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LUNG_CANCER_naive_bayes.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Naive Bayes\n",
"\n",
"## Lung Cancer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"\n",
"import string\n",
"import nltk\n",
"# if you have never done so, you need to download stopwords from nltk\n",
"# nltk.download('stopwords')\n",
"# nltk.download('wordnet')\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem.wordnet import WordNetLemmatizer\n",
"from nltk.tokenize import TweetTokenizer\n",
"from sklearn.model_selection import train_test_split\n",
"import re\n",
"import itertools\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from wordcloud import WordCloud\n",
"\n",
"from nltk.sentiment.util import mark_negation\n",
"from nltk import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>P207</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>P208</td>\n",
" <td>64</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>P209</td>\n",
" <td>39</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>P21</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\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",
" <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",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>P974</td>\n",
" <td>31</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>P975</td>\n",
" <td>38</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>P976</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>P977</td>\n",
" <td>44</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>P978</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>P979</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>P98</td>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>P980</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>P187</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>P981</td>\n",
" <td>35</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>P982</td>\n",
" <td>46</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>P983</td>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>P984</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>P985</td>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>Medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>P986</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>P987</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>P988</td>\n",
" <td>29</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>P989</td>\n",
" <td>39</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>P99</td>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>P188</td>\n",
" <td>29</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>P990</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>P991</td>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>P992</td>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>P993</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>P994</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>P995</td>\n",
" <td>44</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>P996</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>P997</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>P998</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>High</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>P999</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>High</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" patient_id age gender air_pollution alcohol_use dust_allergy \\\n",
"0 P1 33 1 2 4 5 \n",
"1 P10 17 1 3 1 5 \n",
"2 P107 44 1 6 7 7 \n",
"3 P189 39 2 6 8 7 \n",
"4 P19 38 2 2 1 5 \n",
"5 P190 49 1 6 5 6 \n",
"6 P191 37 1 8 8 7 \n",
"7 P192 26 2 7 7 7 \n",
"8 P193 37 2 7 7 7 \n",
"9 P194 33 1 6 7 7 \n",
"10 P195 44 1 6 7 7 \n",
"11 P196 37 2 6 8 7 \n",
"12 P197 25 2 4 5 6 \n",
"13 P108 64 2 6 8 7 \n",
"14 P198 18 2 6 8 7 \n",
"15 P199 47 1 6 5 6 \n",
"16 P2 25 2 3 1 4 \n",
"17 P20 19 1 3 2 4 \n",
"18 P200 26 2 8 8 7 \n",
"19 P201 37 1 7 7 7 \n",
"20 P202 35 2 4 5 6 \n",
"21 P203 33 1 2 4 5 \n",
"22 P204 25 2 3 1 4 \n",
"23 P205 35 2 4 5 6 \n",
"24 P109 39 2 4 5 6 \n",
"25 P206 27 2 2 3 4 \n",
"26 P207 48 1 6 7 7 \n",
"27 P208 64 1 6 8 7 \n",
"28 P209 39 1 4 5 6 \n",
"29 P21 33 1 6 7 7 \n",
".. ... ... ... ... ... ... \n",
"970 P974 31 2 3 2 4 \n",
"971 P975 38 2 1 2 3 \n",
"972 P976 35 1 6 8 7 \n",
"973 P977 44 1 6 7 7 \n",
"974 P978 33 1 2 4 5 \n",
"975 P979 45 1 3 1 4 \n",
"976 P98 26 2 8 8 7 \n",
"977 P980 53 1 3 1 4 \n",
"978 P187 19 1 6 8 7 \n",
"979 P981 35 2 4 5 6 \n",
"980 P982 46 1 6 8 7 \n",
"981 P983 27 1 6 7 7 \n",
"982 P984 26 1 3 2 4 \n",
"983 P985 37 1 1 2 3 \n",
"984 P986 28 1 6 7 7 \n",
"985 P987 19 1 6 8 7 \n",
"986 P988 29 2 4 5 6 \n",
"987 P989 39 2 6 8 7 \n",
"988 P99 37 1 7 7 7 \n",
"989 P188 29 2 4 5 6 \n",
"990 P990 49 1 6 5 6 \n",
"991 P991 37 1 8 8 7 \n",
"992 P992 26 2 7 7 7 \n",
"993 P993 37 2 7 7 7 \n",
"994 P994 33 1 6 7 7 \n",
"995 P995 44 1 6 7 7 \n",
"996 P996 37 2 6 8 7 \n",
"997 P997 25 2 4 5 6 \n",
"998 P998 18 2 6 8 7 \n",
"999 P999 47 1 6 5 6 \n",
"\n",
" occupational_hazards genetic_risk chronic_lung_disease balanced_diet \\\n",
"0 4 3 2 2 \n",
"1 3 4 2 2 \n",
"2 7 7 6 7 \n",
"3 7 7 6 7 \n",
"4 3 2 3 2 \n",
"5 5 5 4 6 \n",
