{"id":38510,"date":"2026-02-01T19:30:42","date_gmt":"2026-02-01T16:30:42","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/sifir-koddan-calisan-bir-kesif-ajanina-python-streamlit-ve-jetbrains-ai-ile-donusum\/"},"modified":"2026-02-01T19:30:42","modified_gmt":"2026-02-01T16:30:42","slug":"sifir-koddan-calisan-bir-kesif-ajanina-python-streamlit-ve-jetbrains-ai-ile-donusum","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/sifir-koddan-calisan-bir-kesif-ajanina-python-streamlit-ve-jetbrains-ai-ile-donusum\/","title":{"rendered":"S\u0131f\u0131r Koddan \u00c7al\u0131\u015fan Bir Ke\u015fif Ajan\u0131na: Python, Streamlit ve JetBrains AI ile D\u00f6n\u00fc\u015f\u00fcm"},"content":{"rendered":"<h2>S\u0131f\u0131r Koddan \u00c7al\u0131\u015fan Bir Ke\u015fif Ajan\u0131na: Python, Streamlit ve JetBrains AI ile D\u00f6n\u00fc\u015f\u00fcm<\/h2>\n<p>Geleneksel ke\u015fif s\u00fcre\u00e7leri, spor d\u00fcnyas\u0131ndan pazar ara\u015ft\u0131rmalar\u0131na, yetenek avc\u0131l\u0131\u011f\u0131ndan bilimsel projelere kadar pek \u00e7ok alanda zaman al\u0131c\u0131, subjektif ve insan hatas\u0131na a\u00e7\u0131k olabilmektedir. Bu makalede, bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in Python&#8217;\u0131n g\u00fcc\u00fcn\u00fc, Streamlit&#8217;in h\u0131zl\u0131 web uygulamas\u0131 geli\u015ftirme yeteneklerini ve JetBrains AI&#8217;\u0131n ak\u0131ll\u0131 kodlama deste\u011fini birle\u015ftirerek, s\u0131f\u0131rdan \u00e7al\u0131\u015fan bir ke\u015fif ajan\u0131 nas\u0131l olu\u015fturulabilece\u011fini ad\u0131m ad\u0131m inceleyece\u011fiz. &#8220;S\u0131f\u0131r kod&#8221; ifadesiyle kast\u0131m\u0131z, haz\u0131r bir \u00e7\u00f6z\u00fcm yerine kendi ihtiya\u00e7lar\u0131n\u0131za \u00f6zel, ba\u015ftan sona, esnek ve g\u00fc\u00e7l\u00fc bir sistem in\u015fa etme s\u00fcrecidir. Bu yolculuk, veri odakl\u0131 kararlar alman\u0131n kap\u0131lar\u0131n\u0131 aralayacak ve rekabet avantaj\u0131 sa\u011flayacakt\u0131r.<\/p>\n<h3>Ke\u015fif Ajan\u0131 Nedir ve Neden \u0130htiya\u00e7 Duyar\u0131z?<\/h3>\n<p>Bir ke\u015fif ajan\u0131, belirli bir alandaki verileri otomatik olarak toplayan, analiz eden ve bu analizler sonucunda de\u011ferli i\u00e7g\u00f6r\u00fcler veya \u00f6neriler sunan ak\u0131ll\u0131 bir sistemdir. Bu ajanlar, insan karar alma s\u00fcre\u00e7lerini desteklemek, h\u0131zland\u0131rmak ve daha objektif hale getirmek amac\u0131yla tasarlan\u0131r.<\/p>\n<h4>Geleneksel Ke\u015fif S\u00fcre\u00e7lerinin Zorluklar\u0131<\/h4>\n<ul>\n<li><b>Zaman Yo\u011funlu\u011fu:<\/b> Manuel veri toplama, g\u00f6zlem ve raporlama s\u00fcre\u00e7leri olduk\u00e7a uzun s\u00fcrebilir.<\/li>\n<li><b>Subjektiflik ve \u00d6nyarg\u0131:<\/b> \u0130nsan fakt\u00f6r\u00fc, kararlara ki\u015fisel \u00f6nyarg\u0131lar\u0131 ve duygusal etkile\u015fimleri dahil edebilir.<\/li>\n<li><b>S\u0131n\u0131rl\u0131 Kapsam:<\/b> Bir insan\u0131n ayn\u0131 anda takip edebilece\u011fi veri miktar\u0131 ve kaynak say\u0131s\u0131 k\u0131s\u0131tl\u0131d\u0131r.