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Lung_cancer_prediction.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 313,
"id": "8e059267",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"plt.rcParams['figure.figsize']=(11,10)\n",
"sns.set_style('whitegrid')\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 314,
"id": "8d7d9751",
"metadata": {},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GENDER</th>\n",
" <th>AGE</th>\n",
" <th>SMOKING</th>\n",
" <th>YELLOW_FINGERS</th>\n",
" <th>ANXIETY</th>\n",
" <th>PEER_PRESSURE</th>\n",
" <th>CHRONIC DISEASE</th>\n",
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" <th>SHORTNESS OF BREATH</th>\n",
" <th>SWALLOWING DIFFICULTY</th>\n",
" <th>CHEST PAIN</th>\n",
" <th>LUNG_CANCER</th>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>63</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NO</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"0 M 69 1 2 2 1 \n",
"1 M 74 2 1 1 1 \n",
"2 F 59 1 1 1 2 \n",
"3 M 63 2 2 2 1 \n",
"4 F 63 1 2 1 1 \n",
"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING \\\n",
"0 1 2 1 2 2 2 \n",
"1 2 2 2 1 1 1 \n",
"2 1 2 1 2 1 2 \n",
"3 1 1 1 1 2 1 \n",
"4 1 1 1 2 1 2 \n",
"\n",
" SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER \n",
"0 2 2 2 YES \n",
"1 2 2 2 YES \n",
"2 2 1 2 NO \n",
"3 1 2 2 NO \n",
"4 2 1 1 NO "
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lc=pd.read_csv('lungcancerdata.csv')\n",
"lc.head()"
]
},
{
"cell_type": "code",
"execution_count": 315,
"id": "92ddc2b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 309 entries, 0 to 308\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 GENDER 309 non-null object\n",
" 1 AGE 309 non-null int64 \n",
" 2 SMOKING 309 non-null int64 \n",
" 3 YELLOW_FINGERS 309 non-null int64 \n",
" 4 ANXIETY 309 non-null int64 \n",
" 5 PEER_PRESSURE 309 non-null int64 \n",
" 6 CHRONIC DISEASE 309 non-null int64 \n",
" 7 FATIGUE 309 non-null int64 \n",
" 8 ALLERGY 309 non-null int64 \n",
" 9 WHEEZING 309 non-null int64 \n",
" 10 ALCOHOL CONSUMING 309 non-null int64 \n",
" 11 COUGHING 309 non-null int64 \n",
" 12 SHORTNESS OF BREATH 309 non-null int64 \n",
" 13 SWALLOWING DIFFICULTY 309 non-null int64 \n",
" 14 CHEST PAIN 309 non-null int64 \n",
" 15 LUNG_CANCER 309 non-null object\n",
"dtypes: int64(14), object(2)\n",
"memory usage: 38.8+ KB\n"
]
},
{
"data": {
"text/plain": [
"((309, 16), None)"
]
},
"execution_count": 315,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lc.shape,lc.info()"
]
},
{
"cell_type": "code",
"execution_count": 316,
"id": "28517ed8",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AGE</th>\n",
" <th>SMOKING</th>\n",
" <th>YELLOW_FINGERS</th>\n",
" <th>ANXIETY</th>\n",
" <th>PEER_PRESSURE</th>\n",
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" <th>CHEST PAIN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>309.000000</td>\n",
" <td>309.000000</td>\n",
" <td>309.000000</td>\n",
" <td>309.000000</td>\n",
" <td>309.000000</td>\n",
" <td>309.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>62.673139</td>\n",
" <td>1.563107</td>\n",
" <td>1.569579</td>\n",
" <td>1.498382</td>\n",
" <td>1.501618</td>\n",
" <td>1.504854</td>\n",
" <td>1.673139</td>\n",
" <td>1.556634</td>\n",
" <td>1.556634</td>\n",
" <td>1.556634</td>\n",
" <td>1.579288</td>\n",
" <td>1.640777</td>\n",
" <td>1.469256</td>\n",
" <td>1.556634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.210301</td>\n",
" <td>0.496806</td>\n",
" <td>0.495938</td>\n",
" <td>0.500808</td>\n",
" <td>0.500808</td>\n",
" <td>0.500787</td>\n",
" <td>0.469827</td>\n",
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" <td>0.497588</td>\n",
" <td>0.497588</td>\n",
" <td>0.494474</td>\n",
