279 lines
12 KiB
Plaintext
279 lines
12 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e81cac73",
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"metadata": {},
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"source": [
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"# Lets import file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4bc999ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "0eec8a65",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"heart-disease.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f588239f",
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"metadata": {},
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"source": [
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"Poglejmo kaj smo importali"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "0cec76a8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>age</th>\n",
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" <th>sex</th>\n",
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" <th>cp</th>\n",
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" <th>trestbps</th>\n",
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" <th>chol</th>\n",
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" <th>fbs</th>\n",
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" <th>restecg</th>\n",
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" <th>thalach</th>\n",
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" <th>exang</th>\n",
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" <th>oldpeak</th>\n",
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" <th>slope</th>\n",
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" <th>ca</th>\n",
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" <th>thal</th>\n",
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" <th>target</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>63</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>145</td>\n",
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" <td>233</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>150</td>\n",
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" <td>0</td>\n",
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" <td>2.3</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>37</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>130</td>\n",
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" <td>250</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>187</td>\n",
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" <td>0</td>\n",
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" <td>3.5</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>41</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>130</td>\n",
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" <td>204</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>172</td>\n",
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" <td>0</td>\n",
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" <td>1.4</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>56</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>120</td>\n",
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" <td>236</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>178</td>\n",
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" <td>0</td>\n",
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" <td>0.8</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>57</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>120</td>\n",
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" <td>354</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>163</td>\n",
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" <td>1</td>\n",
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" <td>0.6</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
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"0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
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"1 37 1 2 130 250 0 1 187 0 3.5 0 \n",
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"2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
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"3 56 1 1 120 236 0 1 178 0 0.8 2 \n",
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"4 57 0 0 120 354 0 1 163 1 0.6 2 \n",
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"\n",
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" ca thal target \n",
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"0 0 1 1 \n",
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"1 0 2 1 \n",
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"2 0 2 1 \n",
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"3 0 2 1 \n",
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"4 0 2 1 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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|
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"id": "ec647955",
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"metadata": {},
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"source": [
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"# Lets graph our data"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 6,
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"id": "c9e4750b",
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"import matplotlib.pyplot as plt"
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]