"6 7 7 6 7 \n",
"7 7 7 6 7 \n",
"8 7 6 7 7 \n",
"9 7 7 7 6 \n",
"10 7 7 6 7 \n",
"11 7 7 6 7 \n",
"12 5 5 4 6 \n",
"13 7 7 6 7 \n",
"14 7 7 6 7 \n",
"15 5 5 4 6 \n",
"16 3 2 3 4 \n",
"17 2 3 2 3 \n",
"18 7 7 6 7 \n",
"19 7 6 7 7 \n",
"20 5 5 4 6 \n",
"21 4 3 2 2 \n",
"22 3 2 3 4 \n",
"23 5 6 5 5 \n",
"24 6 5 4 6 \n",
"25 2 4 3 3 \n",
"26 7 7 6 7 \n",
"27 7 7 6 7 \n",
"28 6 5 4 6 \n",
"29 7 7 6 7 \n",
".. ... ... ... ... \n",
"970 2 3 2 3 \n",
"971 4 2 4 3 \n",
"972 7 7 6 2 \n",
"973 7 7 6 7 \n",
"974 4 3 2 2 \n",
"975 3 2 3 4 \n",
"976 7 7 6 7 \n",
"977 2 3 2 3 \n",
"978 7 7 6 7 \n",
"979 5 5 4 6 \n",
"980 7 7 6 7 \n",
"981 7 7 6 7 \n",
"982 2 3 2 3 \n",
"983 4 2 4 3 \n",
"984 7 7 6 7 \n",
"985 7 7 6 7 \n",
"986 5 5 4 6 \n",
"987 7 7 6 7 \n",
"988 7 6 7 7 \n",
"989 5 5 4 6 \n",
"990 5 5 4 6 \n",
"991 7 7 6 7 \n",
"992 7 7 6 7 \n",
"993 7 6 7 7 \n",
"994 7 7 7 6 \n",
"995 7 7 6 7 \n",
"996 7 7 6 7 \n",
"997 5 5 4 6 \n",
"998 7 7 6 7 \n",
"999 5 5 4 6 \n",
"\n",
" ... fatigue weight_loss shortness_of_breath wheezing \\\n",
"0 ... 3 4 2 2 \n",
"1 ... 1 3 7 8 \n",
"2 ... 5 3 2 7 \n",
"3 ... 3 2 4 1 \n",
"4 ... 6 7 2 5 \n",
"5 ... 8 7 9 2 \n",
"6 ... 3 2 4 1 \n",
"7 ... 2 7 6 7 \n",
"8 ... 4 2 3 1 \n",
"9 ... 8 5 7 6 \n",
"10 ... 5 3 2 7 \n",
"11 ... 9 6 5 7 \n",
"12 ... 8 7 9 2 \n",
"13 ... 9 6 5 7 \n",
"14 ... 3 2 4 1 \n",
"15 ... 8 7 9 2 \n",
"16 ... 3 2 2 4 \n",
"17 ... 4 5 6 5 \n",
"18 ... 3 2 4 1 \n",
"19 ... 4 2 3 1 \n",
"20 ... 8 7 9 2 \n",
"21 ... 3 4 2 2 \n",
"22 ... 3 2 2 4 \n",
"23 ... 1 4 3 2 \n",
"24 ... 5 3 2 4 \n",
"25 ... 1 2 4 6 \n",
"26 ... 5 3 2 7 \n",
"27 ... 9 6 5 7 \n",
"28 ... 5 3 2 4 \n",
"29 ... 4 4 5 6 \n",
".. ... ... ... ... ... \n",
"970 ... 4 5 6 5 \n",
"971 ... 4 1 2 4 \n",
"972 ... 2 7 6 5 \n",
"973 ... 5 3 2 7 \n",
"974 ... 3 4 2 2 \n",
"975 ... 3 2 2 4 \n",
"976 ... 3 2 4 1 \n",
"977 ... 2 2 3 4 \n",
"978 ... 9 6 5 7 \n",
"979 ... 8 7 9 2 \n",
"980 ... 3 2 4 1 \n",
"981 ... 2 7 6 7 \n",
"982 ... 4 5 6 5 \n",
"983 ... 4 1 2 4 \n",
"984 ... 5 3 2 7 \n",
"985 ... 9 6 5 7 \n",
"986 ... 8 7 9 2 \n",
"987 ... 3 2 4 1 \n",
"988 ... 4 2 3 1 \n",
"989 ... 8 7 9 2 \n",
"990 ... 8 7 9 2 \n",
"991 ... 3 2 4 1 \n",
"992 ... 2 7 6 7 \n",
"993 ... 4 2 3 1 \n",
"994 ... 8 5 7 6 \n",
"995 ... 5 3 2 7 \n",
"996 ... 9 6 5 7 \n",
"997 ... 8 7 9 2 \n",
"998 ... 3 2 4 1 \n",
"999 ... 8 7 9 2 \n",
"\n",
" swallowing_difficulty clubbing_of_finger_nails frequent_cold \\\n",
"0 3 1 2 \n",
"1 6 2 1 \n",
"2 8 2 4 \n",
"3 4 2 4 \n",
"4 8 1 3 \n",
"5 1 4 6 \n",
"6 4 2 4 \n",
"7 6 7 2 \n",
"8 4 5 6 \n",
"9 7 8 7 \n",
"10 8 2 4 \n",
"11 2 4 3 \n",
"12 1 4 6 \n",
"13 2 4 3 \n",