<\/li>\n<li><b>Veri Tutars\u0131zl\u0131\u011f\u0131:<\/b> Farkl\u0131 kaynaklardan gelen verilerin manuel olarak birle\u015ftirilmesi ve temizlenmesi zordur.<\/li>\n<\/ul>\n<h4>Dijitalle\u015fmenin Getirdi\u011fi F\u0131rsatlar<\/h4>\n<p>Dijitalle\u015fme ve yapay zeka teknolojileri, ke\u015fif s\u00fcre\u00e7lerini k\u00f6kten de\u011fi\u015ftirme potansiyeli sunar. Bir dijital ke\u015fif ajan\u0131 sayesinde:<\/p>\n<ul>\n<li>Veri toplama otomatikle\u015fir ve daha geni\u015f bir kaynaktan beslenir.<\/li>\n<li>Analizler, makine \u00f6\u011frenimi algoritmalar\u0131 sayesinde objektif ve derinlemesine yap\u0131l\u0131r.<\/li>\n<li>Karar verme s\u00fcre\u00e7leri h\u0131zlan\u0131r ve ger\u00e7ek zamanl\u0131 verilere dayanabilir.<\/li>\n<li>\u0130nsan hatas\u0131 minimize edilir ve daha tutarl\u0131 sonu\u00e7lar elde edilir.<\/li>\n<\/ul>\n<p>A\u015fa\u011f\u0131daki tablo, geleneksel ve dijital ke\u015fif yakla\u015f\u0131mlar\u0131n\u0131n temel \u00f6zelliklerini kar\u015f\u0131la\u015ft\u0131rmaktad\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>Geleneksel Ke\u015fif<\/th>\n<th>Dijital Ke\u015fif Ajan\u0131<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Veri Toplama<\/td>\n<td>Manuel, g\u00f6zleme dayal\u0131, s\u0131n\u0131rl\u0131 kaynaklar<\/td>\n<td>Otomatik, \u00e7e\u015fitli API&#8217;ler ve veri kaynaklar\u0131<\/td>\n<\/tr>\n<tr>\n<td>Analiz Derinli\u011fi<\/td>\n<td>Subjektif, insan yorumuna ba\u011f\u0131ml\u0131<\/td>\n<td>Objektif, ML\/AI destekli derinlemesine analiz<\/td>\n<\/tr>\n<tr>\n<td>H\u0131z ve Verimlilik<\/td>\n<td>Zaman al\u0131c\u0131, yava\u015f<\/td>\n<td>H\u0131zl\u0131, anl\u0131k veya yak\u0131n anl\u0131k sonu\u00e7lar<\/td>\n<\/tr>\n<tr>\n<td>Hata Pay\u0131<\/td>\n<td>Y\u00fcksek (insan hatas\u0131, \u00f6nyarg\u0131)<\/td>\n<td>D\u00fc\u015f\u00fck (algoritmik tutarl\u0131l\u0131k)<\/td>\n<\/tr>\n<tr>\n<td>\u00d6l\u00e7eklenebilirlik<\/td>\n<td>S\u0131n\u0131rl\u0131<\/td>\n<td>Y\u00fcksek, daha fazla veri ve senaryo i\u00e7in geni\u015fletilebilir<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Teknoloji Y\u0131\u011f\u0131n\u0131na Giri\u015f: Python, Streamlit ve JetBrains AI<\/h3>\n<p>Ke\u015fif ajan\u0131 projemizin temelini olu\u015fturacak teknoloji y\u0131\u011f\u0131n\u0131, modern geli\u015ftirme pratiklerinin en g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131n\u0131 bir araya getiriyor.<\/p>\n<h4>Python: Temel Dil Se\u00e7imi<\/h4>\n<p>Python, veri bilimi, makine \u00f6\u011frenimi ve web geli\u015ftirme alanlar\u0131nda en pop\u00fcler dillerden biridir. Okunabilirli\u011fi, geni\u015f k\u00fct\u00fcphane ekosistemi (Pandas, NumPy, Scikit-learn vb.) ve b\u00fcy\u00fck topluluk deste\u011fi sayesinde ke\u015fif ajan\u0131 gibi karma\u015f\u0131k projeler i\u00e7in ideal bir se\u00e7imdir. Veri i\u015fleme, model e\u011fitimi ve i\u015f mant\u0131\u011f\u0131n\u0131n \u00e7o\u011fu Python ile yaz\u0131lacakt\u0131r.