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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'bs4'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[2], line 6\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymongo\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MongoClient\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbs4\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BeautifulSoup\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'bs4'"
]
}
],
"source": [
"import csv\n",
"import pandas as pd \n",
"from pymongo import MongoClient\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Connect to MongoDB\n",
"client = MongoClient(\"mongodb://localhost:27017/\")\n",
"db = client[\"myDatabase\"]\n",
"source_collection = db[\"data\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Export translated data to a CSV file #bu dosyayı json olarak indirdim\n",
"\"\"\"yeni_data = list(source_collection.find())\n",
"print(yeni_data)\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" _id title \\\n",
"0 {'$oid': '66a1020f29abc84d21689044'} Mental Note Vol. 24 \n",
"1 {'$oid': '66a1020f29abc84d21689045'} Your Brain On Coronavirus \n",
"2 {'$oid': '66a1020f29abc84d21689046'} Mind Your Nose \n",
"3 {'$oid': '66a1020f29abc84d21689047'} The 4 Purposes of Dreams \n",
"4 {'$oid': '66a1020f29abc84d21689048'} Surviving a Rod Through the Head \n",
"\n",
" url authors \\\n",
"0 https://medium.com/invisible-illness/mental-no... ['Ryan Fan'] \n",
"1 https://medium.com/age-of-awareness/how-the-pa... ['Simon Spichak'] \n",
"2 https://medium.com/neodotlife/mind-your-nose-f... [] \n",
"3 https://medium.com/science-for-real/the-4-purp... ['Eshan Samaranayake'] \n",
"4 https://medium.com/live-your-life-on-purpose/s... ['Rishav Sinha'] \n",
"\n",
" timestamp \\\n",
"0 2020-12-26 03:38:10.479000+00:00 \n",
"1 2020-09-23 22:10:17.126000+00:00 \n",
"2 2020-10-10 20:17:37.132000+00:00 \n",
"3 2020-12-21 16:05:19.524000+00:00 \n",
"4 2020-02-26 00:01:01.576000+00:00 \n",
"\n",
" tags \n",
"0 ['Mental Health', 'Health', 'Psychology', 'Sci... \n",
"1 ['Mental Health', 'Coronavirus', 'Science', 'P... \n",
"2 ['Biotechnology', 'Neuroscience', 'Brain', 'We... \n",
"3 ['Health', 'Neuroscience', 'Mental Health', 'P... \n",
"4 ['Brain', 'Health', 'Development', 'Psychology... \n"
]
}
],
"source": [
"#csv dosyası olarak yüklenmesi\n",
"df=pd.read_json('myDatabase.data.json')\n",
"print(df.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>_id</th>\n",
" <th>title</th>\n",
" <th>url</th>\n",
" <th>authors</th>\n",
" <th>timestamp</th>\n",
" <th>tags</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>{'$oid': '66a1020f29abc84d21689044'}</td>\n",
" <td>Mental Note Vol. 24</td>\n",
" <td>https://medium.com/invisible-illness/mental-no...</td>\n",
" <td>['Ryan Fan']</td>\n",
" <td>2020-12-26 03:38:10.479000+00:00</td>\n",
" <td>['Mental Health', 'Health', 'Psychology', 'Sci...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>{'$oid': '66a1020f29abc84d21689045'}</td>\n",
" <td>Your Brain On Coronavirus</td>\n",
" <td>https://medium.com/age-of-awareness/how-the-pa...</td>\n",
" <td>['Simon Spichak']</td>\n",
" <td>2020-09-23 22:10:17.126000+00:00</td>\n",
" <td>['Mental Health', 'Coronavirus', 'Science', 'P...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>{'$oid': '66a1020f29abc84d21689046'}</td>\n",
" <td>Mind Your Nose</td>\n",
" <td>https://medium.com/neodotlife/mind-your-nose-f...</td>\n",
" <td>[]</td>\n",
" <td>2020-10-10 20:17:37.132000+00:00</td>\n",
" <td>['Biotechnology', 'Neuroscience', 'Brain', 'We...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>{'$oid': '66a1020f29abc84d21689047'}</td>\n",
" <td>The 4 Purposes of Dreams</td>\n",
" <td>https://medium.com/science-for-real/the-4-purp...</td>\n",
" <td>['Eshan Samaranayake']</td>\n",
" <td>2020-12-21 16:05:19.524000+00:00</td>\n",
" <td>['Health', 'Neuroscience', 'Mental Health', 'P...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>{'$oid': '66a1020f29abc84d21689048'}</td>\n",