" <td>0.480551</td>\n",
" <td>0.499863</td>\n",
" <td>0.497588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>21.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <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",
" <tr>\n",
" <th>25%</th>\n",
" <td>57.000000</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>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>62.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>69.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>87.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"count 309.000000 309.000000 309.000000 309.000000 309.000000 \n",
"mean 62.673139 1.563107 1.569579 1.498382 1.501618 \n",
"std 8.210301 0.496806 0.495938 0.500808 0.500808 \n",
"min 21.000000 1.000000 1.000000 1.000000 1.000000 \n",
"25% 57.000000 1.000000 1.000000 1.000000 1.000000 \n",
"50% 62.000000 2.000000 2.000000 1.000000 2.000000 \n",
"75% 69.000000 2.000000 2.000000 2.000000 2.000000 \n",
"max 87.000000 2.000000 2.000000 2.000000 2.000000 \n",
"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n",
"count 309.000000 309.000000 309.000000 309.000000 309.000000 \n",
"mean 1.504854 1.673139 1.556634 1.556634 1.556634 \n",
"std 0.500787 0.469827 0.497588 0.497588 0.497588 \n",
"min 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"25% 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"50% 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"75% 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"max 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"\n",
" COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \n",
"count 309.000000 309.000000 309.000000 309.000000 \n",
"mean 1.579288 1.640777 1.469256 1.556634 \n",
"std 0.494474 0.480551 0.499863 0.497588 \n",
"min 1.000000 1.000000 1.000000 1.000000 \n",
"25% 1.000000 1.000000 1.000000 1.000000 \n",
"50% 2.000000 2.000000 1.000000 2.000000 \n",
"75% 2.000000 2.000000 2.000000 2.000000 \n",
"max 2.000000 2.000000 2.000000 2.000000 "
]
},
"execution_count": 316,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lc.describe()"
]
},
{
"cell_type": "code",
"execution_count": 317,
"id": "d83e398b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GENDER 0\n",
"AGE 0\n",
"SMOKING 0\n",
"YELLOW_FINGERS 0\n",
"ANXIETY 0\n",
"PEER_PRESSURE 0\n",
"CHRONIC DISEASE 0\n",
"FATIGUE 0\n",
"ALLERGY 0\n",
"WHEEZING 0\n",
"ALCOHOL CONSUMING 0\n",
"COUGHING 0\n",
"SHORTNESS OF BREATH 0\n",
"SWALLOWING DIFFICULTY 0\n",
"CHEST PAIN 0\n",
"LUNG_CANCER 0\n",
"dtype: int64"
]
},
"execution_count": 317,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lc.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 318,
"id": "d5b2228a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33"
]
},
"execution_count": 318,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"lc.duplicated().sum()"
]
},
{
"cell_type": "code",
"execution_count": 319,
"id": "37ca933d",
"metadata": {},
"outputs": [],
"source": [
"lcc=lc.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 320,
"id": "52fde2ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
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},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lcc.duplicated().sum()"
]
},
{
"cell_type": "code",
"execution_count": 321,
"id": "6963f671",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GENDER 2\n",
"AGE 39\n",
"SMOKING 2\n",
"YELLOW_FINGERS 2\n",
"ANXIETY 2\n",
"PEER_PRESSURE 2\n",
"CHRONIC DISEASE 2\n",
"FATIGUE 2\n",
"ALLERGY 2\n",
"WHEEZING 2\n",
"ALCOHOL CONSUMING 2\n",
"COUGHING 2\n",
"SHORTNESS OF BREATH 2\n",
"SWALLOWING DIFFICULTY 2\n",
"CHEST PAIN 2\n",
"LUNG_CANCER 2\n",
"dtype: int64"
]
},
"execution_count": 321,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lcc.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 322,
