diff --git a/.gitignore b/.gitignore
index f73806e..d15b344 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-.ipynb_checkpoints/
\ No newline at end of file
+.ipynb_checkpoints/
+not_in_repo/
\ No newline at end of file
diff --git a/body_performance.ipynb b/body_performance.ipynb
deleted file mode 100644
index 8b38429..0000000
--- a/body_performance.ipynb
+++ /dev/null
@@ -1,274 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "74524ede",
-   "metadata": {},
-   "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(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())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0177f243",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.duplicated().sum()\n",
-    "print(f'with duplicates:{df.shape}')\n",
-    "df.drop_duplicates(inplace=True)\n",
-    "print(f'without duplicates:{df.shape}')\n",
-    "df_copy = df.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8abefe6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "body_train, body_test = train_test_split(df, test_size=int(df[\"age\"].count()*0.2), random_state=1)\n",
-    "body_test, body_valid = train_test_split(body_test, test_size=int(body_test[\"age\"].count()*0.5), random_state=1)\n",
-    "\n",
-    "print(\"number of elements in data frame: {}\".format(df['age'].count()))\n",
-    "print(\"train: {}\".format(body_train[\"age\"].count()))\n",
-    "print(\"test: {}\".format(body_test[\"age\"].count()))\n",
-    "print(\"valid: {}\".format(body_valid[\"age\"].count()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f3ad57a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(df.describe(include='all'))\n",
-    "#sit and bend forward_cm jest na minusie!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b694be50",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler = MinMaxScaler()\n",
-    "df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "    'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "    'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
-    "    'age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "    'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "    'broad jump_cm','BMI']])\n",
-    "\n",
-    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
-    "df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
-    "df.describe(include='all')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5cd376cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2375b677",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))\n",
-    "print('Each class in train data: \\n{}'.format(body_train['class'].value_counts()))\n",
-    "print('Each class in test data: \\n{}'.format(body_test['class'].value_counts()))\n",
-    "print('Each class in valid data: \\n{}'.format(body_valid['class'].value_counts()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "781b7e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "225a3cd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4857a167",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df[\"class\"].value_counts().plot(kind=\"bar\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "779157c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da14bf43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sns.set_theme()\n",
-    "\n",
-    "#sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6597e57c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "957e1b2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f0394f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#px.box(df, y=['height_cm',\n",
-    "#               'weight_kg',\n",
-    "#               'body fat_%',\n",
-    "#               'diastolic',\n",
-    "#               'systolic',\n",
-    "#               'gripForce',\n",
-    "#               'sit and bend forward_cm',\n",
-    "#               'sit-ups counts',\n",
-    "#               'broad jump_cm',\n",
-    "#               'BMI'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22542bba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this is taking too long time\n",
-    "#sns.pairplot(data=df.drop(columns=[\"gender\"]).head(500), hue=\"class\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29730d20",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc21a9cb",
-   "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": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/python_dataset_create.ipynb b/python_dataset_create.ipynb
deleted file mode 100644
index d74524c..0000000
--- a/python_dataset_create.ipynb
+++ /dev/null
@@ -1,143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "441faeb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "usage: ipykernel_launcher.py [-h] [CUTOFF]\n",
-      "ipykernel_launcher.py: error: argument CUTOFF: invalid int value: 'C:\\\\Users\\\\kubar\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-79d59646-528a-4bb1-b035-367b9dda658f.json'\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2488\u001b[0m, in \u001b[0;36mArgumentParser._get_value\u001b[1;34m(self, action, arg_string)\u001b[0m\n\u001b[0;32m   2487\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2488\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mtype_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_string\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2490\u001b[0m \u001b[38;5;66;03m# ArgumentTypeErrors indicate errors\u001b[39;00m\n",