" <td>Surviving a Rod Through the Head</td>\n",
" <td>https://medium.com/live-your-life-on-purpose/s...</td>\n",
" <td>['Rishav Sinha']</td>\n",
" <td>2020-02-26 00:01:01.576000+00:00</td>\n",
" <td>['Brain', 'Health', 'Development', 'Psychology...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" _id title \\\n",
"0 {'$oid': '66a1020f29abc84d21689044'} Mental Note Vol. 24 \n",
"1 {'$oid': '66a1020f29abc84d21689045'} Your Brain On Coronavirus \n",
"2 {'$oid': '66a1020f29abc84d21689046'} Mind Your Nose \n",
"3 {'$oid': '66a1020f29abc84d21689047'} The 4 Purposes of Dreams \n",
"4 {'$oid': '66a1020f29abc84d21689048'} Surviving a Rod Through the Head \n",
"\n",
" url authors \\\n",
"0 https://medium.com/invisible-illness/mental-no... ['Ryan Fan'] \n",
"1 https://medium.com/age-of-awareness/how-the-pa... ['Simon Spichak'] \n",
"2 https://medium.com/neodotlife/mind-your-nose-f... [] \n",
"3 https://medium.com/science-for-real/the-4-purp... ['Eshan Samaranayake'] \n",
"4 https://medium.com/live-your-life-on-purpose/s... ['Rishav Sinha'] \n",
"\n",
" timestamp \\\n",
"0 2020-12-26 03:38:10.479000+00:00 \n",
"1 2020-09-23 22:10:17.126000+00:00 \n",
"2 2020-10-10 20:17:37.132000+00:00 \n",
"3 2020-12-21 16:05:19.524000+00:00 \n",
"4 2020-02-26 00:01:01.576000+00:00 \n",
"\n",
" tags \n",
"0 ['Mental Health', 'Health', 'Psychology', 'Sci... \n",
"1 ['Mental Health', 'Coronavirus', 'Science', 'P... \n",
"2 ['Biotechnology', 'Neuroscience', 'Brain', 'We... \n",
"3 ['Health', 'Neuroscience', 'Mental Health', 'P... \n",
"4 ['Brain', 'Health', 'Development', 'Psychology... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"_id object\n",
"title object\n",
"url object\n",
"authors object\n",
"timestamp object\n",
"tags object\n",
"dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 <class 'dict'>\n",
"Name: _id, dtype: object\n",
"0 <class 'str'>\n",
"Name: title, dtype: object\n",
"0 <class 'str'>\n",
"Name: url, dtype: object\n",
"0 <class 'str'>\n",
"Name: authors, dtype: object\n",
"0 <class 'str'>\n",
"Name: timestamp, dtype: object\n",
"0 <class 'str'>\n",
"Name: tags, dtype: object\n"
]
}
],
"source": [
"for i in df.columns:\n",
" print(df[i].apply(lambda x:type(x)).head(1))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#içeriklerin saklanacağı bir liste oluştrun\n",
"contents=[]\n",
"#her url için içeriği çekin \n",
"\n",
"for url in df['url']:\n",
" try:\n",
" response=requests.get(url)\n",
" soup=BeautifulSoup(response.content,'html.parser')\n",
"\n",
" #medium içeriğini çekmek için uygun seçiciyi kullanın\n",
" article_content=soup.find('articles')\n",
" content=article_content.get_text(separator='') if article_content else 'content not found'\n",
"\n",
" contents.append(content)\n",
" except Exception as e:\n",
" contents.append(f'error retrieving content: {e}')\n",
"\n",
"#içerikleri veri çerçevesine ekleyin.\n",
"df['content']= contents\n",
"\n",
"#yeni veri kümesini kontrol edin\n",
"print(df.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#modeleğitimi için test valid değerleriğ oluşturma \n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_val, y_train, y_val = train_test_split(translated_data, translated_data, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.svm import SVC\n",
"\n",
"vectorizer = TfidfVectorizer()\n",
"X_train_transformed = vectorizer.fit_transform(X_train)\n",
"X_val_transformed = vectorizer.transform(X_val)\n",
"\n",
"model = SVC()\n",
"model.fit(X_train_transformed, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"y_pred = model.predict(X_val_transformed)\n",
"accuracy = accuracy_score(y_val, y_pred)\n",
"print(f\"Accuracy: {accuracy:.2f}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "myenv",
"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": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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