0.0 0.0 0.0 0.0 \n",
"13922 0.0 0.0 0.0 0.0 1.0 \n",
"13923 0.0 0.0 0.0 0.0 1.0 \n",
"\n",
" ... psych T3 TT4 T4U FTI referral_source_STMW \\\n",
"0 ... 0.0 3.0 126.0 2.0 110.0 0.0 \n",
"1 ... 0.0 3.0 103.0 2.0 109.0 0.0 \n",
"2 ... 0.0 3.0 110.0 2.0 121.0 0.0 \n",
"3 ... 0.0 3.0 176.0 2.0 178.0 0.0 \n",
"4 ... 0.0 2.0 62.0 2.0 71.0 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"13919 ... 0.0 2.0 49.0 2.0 55.0 0.0 \n",
"13920 ... 0.0 2.0 49.0 2.0 55.0 0.0 \n",
"13921 ... 0.0 2.0 49.0 2.0 55.0 0.0 \n",
"13922 ... 0.0 3.0 23.0 2.0 58.0 0.0 \n",
"13923 ... 0.0 3.0 23.0 2.0 58.0 0.0 \n",
"\n",
" referral_source_SVHC referral_source_SVHD referral_source_SVI \\\n",
"0 1.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 1.0 \n",
"... ... ... ... \n",
"13919 0.0 0.0 0.0 \n",
"13920 0.0 0.0 0.0 \n",
"13921 0.0 0.0 0.0 \n",
"13922 0.0 0.0 0.0 \n",
"13923 0.0 0.0 0.0 \n",
"\n",
" referral_source_other \n",
"0 0.0 \n",
"1 1.0 \n",
"2 1.0 \n",
"3 1.0 \n",
"4 0.0 \n",
"... ... \n",
"13919 1.0 \n",
"13920 1.0 \n",
"13921 1.0 \n",
"13922 1.0 \n",
"13923 1.0 \n",
"\n",
"[13924 rows x 25 columns]"
]
},
"execution_count": 479,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_sampled"
]
},
{
"cell_type": "code",
"execution_count": 480,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1de9f6b6188>"
]
},
"execution_count": 480,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(y_sampled) \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Our dataset looks balanced now. We can go ahead with training our model on this data."
]
},
{
"cell_type": "code",
"execution_count": null,
"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": "3.7.3"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}