<\/p>\n<h4>Streamlit: H\u0131zl\u0131 Web Uygulamas\u0131 Geli\u015ftirme<\/h4>\n<p>Streamlit, veri bilimcilerin ve m\u00fchendislerin Python betiklerini sadece birka\u00e7 sat\u0131r kodla etkile\u015fimli web uygulamalar\u0131na d\u00f6n\u00fc\u015ft\u00fcrmelerini sa\u011flayan a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. Kullan\u0131c\u0131 aray\u00fcz\u00fc (UI) geli\u015ftirme s\u00fcrecini basitle\u015ftirerek, veri g\u00f6rselle\u015ftirme ve model sonu\u00e7lar\u0131n\u0131 sunma konusunda b\u00fcy\u00fck kolayl\u0131k sa\u011flar. Bu sayede, ajan\u0131n analizlerini kullan\u0131c\u0131 dostu bir aray\u00fczle sunabiliriz.<\/p>\n<h4>JetBrains AI: Geli\u015ftirme S\u00fcrecini H\u0131zland\u0131rma<\/h4>\n<p>JetBrains IDE&#8217;leri (\u00f6rne\u011fin PyCharm), geli\u015ftiricilere kod yazma s\u00fcrecinde \u00f6nemli kolayl\u0131klar sunar. JetBrains AI \u00f6zellikleri ise bu deneyimi bir \u00fcst seviyeye ta\u015f\u0131r. Ak\u0131ll\u0131 kod tamamlama, ba\u011flama duyarl\u0131 \u00f6neriler, hata tespiti, refactoring \u00f6nerileri ve hatta do\u011fal dilden kod \u00fcretimi gibi yetenekleriyle, geli\u015ftirme h\u0131z\u0131n\u0131 art\u0131r\u0131r ve kod kalitesini iyile\u015ftirir. \u00d6zellikle karma\u015f\u0131k algoritmalar veya API entegrasyonlar\u0131 \u00fczerinde \u00e7al\u0131\u015f\u0131rken, JetBrains AI&#8217;\u0131n sundu\u011fu destek, s\u0131f\u0131rdan bir ajan in\u015fa etme s\u00fcrecini \u00f6nemli \u00f6l\u00e7\u00fcde h\u0131zland\u0131racakt\u0131r.<\/p>\n<h3>Veri Toplama ve \u00d6n \u0130\u015fleme<\/h3>\n<p>Herhangi bir yapay zeka veya analiz projesinin temel ta\u015f\u0131, kaliteli veridir. Ke\u015fif ajan\u0131 i\u00e7in de do\u011fru ve temiz veriye ula\u015fmak kritik \u00f6neme sahiptir.<\/p>\n<h4>Veri Kaynaklar\u0131 ve API Entegrasyonu<\/h4>\n<p>Ke\u015fif ajan\u0131, farkl\u0131 kaynaklardan veri toplayabilir:<\/p>\n<ul>\n<li><b>Web Scraping:<\/b> Belirli web sitelerinden (\u00f6rne\u011fin, spor istatistikleri siteleri, haber portallar\u0131) Beautiful Soup veya Scrapy gibi k\u00fct\u00fcphanelerle veri \u00e7ekilebilir. Bu s\u00fcre\u00e7te yasal ve etik kurallara dikkat etmek \u00f6nemlidir.<\/li>\n<li><b>Herkese A\u00e7\u0131k API&#8217;ler:<\/b> Twitter API, Google Trends API, spor liglerinin resmi API&#8217;leri gibi kaynaklardan yap\u0131land\u0131r\u0131lm\u0131\u015f veri elde edilebilir.<\/li>\n<li><b>Veritabanlar\u0131:<\/b> Mevcut kurumsal veritabanlar\u0131ndan (SQL, NoSQL) ilgili veriler \u00e7ekilebilir.<\/li>\n<li><b>CSV\/Excel Dosyalar\u0131:<\/b> Manuel olarak toplanm\u0131\u015f veya d\u0131\u015far\u0131dan al\u0131nan statik veri setleri kullan\u0131labilir.