Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Churn[[:space:]]oluyor[[:space:]]mu[[:space:]]kontrol.xlsx filter=lfs diff=lfs merge=lfs -text

Churn oluyor mu kontrol.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0d3660e7db1da712247599d74c880a8f80a727b000b90ec5cdbfdd98950564
+size 783308

Dockerfile

@@ -0,0 +1,21 @@
+# Use official Python image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and source code
+COPY requirements.txt ./
+COPY gradio_app.py ./
+COPY churn_model.pkl ./
+COPY model_features.pkl ./
+COPY Telco-Customer-Churn.csv ./
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Gradio default port
+EXPOSE 7860
+
+# Run the Gradio app
+CMD ["python", "gradio_app.py"] 

README.md

@@ -1,12 +1,6 @@
 ---
-title: Gradio App
-emoji: 🦀
-colorFrom: pink
-colorTo: yellow
+title: gradio-app
+app_file: gradio_app.py
 sdk: gradio
 sdk_version: 5.31.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,91 @@
+from flask import Flask, render_template, request
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+
+model = joblib.load("churn_model.pkl")
+model_features = joblib.load("model_features.pkl")
+
+value_map = {
+    "Aydan aya": "Month-to-month",
+    "1 yıllık": "One year",
+    "2 yıllık": "Two year",
+    "Elektronik çek": "Electronic check",
+    "Posta çeki": "Mailed check",
+    "Banka havalesi (otomatik)": "Bank transfer (automatic)",
+    "Kredi kartı (otomatik)": "Credit card (automatic)",
+    "Hayır": "No",
+    "Evet": "Yes",
+    "Yok": "No internet service",
+    "Telefon hizmeti yok": "No phone service",
+    "Fiber optik": "Fiber optic"
+}
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    result = None
+
+    if request.method == "POST":
+        form = request.form
+        input_dict = {}
+
+        # Sayısal alanlar
+        tenure = float(form.get("tenure"))
+        monthly = float(form.get("MonthlyCharges"))
+        total = float(form.get("TotalCharges"))
+
+        # Temel sayısal değişkenler
+        input_dict["tenure"] = tenure
+        input_dict["PhoneService"] = form.get("PhoneService") == "Evet"
+        input_dict["avg_charge_per_month"] = total / tenure if tenure > 0 else 0
+        input_dict["charge_ratio"] = total / (monthly * tenure) if monthly > 0 and tenure > 0 else 1
+
+        # tenure_bin
+        tenure_label = "0-12" if tenure <= 12 else "12-24" if tenure <= 24 else "24+"
+        for bin_label in ["0-12", "12-24", "24+"]:
+            input_dict[f"tenure_bin_{bin_label}"] = (tenure_label == bin_label)
+
+        # is_long_term_contract
+        contract_value = value_map.get(form.get("Contract"), form.get("Contract"))
+        input_dict["is_long_term_contract"] = contract_value in ["One year", "Two year"]
+
+        # One-hot kategorik değişkenler
+        categorical_fields = [
+            "gender", "SeniorCitizen", "Partner", "Dependents", "PaperlessBilling",
+            "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
+            "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
+            "Contract", "PaymentMethod"
+        ]
+
+        for field in categorical_fields:
+            raw_value = form.get(field)
+            mapped_value = value_map.get(raw_value, raw_value)
+            for col in model_features:
+                if col.startswith(f"{field}_"):
+                    input_dict[col] = (col == f"{field}_{mapped_value}")
+
+        # Eksik kalan tüm feature'lar tamamlanır
+        for col in model_features:
+            if col not in input_dict:
+                input_dict[col] = 0 if col == "tenure" or "charge" in col or "avg" in col else False
+
+        # DataFrame oluştur ve tahmin yap
+        input_df = pd.DataFrame([[input_dict[col] for col in model_features]], columns=model_features)
+
+        print("💬 MODELE GİDEN VERİLER:", flush=True)
+        print(input_df.to_dict(orient="records")[0], flush=True)
+
+        prediction = model.predict_proba(input_df)[0][1]
+        score = round(prediction * 100, 2)
+        if score >= 50:
+            comment = "Müşteri Kaybedilebilir."
+        else:
+            comment = "Müşteri Kayıp Riski Taşımıyor."
+        result = f"Churn Riski: %{score} — {comment}"
+
+
+    return render_template("index.html", result=result)
+
+if __name__ == "__main__":
+    app.run(debug=False)

churn_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e13f104be0f634de27e6721dd5fea34b64f29d9863684ddcdc8aa6a8bccbfa4c
+size 640263