"id": "4600092e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"YES 238\n",
"NO 38\n",
"Name: LUNG_CANCER, dtype: int64"
]
},
"execution_count": 322,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lcc.LUNG_CANCER.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 323,
"id": "221b9ce6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(lcc['LUNG_CANCER'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 324,
"id": "6cae4908",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(lcc['LUNG_CANCER'],lcc['AGE'], )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 325,
"id": "654fe9cd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(lcc['AGE'] )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61536412",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 326,
"id": "3794a06d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<AxesSubplot:title={'center':'AGE'}>,\n",
" <AxesSubplot:title={'center':'SMOKING'}>,\n",
" <AxesSubplot:title={'center':'YELLOW_FINGERS'}>,\n",
" <AxesSubplot:title={'center':'ANXIETY'}>],\n",
" [<AxesSubplot:title={'center':'PEER_PRESSURE'}>,\n",
" <AxesSubplot:title={'center':'CHRONIC DISEASE'}>,\n",
" <AxesSubplot:title={'center':'FATIGUE '}>,\n",
" <AxesSubplot:title={'center':'ALLERGY '}>],\n",
" [<AxesSubplot:title={'center':'WHEEZING'}>,\n",
" <AxesSubplot:title={'center':'ALCOHOL CONSUMING'}>,\n",
" <AxesSubplot:title={'center':'COUGHING'}>,\n",
" <AxesSubplot:title={'center':'SHORTNESS OF BREATH'}>],\n",
" [<AxesSubplot:title={'center':'SWALLOWING DIFFICULTY'}>,\n",
" <AxesSubplot:title={'center':'CHEST PAIN'}>, <AxesSubplot:>,\n",
" <AxesSubplot:>]], dtype=object)"
]
},
"execution_count": 326,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 1728x1728 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 792x720 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(24,24))\n",
"lcc.hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6959a83",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 327,
"id": "b6bdc17a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(lcc['LUNG_CANCER'],lcc['AGE'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 328,
"id": "39600bd4",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"le=LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 329,
"id": "16e71197",
"metadata": {},
"outputs": [],
"source": [
"lcc['LUNG_CANCER']=le.fit_transform(lcc['LUNG_CANCER'])\n",
"lcc['GENDER']=le.fit_transform(lcc['GENDER'])"
]
},
{
"cell_type": "code",
"execution_count": 330,
"id": "8588f54a",
"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>GENDER</th>\n",
" <th>AGE</th>\n",
" <th>SMOKING</th>\n",
" <th>YELLOW_FINGERS</th>\n",
" <th>ANXIETY</th>\n",
" <th>PEER_PRESSURE</th>\n",
" <th>CHRONIC DISEASE</th>\n",
" <th>FATIGUE</th>\n",
" <th>ALLERGY</th>\n",
" <th>WHEEZING</th>\n",
" <th>ALCOHOL CONSUMING</th>\n",
" <th>COUGHING</th>\n",
" <th>SHORTNESS OF BREATH</th>\n",
" <th>SWALLOWING DIFFICULTY</th>\n",
" <th>CHEST PAIN</th>\n",
" <th>LUNG_CANCER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>69</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>74</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>59</td>\n",
" <td>1</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>63</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>63</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"0 1 69 1 2 2 1 \n",
"1 1 74 2 1 1 1 \n",
"2 0 59 1 1 1 2 \n",
"3 1 63 2 2 2 1 \n",
"4 0 63 1 2 1 1 \n",
"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING \\\n",
"0 1 2 1 2 2 2 \n",
"1 2 2 2 1 1 1 \n",
"2 1 2 1 2 1 2 \n",
"3 1 1 1 1 2 1 \n",
"4 1 1 1 2 1 2 \n",
"\n",
" SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER \n",
"0 2 2 2 1 \n",
"1 2 2 2 1 \n",
"2 2 1 2 0 \n",
"3 1 2 2 0 \n",
"4 2 1 1 0 "
]
},
"execution_count": 330,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lcc.head()"
]
},
{
"cell_type": "code",
"execution_count": 351,
"id": "746913ac",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GENDER</th>\n",
" <th>AGE</th>\n",
" <th>SMOKING</th>\n",
" <th>YELLOW_FINGERS</th>\n",
" <th>ANXIETY</th>\n",
" <th>PEER_PRESSURE</th>\n",
" <th>CHRONIC DISEASE</th>\n",
" <th>FATIGUE</th>\n",
" <th>ALLERGY</th>\n",
" <th>WHEEZING</th>\n",
" <th>ALCOHOL CONSUMING</th>\n",
" <th>COUGHING</th>\n",
" <th>SHORTNESS OF BREATH</th>\n",
" <th>SWALLOWING DIFFICULTY</th>\n",
" <th>CHEST PAIN</th>\n",
" <th>LUNG_CANCER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GENDER</th>\n",
" <td>1.000000</td>\n",
" <td>-0.013120</td>\n",
" <td>0.041131</td>\n",
" <td>-0.202506</td>\n",
" <td>-0.152032</td>\n",
" <td>-0.261427</td>\n",
" <td>-0.189925</td>\n",
" <td>-0.079020</td>\n",
" <td>0.150174</td>\n",
" <td>0.121047</td>\n",
" <td>0.434264</td>\n",
" <td>0.120228</td>\n",
" <td>-0.052893</td>\n",
" <td>-0.048959</td>\n",
" <td>0.361547</td>\n",
" <td>0.053666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AGE</th>\n",
" <td>-0.013120</td>\n",
" <td>1.000000</td>\n",
" <td>-0.073410</td>\n",
" <td>0.025773</td>\n",
" <td>0.050605</td>\n",
" <td>0.037848</td>\n",
" <td>-0.003431</td>\n",
" <td>0.021606</td>\n",
" <td>0.037139</td>\n",
" <td>0.052803</td>\n",
" <td>0.052049</td>\n",
" <td>0.168654</td>\n",
" <td>-0.009189</td>\n",
" <td>0.003199</td>\n",
" <td>-0.035806</td>\n",
" <td>0.106305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SMOKING</th>\n",
" <td>0.041131</td>\n",
" <td>-0.073410</td>\n",
" <td>1.000000</td>\n",
" <td>-0.020799</td>\n",
" <td>0.153389</td>\n",
" <td>-0.030364</td>\n",
" <td>-0.149415</td>\n",
" <td>-0.037803</td>\n",
" <td>-0.030179</td>\n",
" <td>-0.147081</td>\n",
" <td>-0.052771</td>\n",
" <td>-0.138553</td>\n",
" <td>0.051761</td>\n",
" <td>0.042152</td>\n",
" <td>0.106984</td>\n",
" <td>0.034878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YELLOW_FINGERS</th>\n",
" <td>-0.202506</td>\n",
" <td>0.025773</td>\n",
" <td>-0.020799</td>\n",
" <td>1.000000</td>\n",
" <td>0.558344</td>\n",
" <td>0.313067</td>\n",
" <td>0.015316</td>\n",
" <td>-0.099644</td>\n",
" <td>-0.147130</td>\n",
" <td>-0.058756</td>\n",
" <td>-0.273643</td>\n",
" <td>0.020803</td>\n",
" <td>-0.109959</td>\n",
" <td>0.333349</td>\n",
" <td>-0.099169</td>\n",
" <td>0.189192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANXIETY</th>\n",
" <td>-0.152032</td>\n",
" <td>0.050605</td>\n",
" <td>0.153389</td>\n",
" <td>0.558344</td>\n",
" <td>1.000000</td>\n",
" <td>0.210278</td>\n",
" <td>-0.006938</td>\n",
" <td>-0.181474</td>\n",
" <td>-0.159451</td>\n",
" <td>-0.174009</td>\n",
" <td>-0.152228</td>\n",
" <td>-0.218843</td>\n",
" <td>-0.155678</td>\n",
" <td>0.478820</td>\n",
" <td>-0.123182</td>\n",
" <td>0.144322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PEER_PRESSURE</th>\n",
" <td>-0.261427</td>\n",
" <td>0.037848</td>\n",
" <td>-0.030364</td>\n",
" <td>0.313067</td>\n",
" <td>0.210278</td>\n",
" <td>1.000000</td>\n",
" <td>0.042893</td>\n",
" <td>0.094661</td>\n",
" <td>-0.066887</td>\n",
" <td>-0.037769</td>\n",
" <td>-0.132603</td>\n",
" <td>-0.068224</td>\n",
" <td>-0.214115</td>\n",
" <td>0.327764</td>\n",
" <td>-0.074655</td>\n",
" <td>0.195086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHRONIC DISEASE</th>\n",
" <td>-0.189925</td>\n",
" <td>-0.003431</td>\n",
" <td>-0.149415</td>\n",
" <td>0.015316</td>\n",
" <td>-0.006938</td>\n",
" <td>0.042893</td>\n",
" <td>1.000000</td>\n",
" <td>-0.099411</td>\n",
