Uni/machine-learning
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add test script and minor Notebook changes with Tim

Browse Source
This commit is contained in:
FlorianSpeicher
2025-08-11 23:45:16 +02:00
parent 4842b55e4f
commit 1885f86371
2 changed files with 112 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
notebook.ipynb
View File

@@ -10,12 +10,15 @@
"The goal of this project is getting the genre(s) of a game trough its given metadata\n",
"\n",
"## Dataset\n",
"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)"
"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)\n",
"\n",
"### Importing the dataSet\n",
"The dataSet is imported and added as a variable."
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 6,
"id": "3116b75f",
"metadata": {},
"outputs": [
@@ -104,6 +107,7 @@
"- AppId\n",
"- Name of the Game\n",
"- Realease Date\n",
"- Reviews\n",
"- Header Image\n",
"- Website\n",
"- Support URL\n",
@@ -118,7 +122,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "06dedcdf",
"metadata": {},
"outputs": [
@@ -195,7 +199,9 @@
],
"source": [
"# 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",
"dataset.drop(['appid', 'name', 'release_date', 'header_image', 'website', 'support_url', 'support_email', 'metacritic_url', 'developers', 'publishers', 'screenshots', 'movies', 'estimated_owners'], axis=1, inplace=True)\n",
"dataset.drop(['appid', 'name', 'release_date', 'reviews', 'header_image', 'website', 'support_url', 'support_email',\n",
" 'metacritic_url', 'developers', 'publishers', 'screenshots', 'movies', 'estimated_owners'],\n",
" axis=1, inplace=True)\n",
"print(dataset.head())"
]
},
@@ -215,19 +221,24 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 9,
"id": "4e8b407c",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.ndarray' object has no attribute 'head'",
"ename": "ValueError",
"evalue": "all the input array dimensions except for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 1 and the array at index 3 has size 9999",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 4\u001b[39m column_transformer = make_column_transformer(\n\u001b[32m 5\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mdetailed_description\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 6\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mabout_the_game\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 7\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mshort_description\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 8\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mreviews\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 9\u001b[39m )\n\u001b[32m 11\u001b[39m dataset = column_transformer.fit_transform(dataset)\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhead\u001b[49m())\n",
"\u001b[31mAttributeError\u001b[39m: 'numpy.ndarray' object has no attribute 'head'"
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 3\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 4\u001b[39m column_transformer = make_column_transformer(\n\u001b[32m 5\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mdetailed_description\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 6\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mabout_the_game\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 7\u001b[39m (TfidfVectorizer(stop_words=\u001b[33m'\u001b[39m\u001b[33menglish\u001b[39m\u001b[33m'\u001b[39m), [\u001b[33m'\u001b[39m\u001b[33mshort_description\u001b[39m\u001b[33m'\u001b[39m]),\n\u001b[32m 8\u001b[39m (\u001b[33m'\u001b[39m\u001b[33mpassthrough\u001b[39m\u001b[33m'\u001b[39m, [\u001b[33m'\u001b[39m\u001b[33mrequired_age\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mprice\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mdlc_count\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mreviews\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mwindows\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mmac\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mlinux\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mmetacritic_score\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33machievements\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mrecommendations\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mnotes\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33msupported_languages\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mfull_audio_languages\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mcategories\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mgenres\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33muser_score\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mscore_rank\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpositive\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mnegative\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33maverage_playtime_forever\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33maverage_playtime_2weeks\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mmedian_playtime_forever\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mmedian_playtime_2weeks\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mdiscount\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpeak_ccu\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mtags\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpct_pos_total\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mnum_reviews_total\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpct_pos_recent\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mnum_reviews_recent\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m 9\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m dataset = \u001b[43mcolumn_transformer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 12\u001b[39m \u001b[38;5;28mprint\u001b[39m(dataset.head())\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:319\u001b[39m, in \u001b[36m_wrap_method_output.