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Kacper Dudzic 2022-03-20 17:18:31 +01:00
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{
"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
}