" <td>0.134309</td>\n",
" <td>-0.040546</td>\n",
" <td>0.010144</td>\n",
" <td>-0.160813</td>\n",
" <td>-0.011760</td>\n",
" <td>0.068263</td>\n",
" <td>-0.048895</td>\n",
" <td>0.143692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FATIGUE</th>\n",
" <td>-0.079020</td>\n",
" <td>0.021606</td>\n",
" <td>-0.037803</td>\n",
" <td>-0.099644</td>\n",
" <td>-0.181474</td>\n",
" <td>0.094661</td>\n",
" <td>-0.099411</td>\n",
" <td>1.000000</td>\n",
" <td>-0.001841</td>\n",
" <td>0.152151</td>\n",
" <td>-0.181573</td>\n",
" <td>0.148538</td>\n",
" <td>0.407027</td>\n",
" <td>-0.115727</td>\n",
" <td>0.013757</td>\n",
" <td>0.160078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALLERGY</th>\n",
" <td>0.150174</td>\n",
" <td>0.037139</td>\n",
" <td>-0.030179</td>\n",
" <td>-0.147130</td>\n",
" <td>-0.159451</td>\n",
" <td>-0.066887</td>\n",
" <td>0.134309</td>\n",
" <td>-0.001841</td>\n",
" <td>1.000000</td>\n",
" <td>0.166517</td>\n",
" <td>0.378125</td>\n",
" <td>0.206367</td>\n",
" <td>-0.018030</td>\n",
" <td>-0.037581</td>\n",
" <td>0.245440</td>\n",
" <td>0.333552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WHEEZING</th>\n",
" <td>0.121047</td>\n",
" <td>0.052803</td>\n",
" <td>-0.147081</td>\n",
" <td>-0.058756</td>\n",
" <td>-0.174009</td>\n",
" <td>-0.037769</td>\n",
" <td>-0.040546</td>\n",
" <td>0.152151</td>\n",
" <td>0.166517</td>\n",
" <td>1.000000</td>\n",
" <td>0.261061</td>\n",
" <td>0.353657</td>\n",
" <td>0.042289</td>\n",
" <td>0.108304</td>\n",
" <td>0.142846</td>\n",
" <td>0.249054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALCOHOL CONSUMING</th>\n",
" <td>0.434264</td>\n",
" <td>0.052049</td>\n",
" <td>-0.052771</td>\n",
" <td>-0.273643</td>\n",
" <td>-0.152228</td>\n",
" <td>-0.132603</td>\n",
" <td>0.010144</td>\n",
" <td>-0.181573</td>\n",
" <td>0.378125</td>\n",
" <td>0.261061</td>\n",
" <td>1.000000</td>\n",
" <td>0.198023</td>\n",
" <td>-0.163370</td>\n",
" <td>-0.000635</td>\n",
" <td>0.310767</td>\n",
" <td>0.294422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUGHING</th>\n",
" <td>0.120228</td>\n",
" <td>0.168654</td>\n",
" <td>-0.138553</td>\n",
" <td>0.020803</td>\n",
" <td>-0.218843</td>\n",
" <td>-0.068224</td>\n",
" <td>-0.160813</td>\n",
" <td>0.148538</td>\n",
" <td>0.206367</td>\n",
" <td>0.353657</td>\n",
" <td>0.198023</td>\n",
" <td>1.000000</td>\n",
" <td>0.284968</td>\n",
" <td>-0.136885</td>\n",
" <td>0.077988</td>\n",
" <td>0.253027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SHORTNESS OF BREATH</th>\n",
" <td>-0.052893</td>\n",
" <td>-0.009189</td>\n",
" <td>0.051761</td>\n",
" <td>-0.109959</td>\n",
" <td>-0.155678</td>\n",
" <td>-0.214115</td>\n",
" <td>-0.011760</td>\n",
" <td>0.407027</td>\n",
" <td>-0.018030</td>\n",
" <td>0.042289</td>\n",
" <td>-0.163370</td>\n",
" <td>0.284968</td>\n",
" <td>1.000000</td>\n",
" <td>-0.140307</td>\n",
" <td>0.044029</td>\n",
" <td>0.064407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SWALLOWING DIFFICULTY</th>\n",
" <td>-0.048959</td>\n",
" <td>0.003199</td>\n",
" <td>0.042152</td>\n",
" <td>0.333349</td>\n",
" <td>0.478820</td>\n",
" <td>0.327764</td>\n",
" <td>0.068263</td>\n",
" <td>-0.115727</td>\n",
" <td>-0.037581</td>\n",
" <td>0.108304</td>\n",
" <td>-0.000635</td>\n",
" <td>-0.136885</td>\n",
" <td>-0.140307</td>\n",
" <td>1.000000</td>\n",
" <td>0.102674</td>\n",
" <td>0.268940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHEST PAIN</th>\n",
" <td>0.361547</td>\n",
" <td>-0.035806</td>\n",
" <td>0.106984</td>\n",
" <td>-0.099169</td>\n",
" <td>-0.123182</td>\n",
" <td>-0.074655</td>\n",
" <td>-0.048895</td>\n",
" <td>0.013757</td>\n",
" <td>0.245440</td>\n",
" <td>0.142846</td>\n",
" <td>0.310767</td>\n",