-      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'C:\\\\Users\\\\kubar\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-79d59646-528a-4bb1-b035-367b9dda658f.json'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:1859\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[1;34m(self, args, namespace)\u001b[0m\n\u001b[0;32m   1858\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1859\u001b[0m     namespace, args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1860\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ArgumentError:\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2075\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args\u001b[1;34m(self, arg_strings, namespace)\u001b[0m\n\u001b[0;32m   2074\u001b[0m \u001b[38;5;66;03m# consume any positionals following the last Optional\u001b[39;00m\n\u001b[1;32m-> 2075\u001b[0m stop_index \u001b[38;5;241m=\u001b[39m \u001b[43mconsume_positionals\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2077\u001b[0m \u001b[38;5;66;03m# if we didn't consume all the argument strings, there were extras\u001b[39;00m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2031\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args.<locals>.consume_positionals\u001b[1;34m(start_index)\u001b[0m\n\u001b[0;32m   2030\u001b[0m     start_index \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m arg_count\n\u001b[1;32m-> 2031\u001b[0m     \u001b[43mtake_action\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2033\u001b[0m \u001b[38;5;66;03m# slice off the Positionals that we just parsed and return the\u001b[39;00m\n\u001b[0;32m   2034\u001b[0m \u001b[38;5;66;03m# index at which the Positionals' string args stopped\u001b[39;00m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:1920\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args.<locals>.take_action\u001b[1;34m(action, argument_strings, option_string)\u001b[0m\n\u001b[0;32m   1919\u001b[0m seen_actions\u001b[38;5;241m.\u001b[39madd(action)\n\u001b[1;32m-> 1920\u001b[0m argument_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margument_strings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# error if this argument is not allowed with other previously\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# seen arguments, assuming that actions that use the default\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# value don't really count as \"present\"\u001b[39;00m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2455\u001b[0m, in \u001b[0;36mArgumentParser._get_values\u001b[1;34m(self, action, arg_strings)\u001b[0m\n\u001b[0;32m   2454\u001b[0m arg_string, \u001b[38;5;241m=\u001b[39m arg_strings\n\u001b[1;32m-> 2455\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg_string\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2456\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_value(action, value)\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2501\u001b[0m, in \u001b[0;36mArgumentParser._get_value\u001b[1;34m(self, action, arg_string)\u001b[0m\n\u001b[0;32m   2500\u001b[0m     msg \u001b[38;5;241m=\u001b[39m _(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minvalid \u001b[39m\u001b[38;5;132;01m%(type)s\u001b[39;00m\u001b[38;5;124m value: \u001b[39m\u001b[38;5;132;01m%(value)r\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 2501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ArgumentError(action, msg \u001b[38;5;241m%\u001b[39m args)\n\u001b[0;32m   2503\u001b[0m \u001b[38;5;66;03m# return the converted value\u001b[39;00m\n",
-      "\u001b[1;31mArgumentError\u001b[0m: argument CUTOFF: invalid int value: 'C:\\\\Users\\\\kubar\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-79d59646-528a-4bb1-b035-367b9dda658f.json'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mSystemExit\u001b[0m                                Traceback (most recent call last)",
-      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "Cell \u001b[1;32mIn[3], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m parser\u001b[38;5;241m.\u001b[39madd_argument(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCUTOFF\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m, nargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m'\u001b[39m, default\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, help\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHow many rows to take\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 14\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m args\u001b[38;5;241m.\u001b[39mCUTOFF \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:1826\u001b[0m, in \u001b[0;36mArgumentParser.parse_args\u001b[1;34m(self, args, namespace)\u001b[0m\n\u001b[0;32m   1825\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_args\u001b[39m(\u001b[38;5;28mself\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m-> 1826\u001b[0m     args, argv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m argv:\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:1862\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[1;34m(self, args, namespace)\u001b[0m\n\u001b[0;32m   1861\u001b[0m         err \u001b[38;5;241m=\u001b[39m _sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m-> 1862\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1863\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2587\u001b[0m, in \u001b[0;36mArgumentParser.error\u001b[1;34m(self, message)\u001b[0m\n\u001b[0;32m   2586\u001b[0m args \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprog\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprog, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m: message}\n\u001b[1;32m-> 2587\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%(prog)s\u001b[39;49;00m\u001b[38;5;124;43m: error: \u001b[39;49m\u001b[38;5;132;43;01m%(message)s\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\argparse.py:2574\u001b[0m, in \u001b[0;36mArgumentParser.exit\u001b[1;34m(self, status, message)\u001b[0m\n\u001b[0;32m   2573\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_message(message, _sys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[1;32m-> 2574\u001b[0m \u001b[43m_sys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[1;31mSystemExit\u001b[0m: 2",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2047\u001b[0m, in \u001b[0;36mInteractiveShell.showtraceback\u001b[1;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[0;32m   2044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception_only:\n\u001b[0;32m   2045\u001b[0m     stb \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAn exception has occurred, use \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mtb to see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2046\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe full traceback.