<\/li>\n<\/ul>\n<p>\u00d6rne\u011fin, bir spor ke\u015fif ajan\u0131 i\u00e7in oyuncu istatistiklerini bir API&#8217;den \u00e7ekmek:<\/p>\n<pre><code class=\"language-python\">\nimport requests\nimport pandas as pd\n\ndef get_player_stats(player_id):\n    # Bu k\u0131s\u0131m ger\u00e7ek bir API'ye g\u00f6re de\u011fi\u015fecektir\n    api_url = f\"https:\/\/api.example.com\/players\/{player_id}\/stats\"\n    headers = {\"Authorization\": \"Bearer YOUR_API_KEY\"}\n    try:\n        response = requests.get(api_url, headers=headers)\n        response.raise_for_status() # HTTP hatalar\u0131n\u0131 kontrol et\n        return response.json()\n    except requests.exceptions.RequestException as e:\n        print(f\"API iste\u011fi hatas\u0131: {e}\")\n        return None\n\n# \u00d6rnek kullan\u0131m\n# player_data = get_player_stats(\"player123\")\n# if player_data:\n#     df = pd.DataFrame([player_data])\n#     print(df.head())\n<\/pre>\n<p><\/code><\/p>\n<h4>Veri Temizleme ve Yap\u0131land\u0131rma<\/h4>\n<p>Toplanan ham veriler genellikle g\u00fcr\u00fclt\u00fcl\u00fc, eksik veya tutars\u0131zd\u0131r. Bu verilerin analize uygun hale getirilmesi i\u00e7in \u00f6n i\u015fleme ad\u0131mlar\u0131 gereklidir:<\/p>\n<ul>\n<li><b>Eksik De\u011ferlerin Y\u00f6netimi:<\/b> Bo\u015f h\u00fccreler ortalama, medyan ile doldurulabilir veya ilgili sat\u0131rlar silinebilir.<\/li>\n<li><b>Ayk\u0131r\u0131 De\u011ferlerin Tespiti ve \u0130\u015flenmesi:<\/b> Veri setindeki anormal de\u011ferler (outlier) model performans\u0131n\u0131 olumsuz etkileyebilir.<\/li>\n<li><b>Veri Tipi D\u00f6n\u00fc\u015f\u00fcmleri:<\/b> Say\u0131sal olmas\u0131 gereken verilerin do\u011fru tipte oldu\u011fundan emin olmak.<\/li>\n<li><b>Normalizasyon\/Standardizasyon:<\/b> Farkl\u0131 \u00f6l\u00e7eklerdeki \u00f6zellikleri ayn\u0131 aral\u0131\u011fa getirmek, modelin daha iyi \u00f6\u011frenmesini sa\u011flar.<\/li>\n<\/ul>\n<pre><code class=\"language-python\">\n# \u00d6rnek Python Kodu: Veri Y\u00fckleme ve Temizleme\nimport pandas as pd\n\n# \u00d6rnek veri seti olu\u015fturma\ndata = {\n    'Oyuncu': ['Ali', 'Ay\u015fe', 'Can', 'Deniz', 'Ela'],\n    'Ma\u00e7 Say\u0131s\u0131': [10, 12, 8, 15, 9],\n    'Gol Say\u0131s\u0131': [5, 7, 3, 10, None], # Eksik de\u011fer\n    'Asist Say\u0131s\u0131': [2, 3, 1, 4, 2],\n    'Puan Ortalamas\u0131': [7.5, 8.1, 6.9, 8.8, 7.2]\n}\ndf = pd.DataFrame(data)\n\nprint(\"Orijinal Veri Seti:\")\nprint(df)\n\n# Eksik de\u011ferleri ortalama ile doldurma\ndf['Gol Say\u0131s\u0131'].fillna(df['Gol Say\u0131s\u0131'].mean(), inplace=True)\n\nprint(\"\\nEksik de\u011ferler doldurulduktan sonra:\")\nprint(df)\n<\/pre>\n<p><\/code><\/p>\n<h4>\u00d6zellik M\u00fchendisli\u011fi (Feature Engineering)<\/h4>\n<p>Mevcut verilerden yeni ve daha anlaml\u0131 \u00f6zellikler t\u00fcretmek, makine \u00f6\u011frenimi modellerinin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilir. \u00d6rne\u011fin, gol say\u0131s\u0131 ve ma\u00e7 say\u0131s\u0131 verilerinden \"gol ba\u015f\u0131na ma\u00e7 oran\u0131\" gibi bir \u00f6zellik t\u00fcretmek, oyuncunun verimlili\u011fi hakk\u0131nda daha iyi bilgi verebilir.