" <td>0.077988</td>\n",
" <td>0.044029</td>\n",
" <td>0.102674</td>\n",
" <td>1.000000</td>\n",
" <td>0.194856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LUNG_CANCER</th>\n",
" <td>0.053666</td>\n",
" <td>0.106305</td>\n",
" <td>0.034878</td>\n",
" <td>0.189192</td>\n",
" <td>0.144322</td>\n",
" <td>0.195086</td>\n",
" <td>0.143692</td>\n",
" <td>0.160078</td>\n",
" <td>0.333552</td>\n",
" <td>0.249054</td>\n",
" <td>0.294422</td>\n",
" <td>0.253027</td>\n",
" <td>0.064407</td>\n",
" <td>0.268940</td>\n",
" <td>0.194856</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY \\\n",
"GENDER 1.000000 -0.013120 0.041131 -0.202506 -0.152032 \n",
"AGE -0.013120 1.000000 -0.073410 0.025773 0.050605 \n",
"SMOKING 0.041131 -0.073410 1.000000 -0.020799 0.153389 \n",
"YELLOW_FINGERS -0.202506 0.025773 -0.020799 1.000000 0.558344 \n",
"ANXIETY -0.152032 0.050605 0.153389 0.558344 1.000000 \n",
"PEER_PRESSURE -0.261427 0.037848 -0.030364 0.313067 0.210278 \n",
"CHRONIC DISEASE -0.189925 -0.003431 -0.149415 0.015316 -0.006938 \n",
"FATIGUE -0.079020 0.021606 -0.037803 -0.099644 -0.181474 \n",
"ALLERGY 0.150174 0.037139 -0.030179 -0.147130 -0.159451 \n",
"WHEEZING 0.121047 0.052803 -0.147081 -0.058756 -0.174009 \n",
"ALCOHOL CONSUMING 0.434264 0.052049 -0.052771 -0.273643 -0.152228 \n",
"COUGHING 0.120228 0.168654 -0.138553 0.020803 -0.218843 \n",
"SHORTNESS OF BREATH -0.052893 -0.009189 0.051761 -0.109959 -0.155678 \n",
"SWALLOWING DIFFICULTY -0.048959 0.003199 0.042152 0.333349 0.478820 \n",
"CHEST PAIN 0.361547 -0.035806 0.106984 -0.099169 -0.123182 \n",
"LUNG_CANCER 0.053666 0.106305 0.034878 0.189192 0.144322 \n",
"\n",
" PEER_PRESSURE CHRONIC DISEASE FATIGUE ALLERGY \\\n",
"GENDER -0.261427 -0.189925 -0.079020 0.150174 \n",
"AGE 0.037848 -0.003431 0.021606 0.037139 \n",
"SMOKING -0.030364 -0.149415 -0.037803 -0.030179 \n",
"YELLOW_FINGERS 0.313067 0.015316 -0.099644 -0.147130 \n",
"ANXIETY 0.210278 -0.006938 -0.181474 -0.159451 \n",
"PEER_PRESSURE 1.000000 0.042893 0.094661 -0.066887 \n",
"CHRONIC DISEASE 0.042893 1.000000 -0.099411 0.134309 \n",
"FATIGUE 0.094661 -0.099411 1.000000 -0.001841 \n",
"ALLERGY -0.066887 0.134309 -0.001841 1.000000 \n",
"WHEEZING -0.037769 -0.040546 0.152151 0.166517 \n",
"ALCOHOL CONSUMING -0.132603 0.010144 -0.181573 0.378125 \n",
"COUGHING -0.068224 -0.160813 0.148538 0.206367 \n",
"SHORTNESS OF BREATH -0.214115 -0.011760 0.407027 -0.018030 \n",
"SWALLOWING DIFFICULTY 0.327764 0.068263 -0.115727 -0.037581 \n",
"CHEST PAIN -0.074655 -0.048895 0.013757 0.245440 \n",
"LUNG_CANCER 0.195086 0.143692 0.160078 0.333552 \n",
"\n",
" WHEEZING ALCOHOL CONSUMING COUGHING \\\n",
"GENDER 0.121047 0.434264 0.120228 \n",
"AGE 0.052803 0.052049 0.168654 \n",
"SMOKING -0.147081 -0.052771 -0.138553 \n",
"YELLOW_FINGERS -0.058756 -0.273643 0.020803 \n",
"ANXIETY -0.174009 -0.152228 -0.218843 \n",
"PEER_PRESSURE -0.037769 -0.132603 -0.068224 \n",
"CHRONIC DISEASE -0.040546 0.010144 -0.160813 \n",
"FATIGUE 0.152151 -0.181573 0.148538 \n",
"ALLERGY 0.166517 0.378125 0.206367 \n",
"WHEEZING 1.000000 0.261061 0.353657 \n",
"ALCOHOL CONSUMING 0.261061 1.000000 0.198023 \n",
"COUGHING 0.353657 0.198023 1.000000 \n",
"SHORTNESS OF BREATH 0.042289 -0.163370 0.284968 \n",
"SWALLOWING DIFFICULTY 0.108304 -0.000635 -0.136885 \n",
"CHEST PAIN 0.142846 0.310767 0.077988 \n",
"LUNG_CANCER 0.249054 0.294422 0.253027 \n",
"\n",
" SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \\\n",
"GENDER -0.052893 -0.048959 0.361547 \n",
"AGE -0.009189 0.003199 -0.035806 \n",
"SMOKING 0.051761 0.042152 0.106984 \n",
"YELLOW_FINGERS -0.109959 0.333349 -0.099169 \n",
"ANXIETY -0.155678 0.478820 -0.123182 \n",
"PEER_PRESSURE -0.214115 0.327764 -0.074655 \n",