<locals>.wrapped\u001b[39m\u001b[34m(self, X, *args, **kwargs)\u001b[39m\n\u001b[32m 317\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m319\u001b[39m data_to_wrap = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 320\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[32m 321\u001b[39m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[32m 322\u001b[39m return_tuple = (\n\u001b[32m 323\u001b[39m _wrap_data_with_container(method, data_to_wrap[\u001b[32m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[32m 324\u001b[39m *data_to_wrap[\u001b[32m1\u001b[39m:],\n\u001b[32m 325\u001b[39m )\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1389\u001b[39m, in \u001b[36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m 1382\u001b[39m estimator._validate_params()\n\u001b[32m 1384\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m 1385\u001b[39m skip_parameter_validation=(\n\u001b[32m 1386\u001b[39m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 1387\u001b[39m )\n\u001b[32m 1388\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1389\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:1031\u001b[39m, in \u001b[36mColumnTransformer.fit_transform\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m 1028\u001b[39m \u001b[38;5;28mself\u001b[39m._validate_output(Xs)\n\u001b[32m 1029\u001b[39m \u001b[38;5;28mself\u001b[39m._record_output_indices(Xs)\n\u001b[32m-> \u001b[39m\u001b[32m1031\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_hstack\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mXs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_samples\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_samples\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:1225\u001b[39m, in \u001b[36mColumnTransformer._hstack\u001b[39m\u001b[34m(self, Xs, n_samples)\u001b[39m\n\u001b[32m 1215\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1216\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mConcatenating DataFrames from the transformer\u001b[39m\u001b[33m'\u001b[39m\u001b[33ms output lead to\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1217\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m an inconsistent number of samples. The output may have Pandas\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 1220\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m samples.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1221\u001b[39m )\n\u001b[32m 1223\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[32m-> \u001b[39m\u001b[32m1225\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\FlorianSpeicher\\anaconda3\\Lib\\site-packages\\numpy\\_core\\shape_base.py:364\u001b[39m, in \u001b[36mhstack\u001b[39m\u001b[34m(tup, dtype, casting)\u001b[39m\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _nx.concatenate(arrs, \u001b[32m0\u001b[39m, dtype=dtype, casting=casting)\n\u001b[32m 363\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m364\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_nx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcasting\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcasting\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[31mValueError\u001b[39m: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 1 and the array at index 3 has size 9999"
]
}
],
@@ -239,7 +250,7 @@
" (TfidfVectorizer(stop_words='english'), ['detailed_description']),\n",
" (TfidfVectorizer(stop_words='english'), ['about_the_game']),\n",
" (TfidfVectorizer(stop_words='english'), ['short_description']),\n",
" (TfidfVectorizer(stop_words='english'), ['reviews']),\n",
" ('passthrough', ['required_age','price','dlc_count','reviews','windows','mac','linux','metacritic_score','achievements','recommendations','notes','supported_languages','full_audio_languages','categories','genres','user_score','score_rank','positive','negative','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",
")\n",
"\n",
"dataset = column_transformer.fit_transform(dataset)\n",
@@ -262,8 +273,8 @@
"id": "6a2a3d4f",
"metadata": {},
"source": [
"### Setting missing values\n",
"**TODO: Removing or Setting values that are not set or NaN in the DataSet**"
"### Handling missing values\n",
"Removing NaN values in the dataSet and setting missing numerical feature values to the mean feature count. Missing Text values are set to a default String `Unknown`."
]
},
{
@@ -287,12 +298,12 @@
"metadata": {},
"source": [
"# Data Split\n",
"**TODO splitting the Data into Train, test and validation data**"
"Splitting our dataSet to training and testing data. The relation is 80% training and 20% testing data."
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "cfbf3787",
"metadata": {},
"outputs": [
@@ -307,7 +318,7 @@
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Annahme: 'genres' ist das Ziel/Label\n",
"# Setting the target feature 'genres' and dropping it from the dataset\n",
"X = dataset.drop('genres', axis=1)\n",
"y = dataset['genres']\n",
"\n",
@@ -315,7 +326,7 @@
" X, y, test_size=0.2, random_state=42\n",
")\n",
"\n",
"print(f\"Trainingsdaten: {X_train.shape}, Testdaten: {X_test.shape}\")"
"print(f\"Training: {X_train.shape}, Testing: {X_test.shape}\")"
]
},
{