228 lines
18 KiB
Plaintext
228 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a3a7634f",
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"metadata": {},
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"source": [
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"# Machine Learning project in SoSe 2025 at HTW Saar\n",
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"## Idea\n",
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"The goal of this project is getting the genre(s) of a game trough its given metadata\n",
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"\n",
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"## Dataset\n",
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"For our project we use a Steam DataSet from kaggle. You can find it under the following URL: [Kaggle.com](https://www.kaggle.com/datasets/artermiloff/steam-games-dataset/data)"
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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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"id": "3116b75f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" appid name release_date required_age price \\\n",
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"0 730 Counter-Strike 2 2012-08-21 0 0.00 \n",
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"1 578080 PUBG: BATTLEGROUNDS 2017-12-21 0 0.00 \n",
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"2 570 Dota 2 2013-07-09 0 0.00 \n",
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"3 271590 Grand Theft Auto V Legacy 2015-04-13 17 0.00 \n",
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"4 359550 Tom Clancy's Rainbow Six® Siege 2015-12-01 17 3.99 \n",
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"\n",
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" dlc_count detailed_description \\\n",
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"0 1 For over two decades, Counter-Strike has offer... \n",
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"1 0 LAND, LOOT, SURVIVE! Play PUBG: BATTLEGROUNDS ... \n",
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"2 2 The most-played game on Steam. Every day, mill... \n",
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"3 0 When a young street hustler, a retired bank ro... \n",
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"4 9 Edition Comparison Ultimate Edition The Tom Cl... \n",
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"\n",
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" about_the_game \\\n",
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"0 For over two decades, Counter-Strike has offer... \n",
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"1 LAND, LOOT, SURVIVE! Play PUBG: BATTLEGROUNDS ... \n",
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"2 The most-played game on Steam. Every day, mill... \n",
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"3 When a young street hustler, a retired bank ro... \n",
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"4 “One of the best first-person shooters ever ma... \n",
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"\n",
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" short_description \\\n",
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"0 For over two decades, Counter-Strike has offer... \n",
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"1 Play PUBG: BATTLEGROUNDS for free. Land on str... \n",
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"2 Every day, millions of players worldwide enter... \n",
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"3 Grand Theft Auto V for PC offers players the o... \n",
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"4 Tom Clancy's Rainbow Six® Siege is an elite, t... \n",
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"\n",
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" reviews ... \\\n",
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"0 NaN ... \n",
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"1 NaN ... \n",
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"2 “A modern multiplayer masterpiece.” 9.5/10 – D... ... \n",
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"3 NaN ... \n",
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"4 NaN ... \n",
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"\n",
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" average_playtime_2weeks median_playtime_forever median_playtime_2weeks \\\n",
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"0 879 5174 350 \n",
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"1 0 0 0 \n",
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"2 1536 898 892 \n",
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"3 771 7101 74 \n",
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"4 682 2434 306 \n",
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"\n",
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" discount peak_ccu tags \\\n",
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"0 0 1212356 {'FPS': 90857, 'Shooter': 65397, 'Multiplayer'... \n",
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"1 0 616738 {'Survival': 14838, 'Shooter': 12727, 'Battle ... \n",
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"2 0 555977 {'Free to Play': 59933, 'MOBA': 20158, 'Multip... \n",
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"3 0 117698 {'Open World': 32644, 'Action': 23539, 'Multip... \n",
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"4 80 89916 {'FPS': 9831, 'PvP': 9162, 'e-sports': 9072, '... \n",
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"\n",
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" pct_pos_total num_reviews_total pct_pos_recent num_reviews_recent \n",
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"0 86 8632939 82 96473 \n",
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"1 59 2513842 68 16720 \n",
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"2 81 2452595 80 29366 \n",
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"3 87 1803832 92 17517 \n",
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"4 84 1168020 76 12608 \n",
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"\n",
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"[5 rows x 47 columns]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"# load data\n",
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"# appid,name,release_date,required_age,price,dlc_count,detailed_description,about_the_game,short_description,reviews,header_image,website,support_url,support_email,windows,mac,linux,metacritic_score,metacritic_url,achievements,recommendations,notes,supported_languages,full_audio_languages,packages,developers,publishers,categories,genres,screenshots,movies,user_score,score_rank,positive,negative,estimated_owners,average_playtime_forever,average_playtime_2weeks,median_playtime_forever,median_playtime_2weeks,discount,peak_ccu,tags,pct_pos_total,num_reviews_total,pct_pos_recent,num_reviews_recent\n",
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"dataset = pd.read_csv(\"./games_march2025_cleaned.csv\",sep=\",\")\n",
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"print(dataset.head())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cba9750a",
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"metadata": {},
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"source": [
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"## Preparation of the Training-Set\n",
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"### Removing Uniques\n",
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"We remove the following features from the Training-Set as they can uniquely identify a datapoint:\n",
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"- AppId\n",
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"- Name of the Game\n",
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"- Realease Date\n",
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"- Reviews\n",
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"- Header Image\n",
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"- Website\n",
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"- Support URL\n",
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"- Support Email\n",
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"- MetaCritic URL\n",
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"- Developer\n",
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"- Publisher\n",
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"- Screenshots\n",
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"- Movies\n",
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"- Estimated Owners"
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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": 4,
