291 lines
7.5 KiB
Plaintext
291 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c95fbd16-09ed-497b-892a-473496150996",
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"metadata": {},
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"source": [
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"<h1>Cleaning</h1>\n",
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"<p>Import dataset using the ucirepo package</p>"
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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": 1,
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"id": "3eb339fa-ef85-4544-9ad0-bc22d4de9f1a",
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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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"</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>goal</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>145</td>\n",
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" <td>233</td>\n",
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" <td>1</td>\n",
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" <td>2</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>3</td>\n",
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" <td>0.0</td>\n",
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" <td>6.0</td>\n",
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" <td>0</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>67</td>\n",
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" <td>1</td>\n",
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" <td>1.5</td>\n",
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" <td>2</td>\n",
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" <td>3.0</td>\n",
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" <td>3.0</td>\n",
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" <td>2</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>67</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>120</td>\n",
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" <td>2.6</td>\n",
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" <td>2</td>\n",
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" <td>2.0</td>\n",
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" <td>7.0</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>37</td>\n",
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" <td>3</td>\n",
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" <td>130</td>\n",
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" <td>0</td>\n",
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" <td>0</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>3</td>\n",
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" <td>0.0</td>\n",
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" <td>3.0</td>\n",
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" <td>0</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>41</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>1.4</td>\n",
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" <td>1</td>\n",
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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 1 145 233 1 2 150 0 2.3 3 \n",
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"1 67 1 4 160 286 0 2 108 1 1.5 2 \n",
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"2 67 1 4 120 229 0 2 129 1 2.6 2 \n",
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"3 37 1 3 130 250 0 0 187 0 3.5 3 \n",
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"4 41 0 2 130 204 0 2 172 0 1.4 1 \n",
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"\n",
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" ca thal goal \n",
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"0 0.0 6.0 0 \n",
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"1 3.0 3.0 2 \n",
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"2 2.0 7.0 1 \n",
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"3 0.0 3.0 0 \n",
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"4 0.0 3.0 0 "
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]
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},
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"execution_count": 1,
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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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"from ucimlrepo import fetch_ucirepo\n",
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"import pandas as pd\n",
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"\n",
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"# fetch dataset \n",
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"heart_disease = fetch_ucirepo(id=45) \n",
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" \n",
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"# data (as pandas dataframes) \n",
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"X = heart_disease.data.features \n",
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"y = heart_disease.data.targets \n",
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"\n",
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"df = pd.concat([X, y], axis=1)\n",
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"df = df.rename(columns={'num':'goal'})\n",
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"\n",
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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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"id": "8c5ab8b9-e46a-4968-b0c8-fe393f093f73",
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"metadata": {},
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"source": [
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"<p>Get overview of all missing values. As there are only a few, those rows can be dropped.</p>"
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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": "6f7e6a3a-63cb-40e2-8746-937c24b184ef",
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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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"age 0\n",
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"sex 0\n",
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"cp 0\n",
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"trestbps 0\n",
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"chol 0\n",
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"fbs 0\n",
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"restecg 0\n",
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"thalach 0\n",
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"exang 0\n",
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"oldpeak 0\n",
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"slope 0\n",
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"ca 4\n",
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"thal 2\n",
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"goal 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 2,
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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.isna().sum()"
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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": "d1639e92-d401-49fb-a1f1-67250ffa2c81",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.dropna(inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "192da26d-0fb1-4b06-a046-a41b66576ed0",
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"metadata": {},
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"source": [
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"<h1>Preprocessing</h1>\n",
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"<p>Split</p>"
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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": "24675f41-d48f-4e27-a3d8-e303556ee7d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"df['goal'] = df['goal'].replace({0: 0, 1: 1, 2: 1, 3: 1, 4: 1})"
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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": 5,
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"id": "d7bf2c46-7885-4dfe-a4e7-8b8439cf0434",
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"metadata": {},
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"outputs": [],
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"source": [
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"# save 'cleaned' dataset as csv file for further processing\n",
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"import os\n",
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"os.makedirs('data', exist_ok=True)\n",
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"df.to_csv('./data/dataset_cleaned.csv', index=False)"
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]
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}
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],
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