95 lines
2.1 KiB
Plaintext
95 lines
2.1 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install kaggle\n",
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"!pip install pandas\n",
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"!pip install seaborn\n",
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"!pip install torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1 Pobranie zbioru\n",
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"!kaggle datasets download -d joniarroba/noshowappointments"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!unzip -o noshowappointments.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"no_shows=pd.read_csv('KaggleV2-May-2016.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Wyczyszczenie zbioru\n",
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"# Usunięcie negatywnego wieku\n",
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"no_shows = no_shows.drop(no_shows[no_shows[\"Age\"] < 0].index)\n",
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"\n",
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"# Usunięcie niewiadomego wieku (zależy od zastosowania)\n",
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"# no_shows = no_shows.drop(no_shows[no_shows[\"Age\"] == 0].index)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Normalizacja danych\n",
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"\n",
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"# Usunięcie kolumn PatientId oraz AppointmentID\n",
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"no_shows.drop([\"PatientId\", \"AppointmentID\"], inplace=True, axis=1)\n",
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"\n",
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"# Zmiena wartości kolumny No-show z Yes/No na wartość boolowską\n",
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"no_shows[\"No-show\"] = no_shows[\"No-show\"].map({'Yes': 1, 'No': 0})\n",
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"\n",
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"# Normalizacja kolumny Age\n",
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"no_shows[\"Age\"]=(no_shows[\"Age\"]-no_shows[\"Age\"].min())/(no_shows[\"Age\"].max()-no_shows[\"Age\"].min())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Zapisanie wyników jako artefakt"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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