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"id": "06dedcdf",
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyError",
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"evalue": "\"['developer', 'publisher'] not found in axis\"",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mKeyError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# appid,name,release_date,required_age,price,dlc_count,detailed_description,about_the_game,short_description,reviews,header_image,website,support_url,support_email,windows,mac,linux,metacritic_score,metacritic_url,achievements,recommendations,notes,supported_languages,full_audio_languages,packages,developers,publishers,categories,genres,screenshots,movies,user_score,score_rank,positive,negative,estimated_owners,average_playtime_forever,average_playtime_2weeks,median_playtime_forever,median_playtime_2weeks,discount,peak_ccu,tags,pct_pos_total,num_reviews_total,pct_pos_recent,num_reviews_recent\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mappid\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mname\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrelease_date\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mreviews\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mheader_image\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mwebsite\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43msupport_url\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43msupport_email\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmetacritic_url\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdeveloper\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpublisher\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mscreenshots\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmovies\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mestimated_owners\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(dataset.head())\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:5581\u001b[39m, in \u001b[36mDataFrame.drop\u001b[39m\u001b[34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[39m\n\u001b[32m 5433\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdrop\u001b[39m(\n\u001b[32m 5434\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 5435\u001b[39m labels: IndexLabel | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 5442\u001b[39m errors: IgnoreRaise = \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 5443\u001b[39m ) -> DataFrame | \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 5444\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 5445\u001b[39m \u001b[33;03m Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[32m 5446\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 5579\u001b[39m \u001b[33;03m weight 1.0 0.8\u001b[39;00m\n\u001b[32m 5580\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m5581\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5582\u001b[39m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5583\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5584\u001b[39m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5585\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5586\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5587\u001b[39m \u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m=\u001b[49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5588\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5589\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4788\u001b[39m, in \u001b[36mNDFrame.drop\u001b[39m\u001b[34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[39m\n\u001b[32m 4786\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes.items():\n\u001b[32m 4787\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m labels \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[32m-> \u001b[39m\u001b[32m4788\u001b[39m obj = \u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4790\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[32m 4791\u001b[39m \u001b[38;5;28mself\u001b[39m._update_inplace(obj)\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4830\u001b[39m, in \u001b[36mNDFrame._drop_axis\u001b[39m\u001b[34m(self, labels, axis, level, errors, only_slice)\u001b[39m\n\u001b[32m 4828\u001b[39m new_axis = axis.drop(labels, level=level, errors=errors)\n\u001b[32m 4829\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m4830\u001b[39m new_axis = \u001b[43maxis\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4831\u001b[39m indexer = axis.get_indexer(new_axis)\n\u001b[32m 4833\u001b[39m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[32m 4834\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
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"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:7070\u001b[39m, in \u001b[36mIndex.drop\u001b[39m\u001b[34m(self, labels, errors)\u001b[39m\n\u001b[32m 7068\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mask.any():\n\u001b[32m 7069\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors != \u001b[33m\"\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m7070\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask].tolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found in axis\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 7071\u001b[39m indexer = indexer[~mask]\n\u001b[32m 7072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.delete(indexer)\n",
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"\u001b[31mKeyError\u001b[39m: \"['developer', 'publisher'] not found in axis\""
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]
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}
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],
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"source": [
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"# appid,name,release_date,required_age,price,dlc_count,detailed_description,about_the_game,short_description,reviews,header_image,website,support_url,support_email,windows,mac,linux,metacritic_score,metacritic_url,achievements,recommendations,notes,supported_languages,full_audio_languages,packages,developers,publishers,categories,genres,screenshots,movies,user_score,score_rank,positive,negative,estimated_owners,average_playtime_forever,average_playtime_2weeks,median_playtime_forever,median_playtime_2weeks,discount,peak_ccu,tags,pct_pos_total,num_reviews_total,pct_pos_recent,num_reviews_recent\n",
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"dataset.drop(['appid', 'name', 'release_date', 'reviews', 'header_image', 'website', 'support_url', 'support_email', 'metacritic_url', 'developer', 'publisher', 'screenshots', 'movies', 'estimated_owners'], axis=1, inplace=True)\n",
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"print(dataset.head())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5436c87",
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"metadata": {},
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"source": [
|
||
"### Structurize Text\n",
|
||
"**TODO: check if makes sense**\n",
|
||
"The dataset holds a lot of unstructured data, we use Term Frequency-Inverse Document Frequency to structurize most Text-Features.\n",
|
||
"It is important to use an new Instance for each feature so they don't overlap with each other. \n",
|
||
"\n",
|
||
"### Standardize Numbers\n",
|
||
"We standardize the prices so they can "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4e8b407c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[[1. 1. 1.]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.compose import make_column_transformer\n",
|
||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||
"# types,desc_snippet,recent_reviews,all_reviews,release_date,popular_tags,game_details,languages,achievements,genre,game_description,mature_content,minimum_requirements,recommended_requirements,original_price,discount_price\n",
|
||
"column_transformer = make_column_transformer(\n",
|
||
" (TfidfVectorizer(stop_words='english'), ['desc_snippet']),\n",
|
||
" (TfidfVectorizer(stop_words='english'), ['mature_content']),\n",
|
||
" (TfidfVectorizer(stop_words='english'), ['game_description']),\n",
|
||
" (StandardScaler(), ['original_price','discount_price']) # use the same scaling for both\n",
|
||
" ('passthrough', ['price']),\n",
|
||
" #TODO: add transformer for every feature @flo @max\n",
|
||
" #TODO: check why not working\n",
|
||
")\n",
|
||
"#\n",
|
||
"dataset2 = column_transformer.fit_transform(dataset)\n",
|
||
"print(dataset2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ad84e777",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"### Removing Bundles\n",
|
||
"**(TODO: decide whether yes or no), not as important as i thought**\n",
|
||
"As bundles don't have clear genre(s) defined (e.g. publisher bundles )"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.13.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|