Find real world raw data: show me to approve your data before you start working on Apply preprocessing techniques cleaning, integration, feature engineering,normalization, scaling visualize numeric...

{
"cells": [
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
"0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
"1 37 1 2 130 250 0 1 187 0 3.5 0 \n",
"2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
"3 56 1 1 120 236 0 1 178 0 0.8 2 \n",
"4 57 0 0 120 354 0 1 163 1 0.6 2 \n",
"\n",
" ca thal target \n",
"0 0 1 1 \n",
"1 0 2 1 \n",
"2 0 2 1 \n",
"3 0 2 1 \n",
"4 0 2 1 \n",
"\n",
"RangeIndex: 303 entries, 0 to 302\n",
"Data columns (total 14 columns):\n",
"age 303 non-null int64\n",
"sex 303 non-null int64\n",
"cp 303 non-null int64\n",
"trestbps 303 non-null int64\n",
"chol 303 non-null int64\n",
"fbs 303 non-null int64\n",
"restecg 303 non-null int64\n",
"thalach 303 non-null int64\n",
"exang 303 non-null int64\n",
"oldpeak 303 non-null float64\n",
"slope 303 non-null int64\n",
"ca 303 non-null int64\n",
"thal 303 non-null int64\n",
"target 303 non-null int64\n",
"dtypes: float64(1), int64(13)\n",
"memory usage: 33.2 KB\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 207\n",
"0 96\n",
"Name: sex, dtype: int64\n"
]
},
{
"data": {
"image/png":...