<\/p>\n<pre><code class=\"language-python\">\n# Yeni bir \u00f6zellik olu\u015fturma: Gol ba\u015f\u0131na ma\u00e7 say\u0131s\u0131\ndf['Gol_Ma\u00e7_Oran\u0131'] = df['Gol Say\u0131s\u0131'] \/ df['Ma\u00e7 Say\u0131s\u0131']\n\nprint(\"\\nYeni \u00f6zellik eklenmi\u015f veri seti:\")\nprint(df)\n<\/pre>\n<p><\/code><\/p>\n<h3>Yapay Zeka Destekli Analiz ve Karar Mekanizmalar\u0131<\/h3>\n<p>Veriler temizlenip haz\u0131rland\u0131ktan sonra, ke\u015fif ajan\u0131n\u0131n as\u0131l g\u00fcc\u00fcn\u00fc ortaya koyacak olan yapay zeka ve makine \u00f6\u011frenimi modellerini devreye sokabiliriz.<\/p>\n<h4>Makine \u00d6\u011frenimi Modellerinin Se\u00e7imi<\/h4>\n<p>Kullan\u0131lacak model, ajan\u0131n amac\u0131na g\u00f6re de\u011fi\u015fir:<\/p>\n<ul>\n<li><b>S\u0131n\u0131fland\u0131rma (Classification):<\/b> Bir oyuncunun \"ba\u015far\u0131l\u0131\" veya \"ba\u015far\u0131s\u0131z\" olaca\u011f\u0131n\u0131 tahmin etmek gibi ikili veya \u00e7oklu s\u0131n\u0131fland\u0131rma problemleri i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, Lojistik Regresyon, Karar A\u011fa\u00e7lar\u0131, Random Forest).<\/li>\n<li><b>Regresyon (Regression):<\/b> Bir oyuncunun gelecekteki gol say\u0131s\u0131 veya piyasa de\u011feri gibi s\u00fcrekli bir de\u011feri tahmin etmek i\u00e7in kullan\u0131l\u0131r (\u00f6rne\u011fin, Do\u011frusal Regresyon, Destek Vekt\u00f6r Regresyonu).<\/li>\n<li><b>K\u00fcmeleme (Clustering):<\/b> Oyuncular\u0131 benzer \u00f6zelliklerine g\u00f6re gruplamak (\u00f6rne\u011fin, K-Means) veya pazar segmentlerini belirlemek i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<pre><code class=\"language-python\">\n# \u00d6rnek Python Kodu: Basit Bir Makine \u00d6\u011frenimi Modeli\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\n\n# \u00d6rnek veri (ger\u00e7ek bir senaryoda daha fazla \u00f6zellik ve ger\u00e7ek hedef de\u011fi\u015fken olurdu)\n# Hedef de\u011fi\u015fken: 'Ba\u015far\u0131l\u0131' (1) veya 'Ba\u015far\u0131s\u0131z' (0)\nX = df[['Ma\u00e7 Say\u0131s\u0131', 'Gol Say\u0131s\u0131', 'Asist Say\u0131s\u0131', 'Puan Ortalamas\u0131', 'Gol_Ma\u00e7_Oran\u0131']]\ny = [1, 1, 0, 1, 0] # Bu sadece bir \u00f6rnektir, ger\u00e7ek veriden t\u00fcretilmelidir\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = RandomForestClassifier(n_estimators=100, random_state=42)\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_test)\nprint(f\"Model Do\u011frulu\u011fu: {accuracy_score(y_test, y_pred):.2f}\")\n\n# Yeni bir oyuncu i\u00e7in tahmin yapma\nyeni_oyuncu_verisi = pd.DataFrame([[11, 6, 3, 7.8, 0.54]], columns=X.columns)\ntahmin = model.predict(yeni_oyuncu_verisi)\nprint(f\"Yeni oyuncu i\u00e7in tahmin: {'Ba\u015far\u0131l\u0131' if tahmin[0] == 1 else 'Ba\u015far\u0131s\u0131z'}\")\n<\/pre>\n<p><\/code><\/p>\n<h4>Do\u011fal Dil \u0130\u015fleme (NLP) ile Metin Analizi<\/h4>\n<p>Ke\u015fif raporlar\u0131, haber makaleleri veya sosyal medya yorumlar\u0131 gibi yap\u0131land\u0131r\u0131lmam\u0131\u015f metin verilerini analiz etmek i\u00e7in NLP teknikleri kullan\u0131labilir. Duygu analizi ile bir oyuncu hakk\u0131ndaki genel alg\u0131y\u0131 \u00f6l\u00e7ebilir, anahtar kelime \u00e7\u0131karma ile \u00f6nemli yetenekleri veya zay\u0131fl\u0131klar\u0131 belirleyebiliriz. NLTK veya spaCy gibi k\u00fct\u00fcphaneler bu konuda yard\u0131mc\u0131 olur.