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},
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|
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{
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"cell_type": "code",
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|
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"execution_count": 8,
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|
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"id": "a4cf20f4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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|
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"<AxesSubplot:>"
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|
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]
|
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|
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},
|
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|
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"execution_count": 8,
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|
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"metadata": {},
|
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|
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"output_type": "execute_result"
|
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|
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},
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|
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD1CAYAAACrz7WZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOF0lEQVR4nO3df6zdd13H8efL1k2BGLr0tnb9YYd2YEcwmMtEiQaZuBkI3T9LuohpcEmjGQhGA538sb+azB9BTRSTBupqxC0NoGsgIrWKi1E27tiAdWW0YWO7tK53zt8mhY63f9zv9Hg4d/fe8z33Xvrp8/HPOd/P9/s95/1H8+w3357Tk6pCktSW71rrASRJk2fcJalBxl2SGmTcJalBxl2SGmTcJalB69d6AICNGzfWzp0713oMSbqkPPTQQ89W1dSofd8Rcd+5cyczMzNrPYYkXVKSfG2hfd6WkaQGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJatB3xJeYLhU7D3xyrUdoypN3vWWtR5Ca5ZW7JDXIuEtSgxaNe5LDSc4neXRo/V1JHk9yMslvDazfkeRMt+/GlRhakvTilnLP/W7gD4A/eWEhyU8De4DXVNWFJJu69d3AXuA64Grgr5NcW1XPT3pwSdLCFr1yr6r7geeGln8ZuKuqLnTHnO/W9wD3VtWFqnoCOANcP8F5JUlLMO4992uBn0zyQJK/S/K6bn0r8PTAcbPdmiRpFY37Ucj1wAbg9cDrgKNJXgFkxLE16gWS7Af2A+zYsWPMMSRJo4x75T4LfLzmPQh8C9jYrW8fOG4bcHbUC1TVoaqarqrpqamRPyQiSRrTuHH/C+BNAEmuBa4AngWOAXuTXJnkGmAX8OAE5pQkLcOit2WS3AO8EdiYZBa4EzgMHO4+HvkNYF9VFXAyyVHgMeAicLuflJGk1bdo3Kvq1gV2vX2B4w8CB/sMJUnqx2+oSlKDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNWjRuCc5nOR896tLw/t+PUkl2TiwdkeSM0keT3LjpAeWJC1uKVfudwM3DS8m2Q68GXhqYG03sBe4rjvng0nWTWRSSdKSLRr3qrofeG7Ert8F3gvUwNoe4N6qulBVTwBngOsnMagkaenGuuee5G3A16vqC0O7tgJPD2zPdmuSpFW06A9kD0vyEuD9wM+O2j1irUaskWQ/sB9gx44dyx1DkvQixrly/0HgGuALSZ4EtgGfT/L9zF+pbx84dhtwdtSLVNWhqpququmpqakxxpAkLWTZV+5V9SVg0wvbXeCnq+rZJMeAP0vyAeBqYBfw4IRmlfQidh745FqP0Iwn73rLWo/Q21I+CnkP8I/AK5PMJrltoWOr6iRwFHgM+BRwe1U9P6lhJUlLs+iVe1Xdusj+nUPbB4GD/caSJPXhN1QlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIatJRfYjqc5HySRwfWfjvJl5N8McmfJ3n5wL47kpxJ8niSG1dobknSi1jKlfvdwE1Da8eBV1fVa4CvAHcAJNkN7AWu6875YJJ1E5tWkrQki8a9qu4Hnhta+3RVXew2Pwts657vAe6tqgtV9QRwBrh+gvNKkpZgEvfcfxH4y+75VuDpgX2z3ZokaRX1inuS9wMXgY+8sDTisFrg3P1JZpLMzM3N9RlDkjRk7Lgn2Qe8Ffj5qnoh4LPA9oHDtgFnR51fVYeqarqqpqempsYdQ5I0wlhxT3IT8D7gbVX13wO7jgF7k1yZ5BpgF/Bg/zElScuxfrEDktwDvBHYmGQWuJP5T8dcCRxPAvDZqvqlqjqZ5CjwGPO3a26vqudXanhJ0miLxr2qbh2x/OEXOf4gcLDPUJKkfvyGqiQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoMWjXuSw0nOJ3l0YO2qJMeTnO4eNwzsuyPJmSSPJ7lxpQaXJC1sKVfudwM3Da0dAE5U1S7gRLdNkt3AXuC67pwPJlk3sWklSUuyaNyr6n7guaHlPcCR7vkR4OaB9Xur6kJVPQGcAa6fzKiSpKUa95775qo6B9A9burWtwJPDxw3261JklbRpP9BNSPWauSByf4kM0lm5ubmJjyGJF3exo37M0m2AHSP57v1WWD7wHHbgLOjXqCqDlXVdFVNT01NjTmGJGmUceN+DNjXPd8H3DewvjfJlUmuAXYBD/YbUZK0XOsXOyDJPcAbgY1JZoE7gbuAo0luA54CbgGoqpNJjgKPAReB26vq+RWaXZK0gEXjXlW3LrDrhgWOPwgc7DOUJKkfv6EqSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUoF5xT/KrSU4meTTJPUm+J8lVSY4nOd09bpjUsJKkpRk77km2Ar8CTFfVq4F1wF7gAHCiqnYBJ7ptSdIq6ntbZj3wvUnWAy8BzgJ7gCPd/iPAzT3fQ5K0TGPHvaq+DvwO8z+QfQ74t6r6NLC5qs51x5wDNk1iUEnS0vW5LbOB+av0a4CrgZcmefsyzt+fZCbJzNzc3LhjSJJG6HNb5meAJ6pqrqq+CXwc+AngmSRbALrH86NOrqpDVTVdVdNTU1M9xpAkDesT96eA1yd5SZIANwCngGPAvu6YfcB9/UaUJC3X+nFPrKoHknwU+DxwEXgYOAS8DDia5Dbm/wK4ZRKDSpKWbuy4A1TVncCdQ8sXmL+KlyStEb+hKkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1KBecU/y8iQfTfLlJKeS/HiSq5IcT3K6e9wwqWElSUvT98r994FPVdWrgB9h/jdUDwAnqmoXcKLbliStorHjnuT7gJ8CPgxQVd+oqn8F9gBHusOOADf3G1GStFx9rtxfAcwBf5zk4SQfSvJSYHNVnQPoHjdNYE5J0jL0ift64EeBP6qq1wL/xTJuwSTZn2Qmyczc3FyPMSRJw/rEfRaYraoHuu2PMh/7Z5JsAegez486uaoOVdV0VU1PTU31GEOSNGzsuFfVPwFPJ3llt3QD8BhwDNjXre0D7us1oSRp2db3PP9dwEeSXAF8FXgH839hHE1yG/AUcEvP95AkLVOvuFfVI8D0iF039HldSVI/fkNVkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQb3jnmRdkoeTfKLbvirJ8SSnu8cN/ceUJC3HJK7c3w2cGtg+AJyoql3AiW5bkrSKesU9yTbgLcCHBpb3AEe650eAm/u8hyRp+fpeuf8e8F7gWwNrm6vqHED3uKnne0iSlmnsuCd5K3C+qh4a8/z9SWaSzMzNzY07hiRphD5X7m8A3pbkSeBe4E1J/hR4JskWgO7x/KiTq+pQVU1X1fTU1FSPMSRJw8aOe1XdUVXbqmonsBf4m6p6O3AM2Ncdtg+4r/eUkqRlWYnPud8FvDnJaeDN3bYkaRWtn8SLVNVngM90z/8ZuGESrytJGo/fUJWkBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBo0d9yTbk/xtklNJTiZ5d7d+VZLjSU53jxsmN64kaSn6XLlfBH6tqn4YeD1we5LdwAHgRFXtAk5025KkVTR23KvqXFV9vnv+H8ApYCuwBzjSHXYEuLnnjJKkZZrIPfckO4HXAg8Am6vqHMz/BQBsWuCc/UlmkszMzc1NYgx
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.target.value_counts().plot(kind=\"bar\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "a7a80c15",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|