"14 4 2 4 \n",
"15 1 4 6 \n",
"16 2 2 3 \n",
"17 5 4 6 \n",
"18 4 2 4 \n",
"19 4 5 6 \n",
"20 1 4 6 \n",
"21 3 1 2 \n",
"22 2 2 3 \n",
"23 4 6 2 \n",
"24 3 1 7 \n",
"25 5 4 2 \n",
"26 8 2 4 \n",
"27 2 4 3 \n",
"28 3 1 7 \n",
"29 5 5 4 \n",
".. ... ... ... \n",
"970 5 4 6 \n",
"971 6 5 4 \n",
"972 1 9 3 \n",
"973 8 2 4 \n",
"974 3 1 2 \n",
"975 2 2 3 \n",
"976 4 2 4 \n",
"977 1 5 2 \n",
"978 2 4 3 \n",
"979 1 4 6 \n",
"980 4 2 4 \n",
"981 6 7 2 \n",
"982 5 4 6 \n",
"983 6 5 4 \n",
"984 8 2 4 \n",
"985 2 4 3 \n",
"986 1 4 6 \n",
"987 4 2 4 \n",
"988 4 5 6 \n",
"989 1 4 6 \n",
"990 1 4 6 \n",
"991 4 2 4 \n",
"992 6 7 2 \n",
"993 4 5 6 \n",
"994 7 8 7 \n",
"995 8 2 4 \n",
"996 2 4 3 \n",
"997 1 4 6 \n",
"998 4 2 4 \n",
"999 1 4 6 \n",
"\n",
" dry_cough snoring level \n",
"0 3 4 Low \n",
"1 7 2 Medium \n",
"2 5 3 High \n",
"3 2 3 High \n",
"4 2 3 Medium \n",
"5 7 2 High \n",
"6 2 3 High \n",
"7 3 1 High \n",
"8 7 5 High \n",
"9 6 2 High \n",
"10 5 3 High \n",
"11 1 4 High \n",
"12 7 2 High \n",
"13 1 4 High \n",
"14 2 3 High \n",
"15 7 2 High \n",
"16 4 3 Low \n",
"17 5 4 Medium \n",
"18 2 3 High \n",
"19 7 5 High \n",
"20 7 2 High \n",
"21 3 4 Low \n",
"22 4 3 Low \n",
"23 4 1 Medium \n",
"24 5 6 Medium \n",
"25 1 5 Medium \n",
"26 5 3 High \n",
"27 1 4 High \n",
"28 5 6 Medium \n",
"29 6 5 High \n",
".. ... ... ... \n",
"970 5 4 Medium \n",
"971 2 5 Medium \n",
"972 4 2 Medium \n",
"973 5 3 High \n",
"974 3 4 Low \n",
"975 4 3 Low \n",
"976 2 3 High \n",
"977 6 2 Low \n",
"978 1 4 High \n",
"979 7 2 High \n",
"980 2 3 High \n",
"981 3 1 High \n",
"982 5 4 Medium \n",
"983 2 5 Medium \n",
"984 5 3 High \n",
"985 1 4 High \n",
"986 7 2 High \n",
"987 2 3 High \n",
"988 7 5 High \n",
"989 7 2 High \n",
"990 7 2 High \n",
"991 2 3 High \n",
"992 3 1 High \n",
"993 7 5 High \n",
"994 6 2 High \n",
"995 5 3 High \n",
"996 1 4 High \n",
"997 7 2 High \n",
"998 2 3 High \n",
"999 7 2 High \n",
"\n",
"[1000 rows x 25 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#load data and add a column for sentiment based on polarity\n",
"data = pd.read_csv(\"cancer_patient.csv\", encoding=\"ISO-8859-1\")\n",
"\n",
"#check data to see if everything worked\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### here we see that the gender 1 which refer to male is in high level of cancer than female"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<span style=\"color:red\"> Interesting and especially for higher levels of lung cancer. Please include a summary sentence like this for all your graphs.