"CHRONIC DISEASE -0.011760 0.068263 -0.048895 \n",
"FATIGUE 0.407027 -0.115727 0.013757 \n",
"ALLERGY -0.018030 -0.037581 0.245440 \n",
"WHEEZING 0.042289 0.108304 0.142846 \n",
"ALCOHOL CONSUMING -0.163370 -0.000635 0.310767 \n",
"COUGHING 0.284968 -0.136885 0.077988 \n",
"SHORTNESS OF BREATH 1.000000 -0.140307 0.044029 \n",
"SWALLOWING DIFFICULTY -0.140307 1.000000 0.102674 \n",
"CHEST PAIN 0.044029 0.102674 1.000000 \n",
"LUNG_CANCER 0.064407 0.268940 0.194856 \n",
"\n",
" LUNG_CANCER \n",
"GENDER 0.053666 \n",
"AGE 0.106305 \n",
"SMOKING 0.034878 \n",
"YELLOW_FINGERS 0.189192 \n",
"ANXIETY 0.144322 \n",
"PEER_PRESSURE 0.195086 \n",
"CHRONIC DISEASE 0.143692 \n",
"FATIGUE 0.160078 \n",
"ALLERGY 0.333552 \n",
"WHEEZING 0.249054 \n",
"ALCOHOL CONSUMING 0.294422 \n",
"COUGHING 0.253027 \n",
"SHORTNESS OF BREATH 0.064407 \n",
"SWALLOWING DIFFICULTY 0.268940 \n",
"CHEST PAIN 0.194856 \n",
"LUNG_CANCER 1.000000 "
]
},
"execution_count": 351,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lcc.corr()"
]
},
{
"cell_type": "code",
"execution_count": 331,
"id": "ac65b0f8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(lcc['LUNG_CANCER'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 332,
"id": "acd37c0e",
"metadata": {},
"outputs": [],
"source": [
"X=lcc.drop('LUNG_CANCER', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 333,
"id": "258aa1ec",
"metadata": {},
"outputs": [],
"source": [
"Y=lcc['LUNG_CANCER']"
]
},
{
"cell_type": "code",
"execution_count": 334,
"id": "e42cd884",
"metadata": {},
"outputs": [
{
"data": {
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GENDER</th>\n",
" <th>AGE</th>\n",
" <th>SMOKING</th>\n",
" <th>YELLOW_FINGERS</th>\n",
" <th>ANXIETY</th>\n",
" <th>PEER_PRESSURE</th>\n",
" <th>CHRONIC DISEASE</th>\n",
" <th>FATIGUE</th>\n",
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" <th>WHEEZING</th>\n",
" <th>ALCOHOL CONSUMING</th>\n",
" <th>COUGHING</th>\n",
" <th>SHORTNESS OF BREATH</th>\n",
" <th>SWALLOWING DIFFICULTY</th>\n",
" <th>CHEST PAIN</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
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" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>276 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n",
"0 1 69 1 2 2 1 \n",
"1 1 74 2 1 1 1 \n",
"2 0 59 1 1 1 2 \n",
"3 1 63 2 2 2 1 \n",
"4 0 63 1 2 1 1 \n",
".. ... ... ... ... ... ... \n",
"279 0 59 1 2 2 2 \n",
"280 0 59 2 1 1 1 \n",
"281 1 55 2 1 1 1 \n",
"282 1 46 1 2 2 1 \n",
"283 1 60 1 2 2 1 \n",
"\n",
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n",
"0 1 2 1 2 2 \n",
"1 2 2 2 1 1 \n",
"2 1 2 1 2 1 \n",
"3 1 1 1 1 2 \n",
"4 1 1 1 2 1 \n",
".. ... ... ... ... ... \n",
"279 1 1 2 2 1 \n",
"280 2 2 2 1 1 \n",
"281 1 2 2 1 1 \n",
"282 1 1 1 1 1 \n",
"283 1 2 1 2 2 \n",
"\n",
" COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \n",
"0 2 2 2 2 \n",
"1 1 2 2 2 \n",
"2 2 2 1 2 \n",
"3 1 1 2 2 \n",
"4 2 2 1 1 \n",
".. ... ... ... ... \n",
"279 2 1 2 1 \n",
"280 1 2 1 1 \n",
"281 1 2 1 2 \n",
"282 1 1 2 2 \n",
"283 2 2 2 2 \n",
"\n",
"[276 rows x 15 columns]"
]
},
"execution_count": 334,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 335,
"id": "3c434968",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 1\n",
"2 0\n",
"3 0\n",
"4 0\n",
" ..\n",
"279 1\n",
"280 0\n",
"281 0\n",
"282 0\n",
"283 1\n",
"Name: LUNG_CANCER, Length: 276, dtype: int32"
]
},
"execution_count": 335,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y"
]
},
{
"cell_type": "code",
"execution_count": 336,
"id": "a0da0136",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.20,random_state=42,shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 337,
"id": "3f0ca9f0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((220, 15), (220,))"