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m-> 2047\u001b[0m     stb\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInteractiveTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_exception_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2048\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   2049\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2050\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   2051\u001b[0m         \u001b[38;5;66;03m# Exception classes can customise their traceback - we\u001b[39;00m\n\u001b[0;32m   2052\u001b[0m         \u001b[38;5;66;03m# use this in IPython.parallel for exceptions occurring\u001b[39;00m\n\u001b[0;32m   2053\u001b[0m         \u001b[38;5;66;03m# in the engines. This should return a list of strings.\u001b[39;00m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:585\u001b[0m, in \u001b[0;36mListTB.get_exception_only\u001b[1;34m(self, etype, value)\u001b[0m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_exception_only\u001b[39m(\u001b[38;5;28mself\u001b[39m, etype, value):\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;124;03m\"\"\"Only print the exception type and message, without a traceback.\u001b[39;00m\n\u001b[0;32m    579\u001b[0m \n\u001b[0;32m    580\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    583\u001b[0m \u001b[38;5;124;03m    value : exception value\u001b[39;00m\n\u001b[0;32m    584\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 585\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mListTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:452\u001b[0m, in \u001b[0;36mListTB.structured_traceback\u001b[1;34m(self, etype, evalue, etb, tb_offset, context)\u001b[0m\n\u001b[0;32m    449\u001b[0m     chained_exc_ids\u001b[38;5;241m.\u001b[39madd(\u001b[38;5;28mid\u001b[39m(exception[\u001b[38;5;241m1\u001b[39m]))\n\u001b[0;32m    450\u001b[0m     chained_exceptions_tb_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    451\u001b[0m     out_list \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 452\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    453\u001b[0m \u001b[43m            \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchained_exc_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    454\u001b[0m \u001b[43m            \u001b[49m\u001b[43mchained_exceptions_tb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    455\u001b[0m         \u001b[38;5;241m+\u001b[39m chained_exception_message\n\u001b[0;32m    456\u001b[0m         \u001b[38;5;241m+\u001b[39m out_list)\n\u001b[0;32m    458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out_list\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:1118\u001b[0m, in \u001b[0;36mAutoFormattedTB.structured_traceback\u001b[1;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m   1116\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1117\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtb \u001b[38;5;241m=\u001b[39m tb\n\u001b[1;32m-> 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mFormattedTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1119\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:1012\u001b[0m, in \u001b[0;36mFormattedTB.structured_traceback\u001b[1;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m   1009\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose_modes:\n\u001b[0;32m   1011\u001b[0m     \u001b[38;5;66;03m# Verbose modes need a full traceback\u001b[39;00m\n\u001b[1;32m-> 1012\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVerboseTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1013\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[0;32m   1014\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1015\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMinimal\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m   1016\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ListTB\u001b[38;5;241m.\u001b[39mget_exception_only(\u001b[38;5;28mself\u001b[39m, etype, value)\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:865\u001b[0m, in \u001b[0;36mVerboseTB.structured_traceback\u001b[1;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m    856\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstructured_traceback\u001b[39m(\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    858\u001b[0m     etype: \u001b[38;5;28mtype\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    862\u001b[0m     number_of_lines_of_context: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m,\n\u001b[0;32m    863\u001b[0m ):\n\u001b[0;32m    864\u001b[0m     \u001b[38;5;124;03m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 865\u001b[0m     formatted_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_exception_as_a_whole\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    866\u001b[0m \u001b[43m                                                           \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    868\u001b[0m     colors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mColors  \u001b[38;5;66;03m# just a shorthand + quicker name lookup\u001b[39;00m\n\u001b[0;32m    869\u001b[0m     colorsnormal \u001b[38;5;241m=\u001b[39m colors\u001b[38;5;241m.