</span>"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.catplot(x=\"gender\", col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"genetic_risk\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"coughing_of_blood\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"wheezing\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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CFwAAgIUIXwAAABYifAEAAFiI8AUAAGAhwhcAAICFCF8AAAAWInwBAABYiPAFAABgIcIXAACAhQhfAAAAFiJ8AQAAWIjwBQAAYCHCFwAAgIUIXwAAABbyafhavny54uPjFR8fr6VLl0qSCgoK5HQ6FRMTo5ycHO99jx49qqSkJMXGxmrBggWqra31ZWsAAADNwmfhq6CgQO+//742b96sLVu26H//93+Vl5en9PR0rVixQtu2bVNxcbH27t0rSXrllVeUkZGhHTt2yDAMrV+/3letAQAANBufhS+Hw6F58+YpODhYQUFB6tWrl0pKStSzZ09FRETIbrfL6XQqPz9fZ8+eVVVVlQYOHChJSkpKUn5+vq9aAwAAaDY+C18PPvigN0yVlJRo+/btstlscjgc3vuEh4errKxM58+fr7ff4XCorKzMV60BAAA0G7uvD/Dpp59qxowZSktLU+vWrVVSUuK9zTAM2Ww2eTwe2Wy2m/Y3RlhYe7NaBu4azD1aIuYe/sLhCG3S43wavgoLCzVnzhylp6crPj5eH3zwgVwul/d2l8ul8PBwde3atd7+CxcuKDw8vFHHKi+vlMdjmNY7cCea+oRsLOYe/oS5h78ze0Zdroom1fTZZcfS0lL98Ic/VHZ2tuLj4yVJAwYM0KlTp3T69GnV1dUpLy9PUVFR6t69u0JCQlRYWChJ2rp1q6KionzVGgAAQLPx2ZmvVatWye12Kysry7svJSVFWVlZmj17ttxut6KjozV69GhJUnZ2thYuXKjKykr169dPqampvmoNAACg2dgMwwiIc7echoY/4fILWiLmHv7O4QjV4RV7TKk1YObjvr3s+E2vPDxx4kSjDwYAANDS3TZ8Xb58WZcvX9Zzzz2nK1eueLcvXLigWbNmWdUjAABAwLjtmq+XX35Z+/btkyQNHjz4/x5ktys2Nta3nQEAAASg24avVatWSZLmz5+v1157zZKGAAAAAlmDXu342muv6ezZs7py5Yq+vj6/X79+PmsMAAAgEDUofOXm5mrVqlUKCwvz7rPZbNq1a5fPGgMAAAhEDQpfW7Zs0c6dO9WlSxdf9wMAABDQGvRWE926dSN4AQAAmKBBZ74iIyO1dOlSjRw5Um3atPHuZ80XAABA4zQofG3atEmSlJ+f793Hmi8AAIDGa1D42r17t6/7AAAAaBEaFL5Wr179jfunTp1qajMAAACBrkHh6/jx496vq6urdejQIUVGRvqsKQAAgEDV4DdZ/bqysjItWLDAJw0BAAAEsga91cTf69Kli86ePWt2LwAAAAGv0Wu+DMNQcXFxvXe7BwAAQMM0es2X9OWbrqalpfmkIQAAgEDWqDVfZ8+eVW1trXr27OnTpgAAAAJVg8LX6dOnNXPmTJ0/f14ej0f33XefVq5cqV69evm6PwAAgIDSoAX3ixYt0vTp03Xo0CEVFhbqxRdf1M9+9jNf9wYAABBwGhS+ysvLlZiY6N0eP368Ll265LOmAAAAAlWDwlddXZ0uX77s3b548aLPGgIAAAhkDVrz9cwzz+jpp5/WmDFjZLPZtG3bNj377LO+7g0AACDgNOjMV3R0tCSppqZGJ0+eVFlZmUaNGtWgA1RWViohIUFnzpyRJM2fP18xMTEaO3asxo4dq/fee0+SVFBQIKfTqZiYGOXk5DTlewEAAPB7DTrzNW/ePE2aNEmpqalyu9363e9+p/T0dP3617++7eMOHz6shQsXqqSkxLuvuLhYa9asUXh4uHdfVVWV0tPT9c4776hbt26aMWOG9u7d6w19AAAAgaJBZ74uXbqk1NRUSVJISIimTJkil8v1rY9bv369MjMzvUHrxo0bOnfunNLT0+V0OpWbmyuPx6OioiL17NlTERERstvtcjqdys/Pv4NvCwAAwD816MxXXV2dysrK1KVLF0nShQsXZBjGtz5u8eLF9bYvXLigIUOGKDMzU6GhoZoxY4Y2bNigdu3ayeFweO8XHh6usrKyxnwfCgtr36j7A4GAuUdLxNzDXzgcoU16XIPC15QpUzRu3Dg99thjstlsKigoaNLHC0VEROj111/3bk+ePFlbtmxRbGysbDabd79hGPW2G6K8vFIez7cHQsAKTX1CNhZzD3/C3MPfmT2jLldFk2o2KHwlJyfre9/7ng4cOKDWrVvrX//1X9WnT59GH+zYsWMqKSlRbGyspC9Dlt1uV9euXetdxnS5XPXWhAEAAASKBoUvSerbt6/69u17RwczDENLlizRkCFD1K5dO61bt06JiYkaMGCATp06pdOnT6tHjx7Ky8vT+PHj7+hYAAAA/qjB4