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s487178 2023-04-05 20:23:36 +02:00
parent 9ff871c91b
commit cbae3528d0
4 changed files with 67 additions and 12 deletions
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34
.ipynb_checkpoints/body_performance-checkpoint.ipynb
View File

@ -2,18 +2,46 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "74524ede",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" age gender height_cm weight_kg body fat_% diastolic systolic \\\n",
"0 27.0 M 172.3 75.24 21.3 80.0 130.0 \n",
"1 25.0 M 165.0 55.80 15.7 77.0 126.0 \n",
"2 31.0 M 179.6 78.00 20.1 92.0 152.0 \n",
"3 32.0 M 174.5 71.10 18.4 76.0 147.0 \n",
"4 28.0 M 173.8 67.70 17.1 70.0 127.0 \n",
"\n",
" gripForce sit and bend forward_cm sit-ups counts broad jump_cm class \\\n",
"0 54.9 18.4 60.0 217.0 C \n",
"1 36.4 16.3 53.0 229.0 A \n",
"2 44.8 12.0 49.0 181.0 C \n",
"3 41.4 15.2 53.0 219.0 B \n",
"4 43.5 27.1 45.0 217.0 B \n",
"\n",
" BMI \n",
"0 25.344179 \n",
"1 20.495868 \n",
"2 24.181428 \n",
"3 23.349562 \n",
"4 22.412439 \n"
]
}
],
"source": [
"import pandas as pd\n",
"import plotly.express as px\n",
"import seaborn as sns\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"df = pd.read_csv(r'.\\body_performance.csv')\n",
"df = pd.read_csv(os.path.join('.', 'body_performance.csv'))\n",
"\n",
"df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
"print(df.head())"

34
body_performance.ipynb
View File

@ -2,18 +2,46 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "74524ede",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" age gender height_cm weight_kg body fat_% diastolic systolic \\\n",
"0 27.0 M 172.3 75.24 21.3 80.0 130.0 \n",
"1 25.0 M 165.0 55.80 15.7 77.0 126.0 \n",
"2 31.0 M 179.6 78.00 20.1 92.0 152.0 \n",
"3 32.0 M 174.5 71.10 18.4 76.0 147.0 \n",
"4 28.0 M 173.8 67.70 17.1 70.0 127.0 \n",
"\n",
" gripForce sit and bend forward_cm sit-ups counts broad jump_cm class \\\n",
"0 54.9 18.4 60.0 217.0 C \n",
"1 36.4 16.3 53.0 229.0 A \n",
"2 44.8 12.0 49.0 181.0 C \n",
"3 41.4 15.2 53.0 219.0 B \n",
"4 43.5 27.1 45.0 217.0 B \n",
"\n",
" BMI \n",
"0 25.344179 \n",
"1 20.495868 \n",
"2 24.181428 \n",
"3 23.349562 \n",
"4 22.412439 \n"
]
}
],
"source": [
"import pandas as pd\n",
"import plotly.express as px\n",
"import seaborn as sns\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"df = pd.read_csv(r'.\\body_performance.csv')\n",
"df = pd.read_csv(os.path.join('.', 'body_performance.csv'))\n",
"\n",
"df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
"print(df.head())"

8
body_performance.py
View File

@ -1,19 +1,17 @@
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# In[1]:
import pandas as pd
import plotly.express as px
import seaborn as sns
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
try:
df = pd.read_csv(r'.\body_performance.csv')
except:
df = pd.read_csv(r'./body_performance.csv')
df = pd.read_csv(os.path.join('.', 'body_performance.csv'))
df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])
print(df.head())

3
dockerfile3
View File

@ -9,7 +9,8 @@ RUN apt-get update \
&& pip install --user pandas \
&& pip install --user plotly \
&& pip install --user seaborn \
&& pip install --user scikit-learn
&& pip install --user scikit-learn \
&& pip install --user os
ENV PATH="/root/.local/bin:$PATH"