<\/p>\n<h4>Tahminsel Analiz ve \u00d6neri Sistemleri<\/h4>\n<p>E\u011fitilen modeller sayesinde, ajan\u0131n temel i\u015flevi olan tahmin ve \u00f6neri yapma yetene\u011fini geli\u015ftirebiliriz. \u00d6rne\u011fin, belirli kriterlere uyan en iyi 5 oyuncuyu \u00f6nerebilir, veya bir oyuncunun gelecekteki performans\u0131n\u0131 tahmin edebiliriz. Bu sistemler, karar vericilere somut, veri odakl\u0131 se\u00e7enekler sunar.<\/p>\n<h3>Kullan\u0131c\u0131 Aray\u00fcz\u00fc Geli\u015ftirme: Streamlit ile Etkile\u015fim<\/h3>\n<p>Ke\u015fif ajan\u0131 ne kadar ak\u0131ll\u0131 olursa olsun, kullan\u0131c\u0131lar\u0131n onunla kolayca etkile\u015fim kurabilmesi ve sonu\u00e7lar\u0131 anla\u015f\u0131l\u0131r bir \u015fekilde g\u00f6rebilmesi gerekir. Streamlit, bu ihtiyac\u0131 kar\u015f\u0131lamak i\u00e7in ideal bir ara\u00e7t\u0131r.<\/p>\n<h4>Streamlit Bile\u015fenleri ile Aray\u00fcz Tasar\u0131m\u0131<\/h4>\n<p>Streamlit, metin giri\u015f kutular\u0131 (<code>st.text_input<\/code>), kayd\u0131r\u0131c\u0131lar (<code>st.slider<\/code>), d\u00fc\u011fmeler (<code>st.button<\/code>), a\u00e7\u0131l\u0131r men\u00fcler (<code>st.selectbox<\/code>) gibi bir\u00e7ok haz\u0131r bile\u015fen sunar. Bu bile\u015fenleri kullanarak, kullan\u0131c\u0131lar\u0131n veri girmesini, filtreleme yapmas\u0131n\u0131 veya analizleri tetiklemesini sa\u011flayabiliriz. Ayr\u0131ca <code>st.columns<\/code> ile d\u00fczenlemeler yaparak aray\u00fcz\u00fc daha estetik hale getirebiliriz.<\/p>\n<pre><code class=\"language-python\"><br \/>\n# \u00d6rnek Python Kodu: Basit Streamlit Uygulamas\u0131 Yap\u0131s\u0131<br \/>\nimport streamlit as st<br \/>\nimport pandas as pd<br \/>\n# \u00d6nceki veri \u00e7er\u00e7evemizi burada kullanabiliriz<br \/>\n# from your_module import df, model # Ger\u00e7ek bir uygulamada model ve df buradan import edilirdi<\/p>\n<p>st.title(\"\u26bd Ak\u0131ll\u0131 Ke\u015fif Ajan\u0131\")<br \/>\nst.markdown(\"Oyuncu verilerini analiz edin ve potansiyel yetenekleri ke\u015ffedin!\")<\/p>\n<p># Yan men\u00fc<br \/>\nst.sidebar.header(\"Ayarlar\")<br \/>\nveri_kaynagi = st.sidebar.selectbox(\"Veri Kayna\u011f\u0131 Se\u00e7in\", [\"API\", \"CSV Y\u00fckle\"])<\/p>\n<p>if veri_kaynagi == \"CSV Y\u00fckle\":<br \/>\n    uploaded_file = st.sidebar.file_uploader(\"CSV Dosyas\u0131 Y\u00fckle\", type=[\"csv<\/p>\n","protected":false},"excerpt":{"rendered":"Bir ke\u015fif ajan\u0131, belirli bir alandaki verileri otomatik olarak toplayan, analiz eden ve bu analizler sonucunda de\u011ferli i\u00e7g\u00f6r\u00fcler veya \u00f6neriler sunan a&#8230;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1403],"tags":[],"class_list":{"0":"post-38510","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-python","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - 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