csMffv21fPPP68JEyaotrZWMTExSkhIkCRlZWVp9uzZcrvdio6O1ujRo61sDQAAwBI2oyEr5+8CrAGAP2HtC1oi5h7+zuEI1eEVe0ypNWDm401e89Wgt5oAAACAOQhfAAAAFiJ8AQAAWIjwBQAAYCHCFwAAgIUIXwAAABYifAEAAFiI8AUAAGAhwhcAAICFCF8AAAAWInwBAABYiPAFAABgIcIXAACAhezN3QAA+ErH0GAFtQkxpVZNlVuXK6pNqQWgZSN8AQhYQW1CtC11qim14t5eLRG+AJiAy44AAAAWInwBAABYiPAFAABgIcIXAACAhQhfAAAAFiJ8AQAAWIjwBQAAYCGfhq/KykolJCTozJkzkqSCggI5nU7FxMQoJyfHe7+jR48qKSlJsbGxWrBggWpra33ZFgAAQLPxWfg6fPiwJkyYoJKSEklSVVWV0tPTtWLFCm3btk3FxcXau3evJOmVV15RRkaGduzYIcMwtH79el+1BQAA0Kx89g7369evV2ZmptLS0iRJRUVF6tmzpyIiIiRJTqdT+fn56t27t6qqqjRw4EBJUlJSknJzczVx4kRftQbAj9zbIUQhwcF3XMddXa2rV9wmdAQAvuWz8LV48eJ62+fPn5fD4fBuh4eHq6ys7Kb9DodDZWVlvmoLgJ8JCQ7WlNU/uuM6b01dJonwBcD/WfbZjh6PRzabzbttGIZsNtst9zdWWFh7U/q8HU9tjVrZg/yuFlouK+b+buJwhN7V9dEwzD38RVN/J1gWvrp27SqXy+XddrlcCg8Pv2n/hQsXFB4e3uj65eWV8ngMU3q9FYcjVIVLp5tS65G0/yeXq8KUWvA/Vv0jbcXc+5qZP6u/f06Z/f+B5+ztMffwd774ndCUmpa91cSAAQN06tQpnT59WnV1dcrLy1NUVJS6d++ukJAQFRYWSpK2bt2qqKgoq9oCAACwlGVnvkJCQpSVlaXZs2fL7XYrOjpao0ePliRlZ2dr4cKFqqysVL9+/ZSammpVWwAAAJbyefjavXu39+vIyEj94Q9/uOk+ffv21YYNG3zdCgAAQLPjHe4BAAAsRPgCAACwEOELAADAQoQvAAAACxG+AAAALET4AgAAsBDhCwAAwEKELwAAAAsRvgAAACxE+AIAALAQ4QsAAMBChC8AAAAL+fyDtdFw93YIUUhw8B3XcVdX6+oVtwkdAQAAswVU+Aq9t43ahASZUqvKXaOKq1Wm1GqokOBgTVn9ozuu89bUZZIIXwAA+KOACl9tQoI0Me1dU2r9dukkVcja8AUAAAIfa74AAAAsRPgCAACwEOELAADAQoQvAAAACwXUgnvcWsfQYAW1CTGlVk2VW5crqk2pBQBAS0P4aiGC2oRoW+pUU2rFvb1aInwBANAkXHYEAACwULOc+Zo8ebIuXrwou/3Lwy9atEiff/65fvWrX6m2tlbPPvusJk2a1BytAQAA+JTl4cswDJWUlOi///u/veGrrKxMc+fO1aZNmxQcHKyUlBQNHjxYvXv3tro9AAAAn7I8fH322WeSpGnTpuny5cv6wQ9+oHvuuUdDhgxRx44dJUmxsbHKz8/XrFmzrG4PAADApyxf83X16lVFRkbq9ddf11tvvaW1a9fq3Llzcjgc3vuEh4errKzM6tYAAAB8zvIzX4MGDdKgQYO828nJyXrttdf04osvevcZhiGbzdaoumFh7U3r8SsOR6jpNa2qfzf3jobzxdzfzZj7loG5h79o6u8Ey8PXhx9+qJqaGkVGRkr6Mmh1795dLpfLex+Xy6Xw8PBG1S0vrzT9CelyVdTbNvsXry/rW9076rPqH+ny8kp5PIYlx/IV5j5wMPfwd774ndCUmpZfdqyoqNDSpUvldrtVWVmpzZs36z/+4z+0f/9+Xbx4UTdu3NDOnTsVFRVldWsAAAA+Z/mZr+HDh+vw4cMaN26cPB6PJk6cqEceeURz585VamqqampqlJycrIcfftjq1gAAAHyuWd7n66WXXtJLL71Ub5/T6ZTT6WyOdgAAACzDO9wDAABYiM92BHBb93UIlj3YnA9lr61269IVPhcUQMtG+AJwW/bgEBUunW5KrUfS/p8kwheAlo3LjgAAABYifAEAAFiI8AUAAGAhwhcAAICFWHAPAC3QfR3ayh5szj8BtdW1unTlhim1gJaA8AUALZA92K7DK/aYUmvAzMdNqQO0FFx2BAAAsBDhCwAAwEKELwAAAAsRvgAAACxE+AIAALAQr3YEgCbqcG9bBYeY82u02l2rK1f/7+0aOtwbrOAQcz7QvNrt1pWrfKamP7m3Q4hCgoPvuI67ulpXr7hN6AhWInwBQBMFh9i1ZMEGU2qlL07+u9oh+uX8GabU/vFrK8UHmvuXkOBgTVn9ozuu89bUZZIIX3cbLjsCAABYiPAFAABgIcIXAACAhQhfAAAAFmLBPQAAAaRjaLCC2pjzStmaKrcuV1j3Yo2W8ipfwhcAAAEkqE2ItqVONaVW3NurJQvDV0t5lS+XHQEAACzkV+Hrj3/8o+Li4hQTE6N33323udsBAAAwnd9cdiwrK1NOTo42bdqk4OBgpaSkaPDgwerdu3dztwYAAGAavwlfBQUFGjJkiDp27ChJio2