\u001b[39mNormal  \u001b[38;5;66;03m# used a lot\u001b[39;00m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:799\u001b[0m, in \u001b[0;36mVerboseTB.format_exception_as_a_whole\u001b[1;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[0;32m    796\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tb_offset, \u001b[38;5;28mint\u001b[39m)\n\u001b[0;32m    797\u001b[0m head \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_header(etype, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlong_header)\n\u001b[0;32m    798\u001b[0m records \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 799\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_records\u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m etb \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[0;32m    800\u001b[0m )\n\u001b[0;32m    802\u001b[0m frames \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m    803\u001b[0m skipped \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\IPython\\core\\ultratb.py:854\u001b[0m, in \u001b[0;36mVerboseTB.get_records\u001b[1;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[0;32m    848\u001b[0m     formatter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    849\u001b[0m options \u001b[38;5;241m=\u001b[39m stack_data\u001b[38;5;241m.\u001b[39mOptions(\n\u001b[0;32m    850\u001b[0m     before\u001b[38;5;241m=\u001b[39mbefore,\n\u001b[0;32m    851\u001b[0m     after\u001b[38;5;241m=\u001b[39mafter,\n\u001b[0;32m    852\u001b[0m     pygments_formatter\u001b[38;5;241m=\u001b[39mformatter,\n\u001b[0;32m    853\u001b[0m )\n\u001b[1;32m--> 854\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstack_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFrameInfo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m[tb_offset:]\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\stack_data\\core.py:546\u001b[0m, in \u001b[0;36mFrameInfo.stack_data\u001b[1;34m(cls, frame_or_tb, options, collapse_repeated_frames)\u001b[0m\n\u001b[0;32m    530\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    531\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack_data\u001b[39m(\n\u001b[0;32m    532\u001b[0m         \u001b[38;5;28mcls\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    536\u001b[0m         collapse_repeated_frames: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    537\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFrameInfo\u001b[39m\u001b[38;5;124m'\u001b[39m, RepeatedFrames]]:\n\u001b[0;32m    538\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    539\u001b[0m \u001b[38;5;124;03m    An iterator of FrameInfo and RepeatedFrames objects representing\u001b[39;00m\n\u001b[0;32m    540\u001b[0m \u001b[38;5;124;03m    a full traceback or stack. Similar consecutive frames are collapsed into RepeatedFrames\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    544\u001b[0m \u001b[38;5;124;03m    and optionally an Options object to configure.\u001b[39;00m\n\u001b[0;32m    545\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 546\u001b[0m     stack \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miter_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe_or_tb\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    548\u001b[0m     \u001b[38;5;66;03m# Reverse the stack from a frame so that it's in the same order\u001b[39;00m\n\u001b[0;32m    549\u001b[0m     \u001b[38;5;66;03m# as the order from a traceback, which is the order of a printed\u001b[39;00m\n\u001b[0;32m    550\u001b[0m     \u001b[38;5;66;03m# traceback when read top to bottom (most recent call last)\u001b[39;00m\n\u001b[0;32m    551\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_frame(frame_or_tb):\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\stack_data\\utils.py:98\u001b[0m, in \u001b[0;36miter_stack\u001b[1;34m(frame_or_tb)\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m frame_or_tb:\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m frame_or_tb\n\u001b[1;32m---> 98\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mis_frame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe_or_tb\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m     99\u001b[0m         frame_or_tb \u001b[38;5;241m=\u001b[39m frame_or_tb\u001b[38;5;241m.\u001b[39mf_back\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\stack_data\\utils.py:91\u001b[0m, in \u001b[0;36mis_frame\u001b[1;34m(frame_or_tb)\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_frame\u001b[39m(frame_or_tb: Union[FrameType, TracebackType]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[1;32m---> 91\u001b[0m     \u001b[43massert_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mframe_or_tb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFrameType\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTracebackType\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     92\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(frame_or_tb, (types\u001b[38;5;241m.\u001b[39mFrameType,))\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\stack_data\\utils.py:172\u001b[0m, in \u001b[0;36massert_\u001b[1;34m(condition, error)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(error, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    171\u001b[0m     error \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mAssertionError\u001b[39;00m(error)\n\u001b[1;32m--> 172\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error\n",
-      "\u001b[1;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import seaborn as sns\n",
-    "import os\n",
-    "import sys\n",
-    "import argparse\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "\n",
-    "\n",
-    "parser = argparse.ArgumentParser(description='Program to create dataset')\n",
-    "parser.add_argument('CUTOFF', type=int, nargs='?', default=0, help='How many rows to take')\n",