]
},
"execution_count": 337,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape, Y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 338,
"id": "46e49c5c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((56, 15), (56,))"
]
},
"execution_count": 338,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape,Y_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 339,
"id": "3b5c7038",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"std=StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 340,
"id": "992ef9b8",
"metadata": {},
"outputs": [],
"source": [
"X_train_std=std.fit_transform(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 341,
"id": "03932683",
"metadata": {},
"outputs": [],
"source": [
"X_test_std=std.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27dcd805",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 342,
"id": "5f476f23",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"lr=LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": 343,
"id": "7575bc0a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 343,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.fit(X_train_std,Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 344,
"id": "d33decb4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
" 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1])"
]
},
"execution_count": 344,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_predict=lr.predict(X_test_std)\n",
"y_predict\n"
]
},
{
"cell_type": "code",
"execution_count": 345,
"id": "5b113a01",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30 0\n",
"127 1\n",
"200 1\n",
"130 1\n",
"221 0\n",
"240 1\n",
"147 1\n",
"207 0\n",
"261 1\n",
"146 1\n",
"79 1\n",
"135 1\n",
"243 1\n",
"272 0\n",
"155 1\n",
"145 1\n",
"267 1\n",
"60 1\n",
"84 1\n",
"245 0\n",
"45 1\n",
"73 1\n",
"183 0\n",
"167 1\n",
"271 1\n",
"215 0\n",
"120 1\n",
"42 1\n",
"9 1\n",
"143 1\n",
"22 0\n",
"111 1\n",
"24 1\n",
"123 1\n",
"178 1\n",
"75 1\n",
"237 1\n",
"239 1\n",
"6 1\n",
"68 1\n",
"46 1\n",
"129 0\n",
"66 1\n",
"25 1\n",
"116 1\n",
"210 1\n",
"19 0\n",
"205 1\n",
"171 0\n",
"208 1\n",
"86 1\n",
"157 0\n",
"162 1\n",
"15 1\n",
"161 1\n",
"10 1\n",
"Name: LUNG_CANCER, dtype: int32"
]
},
"execution_count": 345,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_test"
]
},
{
"cell_type": "code",
"execution_count": 346,
"id": "4ac5a873",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 792x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(y_predict,Y_test)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 347,
"id": "54046fba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9285714285714286"
]
},
"execution_count": 347,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"ac_lr=accuracy_score(Y_test,y_predict)\n",
"ac_lr"
]
},
{
"cell_type": "code",
"execution_count": 348,
"id": "0598f63c",
"metadata": {},
"outputs": [],
"source": [
"lr_training_accuracy=lr.score(X_train_std,Y_train)\n",
"lr_testing_accuracy=lr.score(X_test_std,Y_test)"
]
},
{
"cell_type": "code",
"execution_count": 349,
"id": "eb3340d0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9318181818181818"
]
},
"execution_count": 349,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr_training_accuracy"
]
},
{
"cell_type": "code",
"execution_count": 350,
"id": "d9b9418d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9285714285714286"
]
},
"execution_count": 350,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr_testing_accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1bbe9264",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a3d0c1b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "266926ac",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}