NVX5+vmbNmtWgx7dqZZMkdb7vHtN6+qrm1wXfG+bT+p3bd/JZ7badfds7rGfV/wfm/tb1O3Rs57P693b0be9BoW18Wt9XAuX3z908977UEubeZhiGYVoXd2DlypW6fv265s6dK0n6/e9/r6KiIv385z9v5s4AAADM4zdrvjwej2y2/0uQhmHU2wYAAAgEfhO+unbtKpfL5d12uVwKDw9vxo4AAADM5zfh61/+5V+0f/9+Xbx4UTdu3NDOnTsVFRXV3G0BAACYym8W3Hfp0kVz585VamqqampqlJycrIcffri52wIAADCV3yy4BwAAaAn85rIjAABAS0D4AgAAsBDhCwAAwEKELwAAAAv5zasdrVJZWamUlBS98cYb6tGjh6m1ly9fru3bt0uSoqOjlZaWZlrtZcuWaceOHbLZbEpOTtbUqVNNq/11v/jFL3Tp0iVlZWWZWnfy5Mm6ePGi7PYvR27RokUaMGCAKbV3796t5cuX68aNGxo6dKgWLlxoSl3py09aWLNmjXf7zJkzGjt2rDIyMkw7hhWY+9tj7utj7r8dc39rzH0DGC3IRx99ZCQkJBj9+vUzvvjiC1Nr79u3z3j66acNt9ttVFdXG6mpqcbOnTtNqX3w4EEjJSXFqKmpMW7cuGEMHz7cOHnypCm1v66goMAYPHiw8ZOf/MTUuh6Pxxg2bJhRU1Njal3DMIzPP//cGDZsmFFaWmpUV1cbEyZMMPbs2WP6cQzDMI4fP26MGjXKKC8v90l9X2Hub4+5vz3m/mbM/a0x9w3Toi47rl+/XpmZmT5553yHw6F58+YpODhYQUFB6tWrl86dO2dK7UcffVRvv/227Ha7ysvLVVdXp3btzPswX0m6fPmycnJy9MILL5haV5I+++wzSdK0adP05JNP1vvL4k699957iouLU9euXRUUFKScnBzT/sL6ez/96U81d+5cdepkzofhWoW5vzXm/tsx9zdj7m+NuW+YFnXZcfHixT6r/eCDD3q/Likp0fbt2/W73/3OtPpBQUHKzc3Vb37zG40ePVpdunQxrbYkZWRkaO7cuSotLTW1riRdvXpVkZGRevXVV1VTU6PU1FR95zvf0dChQ++49unTpxUUFKQXXnhBpaWlevzxx/XSSy+Z0HV9BQUFqqqq0pgxY0yv7WvM/a0x97fH3H8z5v7WmPuGaVEmsEXjAAAGB0lEQVRnvqzw6aefatq0aUpLS9MDDzxgau05c+Zo//79Ki0t1fr1602r+/vf/17dunVTZGSkaTW/btCgQVq6dKlCQ0PVqVMnJScna+/evabUrqur0/79+7VkyRKtW7dORUVF2rx5sym1v27t2rU+W3cRCJj7mzH3gY+5vxlz3zCELxMVFhZqypQpevnll5WYmGha3ZMnT+ro0aOSpLZt2yomJkbHjh0zrf62bdu0b98+jR07Vrm5udq9e7eWLFliWv0PP/xQ+/fv924bhuFdiHmnOnfurMjISHXq1Elt2rTRE088oaKiIlNqf6W6ulqHDh3SiBEjTK0bKJj7b8bcBzbm/psx9w3kk5Vkfm748OGmL8A8d+6cMXjwYKOgoMDUuoZhGHv27DGSkpIMt9ttuN1uY+rUqUZeXp7pxzEMw9i4caPpCzB3795tjBs3zqiqqjIqKioMp9Np/M///I8ptT/66CMjNjbWuHLlilFbW2vMmDHDWL9+vSm1v1JUVGSkpKSYWrM5MPe3xtzfjLm/Neb+1pj7hmlRa758adWqVXK73fVespuSkqIJEybcce3o6GgVFRVp3Lhxat26tWJiYhQfH3/Hda0yfPhwHT58WOPGjZPH49HEiRM1aNAgU2oPGDBA06dP18SJE1VTU6OhQ4dq/PjxptT+yhdffKGuXbuaWjNQMPe3xtwHLub+1pj7huGDtQEAACzEmi8AAAALEb4AAAAsRPgCAACwEOELAADAQoQvAAAACxG+Ati8efO0atUqU2odOXJEc+bMkSQVFRX55lPeARMw92iJmPu7C+ELDdK/f3/l5uZKkk6cOKGysrJm7gjwPeYeLRFz73u8z1eAWLdund555x21atVKnTt31quvvqqVK1fq6tWrunDhgiorKzV06FD95Cc/kd1u18mTJ7V48WJdvnxZdXV1mjx5spKTk3Xt2jXNnz9fp0+fVqtWrdSvXz8tWrRIhw4d0s9//nP9+te/1oQJE1RRUaGYmBi1bt1aYWFhmjt3riRp69at2rlzp15//fVm/omgJWDu0RIx9wHA5++hD58rKCgwnnjiCaO8vNwwjC8/MmLMmDFGWlqakZiYaFy7ds1wu93GM888Y7z77rtGTU2NERcXZxQXFxuGYRhXr141xowZY/ztb38zNm/ebEybNs0wDMOora01FixYYJSUlBgHDhww4uPjvfWff/55wzAM4+OPPzaGDh1q1NTUGIZhGBMnTjT+8pe/WP0jQAvE3KMlYu4DA5cdA8Bf//pXxcXFqVOnTpKkpKQk72nisWPHql27dgoODtaTTz6pgoIClZSU6PPPP1d6errGjh2rZ555RlVVVfr444/1yCOP6MSJE5o8ebLefPNNPfvss+rZs+ctj/1P//RP6tGjh/bs2aOTJ0/q/PnzGjZsmCXfN1