-    "\n",
-    "args = parser.parse_args()\n",
-    "\n",
-    "if args.CUTOFF != 0:\n",
-    "    df = pd.read_csv(os.path.join('.', 'body_performance.csv'), nrows = args.CUTOFF)\n",
-    "else:\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",
-    "df.duplicated().sum()\n",
-    "df.drop_duplicates(inplace=True)\n",
-    "df_copy = df.copy()\n",
-    "\n",
-    "scaler = MinMaxScaler()\n",
-    "df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "    'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "    'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
-    "    'age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "    'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "    'broad jump_cm','BMI']])\n",
-    "\n",
-    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
-    "df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
-    "\n",
-    "df.to_csv('body_performance_processed.csv', index=False)\n",
-    "\n",
-    "if os.environ.get('JENKINS_HOME'):\n",
-    "    artifacts_dir = os.path.join(os.environ.get('WORKSPACE'), 'artifacts')\n",
-    "    os.makedirs(artifacts_dir, exist_ok=True)\n",
-    "    artifacts_path = os.path.join(artifacts_dir, csv_filename)\n",
-    "    os.rename(csv_filename, artifacts_path)\n",
-    "    print(f\"Stworzono plik CSV: {artifacts_path}\")\n",
-    "else:\n",
-    "    print(f\"Stworzono plik CSV: body_performance_processed.csv\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2f23d54",
-   "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": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/python_dataset_create_stats.ipynb b/python_dataset_create_stats.ipynb
deleted file mode 100644
index 55d066e..0000000
--- a/python_dataset_create_stats.ipynb
+++ /dev/null
@@ -1,91 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a88b729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import seaborn as sns\n",
-    "import os\n",
-    "import sys\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "\n",
-    "\n",
-    "arg = sys.argv[1]\n",
-    "\n",
-    "# body_train =  pd.DataFrame()\n",
-    "# body_test = pd.DataFrame()\n",
-    "# body_valid = pd.DataFrame()\n",
-    "\n",
-    "if arg == 'data_create':\n",
-    "    df = pd.read_csv(os.path.join('.', 'body_performance.csv'), nrows = int(arg))\n",
-    "\n",
-    "    df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
-    "    df.duplicated().sum()\n",
-    "    df.drop_duplicates(inplace=True)\n",
-    "    df_copy = df.copy()\n",
-    "\n",
-    "    scaler = MinMaxScaler()\n",
-    "    df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "        'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "        'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
-    "        'age', 'height_cm', 'weight_kg','body fat_%',\n",
-    "        'diastolic','systolic','gripForce','sit-ups counts',\n",
-    "        'broad jump_cm','BMI']])\n",
-    "\n",
-    "    scaler = MinMaxScaler(feature_range=(-1, 1))\n",
-    "    df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
-    "\n",
-    "#     body_train, body_test = train_test_split(df, test_size=int(df[\"age\"].count()*0.2), random_state=1)\n",
-    "#     body_test, body_valid = train_test_split(body_test, test_size=int(body_test[\"age\"].count()*0.5), random_state=1)\n",
-    "\n",
-    "elif arg == 'data_stats':\n",
-    "    \n",
-    "    print(df.head())\n",
-    "    \n",
-    "    print(\"number of elements in data frame: {}\".format(df['age'].count()))\n",
-    "#     print(\"train: {}\".format(body_train[\"age\"].count()))\n",
-    "#     print(\"test: {}\".format(body_test[\"age\"].count()))\n",
-    "#     print(\"valid: {}\".format(body_valid[\"age\"].count()))\n",
-    "    \n",
-    "    print(df.describe(include='all'))\n",
-    "    \n",
-    "    print(df.info())\n",
-    "    \n",
-    "    print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))\n",
-    "#     print('Each class in train data: \\n{}'.format(body_train['class'].value_counts()))\n",
-    "#     print('Each class in test data: \\n{}'.format(body_test['class'].value_counts()))\n",
-    "#     print('Each class in valid data: \\n{}'.format(body_valid['class'].value_counts()))\n",
-    "\n",
-    "else:\n",
-    "    print(\"There is no command to run skript like: {}\".format(arg))"
-   ]
-  }
- ],
- "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": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/python_dataset_stats.ipynb b/python_dataset_stats.ipynb
deleted file mode 100644
index 095217b..0000000
--- a/python_dataset_stats.ipynb
+++ /dev/null
@@ -1,75 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "37dcf406",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '.\\\\body_performance_processed.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbody_performance_processed.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mhead())\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumber of elements in data frame: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mcount()))\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '.\\\\body_performance_processed.csv'"
-     ]
-    }
-   ],
-   "source": [
-    "    import pandas as pd\n",
-    "    import os\n",
-    "    \n",
-    "    df = pd.read_csv(os.path.join('.', 'body_performance_processed.csv'))\n",
-    "    \n",
-    "    print(df.head())\n",
-    "    \n",
-    "    print(\"number of elements in data frame: {}\".format(df['age'].count()))\n",
-    "    \n",
-    "    print(df.describe(include='all'))\n",
-    "    \n",
-    "    print(df.info())\n",
-    "    \n",
-    "    print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8fbafa56",
-   "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": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}