diff --git a/02_zadanie.ipynb b/02_zadanie.ipynb
deleted file mode 100644
index 3415a76..0000000
--- a/02_zadanie.ipynb
+++ /dev/null
@@ -1,231 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b726950a",
-   "metadata": {},
-   "source": [
-    "<font size=\"5\">**1. Pobieramy wybrany zbiór**</font>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13106acf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --user kaggle \n",
-    "!pip install --user pandas\n",
-    "!kaggle datasets download -d mterzolo/lego-sets\n",
-    "!unzip -o lego-sets.zip"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "661a8c28",
-   "metadata": {},
-   "source": [
-    "<font size=\"5\">**2. Dokonujemy inspekcji danych**</font>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8dc2c5fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "90670da6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wc -l lego_sets.csv\n",
-    "!head -n 5 lego_sets.csv # duzo tekstu w niektorych kolumnach..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e92afb9c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "lego = pd.read_csv('lego_sets.csv')\n",
-    "lego # wglad w strukture elementow i klasy, wielkosc itd."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "824ffb81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lego.describe(include='all') # srednia, odchylenie standardowe itd."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "290de05b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lego[\"theme_name\"].value_counts() # rozklad czestosci dla przykladowej klasy (tematyka zestawu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "151119d7",
-   "metadata": {},
-   "source": [
-    "<font size=\"5\">**3. Preprocessing**</font>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7327e72b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!grep -P \"^$\" -n lego_sets.csv # puste linie - nie ma\n",
-    "!grep -P \",,\" -n lego_sets.csv # puste pola"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e0a4327",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# usuwamy puste pola\n",
-    "lego_all = pd.read_csv('lego_sets.csv').dropna()\n",
-    "lego_all.to_csv('lego_sets_clean.csv', index = None, header=True)\n",
-    "lego_clean = pd.read_csv('lego_sets_clean.csv')\n",
-    "lego_clean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89840c87",
-   "metadata": {},
-   "source": [
-    "<font size=\"5\">**4. Normalizacja**</font>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1f33e04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --user numpy\n",
-    "import numpy as np\n",
-    "\n",
-    "# list_price moze byc do dwoch miejsc po przecinku\n",
-    "lego_clean['list_price'] = lego_clean['list_price'].round(2)\n",
-    "\n",
-    "# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi\n",
-    "lego_clean['num_reviews'] = lego_clean['num_reviews'].apply(np.int64)\n",
-    "lego_clean['piece_count'] = lego_clean['piece_count'].apply(np.int64)\n",
-    "lego_clean['prod_id'] = lego_clean['prod_id'].apply(np.int64)\n",
-    "\n",
-    "# czysto dla przykladu normalizujemy pozostale floaty (chociaz nie trzeba, wszystkie juz sa w tej samej skali)\n",
-    "lego_clean['play_star_rating'] = (lego_clean['play_star_rating']  - lego_clean['play_star_rating'].min() ) / (lego_clean['play_star_rating'].max()  - lego_clean['play_star_rating'].min())\n",
-    "lego_clean['star_rating'] = (lego_clean['star_rating']  - lego_clean['star_rating'].min() ) / (lego_clean['star_rating'].max()  - lego_clean['star_rating'].min())\n",
-    "lego_clean['val_star_rating'] = (lego_clean['val_star_rating']  - lego_clean['val_star_rating'].min() ) / (lego_clean['val_star_rating'].max()  - lego_clean['val_star_rating'].min())\n",
-    "\n",
-    "lego_clean.to_csv('lego_sets_clean_normalised.csv', index = None, header=True)\n",
-    "lego_clean_normalised = pd.read_csv('lego_sets_clean_normalised.csv')\n",
-    "lego_clean_normalised"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "739ea946",
-   "metadata": {},
-   "source": [
-    "<font size=\"5\">**5. Podział na podzbiory**</font>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ed5b5bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --user sklearn\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# pierwszy podzial, wydzielamy zbior treningowy\n",
-    "lego_train, lego_rem = train_test_split(lego_clean_normalised, train_size=0.8, random_state=1)\n",
-    "\n",
-    "# drugi podział, wydzielamy walidacyjny i testowy\n",
-    "lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5, random_state=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d0bdaf9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lego_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc151dc5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lego_valid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d6ba0fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lego_test"
-   ]
-  }
- ],
- "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": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}