o25h4tEXMfGPhsxwDg8Xhu2mcYhmpra9W6det6++x2u+rq6hQaGqqtW7d6b7tw4YJCQ0MVEhKi9957TwcPHtSBAwc0depULVq0SPfcc88tjz9p0iRt3LhRDzzwgH7wgx/IZrOZ+w0C34C5R0vE3AcGznwFgMcee0zbtm3TxYsXJUkbN25Ux44d1bp1a/3pT39SdXW13G63Nm/erKioKH3nO99RmzZtvE/G0tJSJSQkqLi4WL/97W81f/58DRs2TK+88oqGDRumjz/+uN7xWrdurdraWu92bGysjh49qh07dpj+IafArTD3aImY+8BA+AoAQ4cO1ZQpU/Tss88qPj5eW7Zs0cqVK9WqVSv16NFDEydO1Lhx4/T9739fiYmJCg4O1ooVK7RhwwY5nU5NmzZNP/rRj/TII49o3LhxqqurU1xcnJKSklRRUaHJkyfXO97AgQP1xRdfaNasWZKk4OBgxcbGatCgQd5T4YCvMfdoiZj7wMCrHXHHrl+/rmeeeUYZGRkaOHBgc7cDWIK5R0vE3JuDM1+4I3/961/1+OOP67HHHuOJiBaDuUdLxNybhzNfAAAAFuLMFwAAgIUIXwAAABYifAEAAFiI8AUAAGAhwhcAAICFCF8AAAAW+v8FeqTzdw1mRgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"obesity\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"snoring\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"air_pollution\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 604.8x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"clubbing_of_finger_nails\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.7);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## let's see which gender is more affected by lung_cancer?\n",
"sns.countplot(x = 'gender', data = data)\n",
"(data['level']).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 777.6x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gr = sns.catplot(x=\"age\",col=\"level\",\n",
" data=data, kind=\"count\",height=4, aspect=.9);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x = 'genetic_risk', data = data)\n",
"(data['level']).value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We're observing that too many patients have lung cancer caused by genetic risk"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 49,
"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(x = 'coughing_of_blood', data = data)\n",
"(data['level']).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 50,
"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(x = 'wheezing', data = data)\n",
"(data['level']).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x = 'snoring', data = data)\n",
"data['level'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like most people have positive or neutral comments about microfinance. In fact, the volume of negative comments is the most interesting and should be investigated further."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"High 365\n",
"Medium 332\n",
"Low 303\n",
"Name: level, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x = 'clubbing_of_finger_nails', data = data)\n",
"data['level'].value_counts()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}