churn_model.py

@@ -0,0 +1,110 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from xgboost import XGBClassifier
+from sklearn.model_selection import StratifiedKFold, cross_val_predict
+from sklearn.preprocessing import MinMaxScaler
+from imblearn.over_sampling import SMOTE
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
+from imblearn.pipeline import Pipeline
+import joblib
+
+sns.set(style='whitegrid')
+
+# Veri setini oku
+telco = pd.read_csv('Telco-Customer-Churn.csv')
+pd.set_option('display.max_columns', None)
+
+# TotalCharges sayısallaştırma ve eksik verileri çıkarma
+telco.TotalCharges = pd.to_numeric(telco.TotalCharges, errors='coerce')
+telco.dropna(inplace=True)
+
+# CustomerID'yi çıkar
+df2 = telco.iloc[:, 1:]
+
+# Binary sütunları True/False yap
+bool_map = {'Yes': True, 'No': False}
+binary_columns = ['Churn', 'Partner', 'Dependents', 'PhoneService', 'PaperlessBilling']
+for col in binary_columns:
+    df2[col] = df2[col].map(bool_map)
+
+df2['SeniorCitizen'].replace({1: True, 0: False}, inplace=True)
+df2['gender'].replace({'Female': True, 'Male': False}, inplace=True)
+
+# Yeni özellikler
+# Ortalama aylık ödeme
+df2['avg_charge_per_month'] = df2['TotalCharges'] / df2['tenure'].replace(0, 1)
+# Toplam ödeme ile (aylık * süre) oranı
+df2['charge_ratio'] = df2.apply(
+    lambda row: row['TotalCharges'] / (row['MonthlyCharges'] * row['tenure'])
+    if row['MonthlyCharges'] > 0 and row['tenure'] > 0 else 1, axis=1)
+
+# Süre kategorisi
+df2['tenure_bin'] = pd.cut(df2['tenure'], bins=[0, 12, 24, df2['tenure'].max()], labels=['0-12', '12-24', '24+'])
+
+# One-hot encoding
+multi_cat_cols = ['MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup',
+                  'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',
+                  'Contract', 'PaymentMethod', 'tenure_bin']
+df_dummies = pd.get_dummies(df2, columns=multi_cat_cols, drop_first=False)
+
+# Uzun vadeli sözleşme özelliği
+df_dummies['is_long_term_contract'] = (
+    df_dummies.get('Contract_One year', False) | df_dummies.get('Contract_Two year', False)
+)
+
+# Hedef ve bağımsız değişkenler
+y = df_dummies['Churn'].values
+X = df_dummies.drop(columns=['Churn'])
+
+# En önemli 15 özelliği seçmek için XGB fit
+temp_model = XGBClassifier(random_state=42)
+temp_model.fit(X, y)
+feature_importances = pd.Series(temp_model.feature_importances_, index=X.columns).sort_values(ascending=False)
+top_15_features = feature_importances.head(15).index.tolist()
+X_selected = X[top_15_features]
+
+# Pipeline: Ölçekleme + SMOTE + XGBoost
+pipe = Pipeline([
+    ("scaler", MinMaxScaler()),
+    ("smote", SMOTE(sampling_strategy=1.0, random_state=42)),
+    ("xgb", XGBClassifier(
+        n_estimators=100,
+        max_depth=4,
+        learning_rate=0.1,
+        subsample=1.0,
+        colsample_bytree=0.7,
+        scale_pos_weight=1,
+        eval_metric='logloss',
+        random_state=42
+    ))
+])
+
+# 5-fold stratified cross-validation
+cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+y_pred = cross_val_predict(pipe, X_selected, y, cv=cv, method="predict")
+y_proba = cross_val_predict(pipe, X_selected, y, cv=cv, method="predict_proba")[:, 1]
+
+# Performans metrikleri
+print("XGBoost Cross-Validation Sonuçları (Binary):")
+print("--------------------------------------------")
+print("Accuracy: {:.4f}".format(accuracy_score(y, y_pred)))
+print("Precision: {:.4f}".format(precision_score(y, y_pred)))
+print("Recall: {:.4f}".format(recall_score(y, y_pred)))
+print("F1 Score: {:.4f}".format(f1_score(y, y_pred)))
+print("ROC-AUC: {:.4f}".format(roc_auc_score(y, y_proba)))
+print("")
+print("XGBoost Cross-Validation Sonuçları (Macro):")
+print("--------------------------------------------")
+print("Accuracy: {:.4f}".format(accuracy_score(y, y_pred)))
+print("Precision: {:.4f}".format(precision_score(y, y_pred, average='macro')))
+print("Recall: {:.4f}".format(recall_score(y, y_pred, average='macro')))
+print("F1 Score: {:.4f}".format(f1_score(y, y_pred, average='macro')))
+print("ROC-AUC: {:.4f}".format(roc_auc_score(y, y_proba)))
+
+# Modeli eğit ve kaydet
+pipe.fit(X_selected, y)
+joblib.dump(pipe, 'churn_model.pkl')
+joblib.dump(top_15_features, "model_features.pkl")

deneme.py

@@ -0,0 +1,73 @@
+import joblib
+import pandas as pd
+
+# 🔁 Kaydedilen modeli ve özellik listesini yükle
+model = joblib.load("churn_model.pkl")
+model_features = joblib.load("model_features.pkl")
+
+# 🔍 Müşteri verisi (güncel örneğe göre)
+customer = {
+    'gender': True,  # Female → True
+    'SeniorCitizen': False,
+    'Partner': True,
+    'Dependents': False,
+    'PhoneService': True,
+    'PaperlessBilling': True,
+    'tenure': 28,
+    'MonthlyCharges': 104.8,
+    'TotalCharges': 3046.05,
+    
+    'MultipleLines_No': False,
+    'MultipleLines_No phone service': False,
+    'MultipleLines_Yes': True,
+
+    'InternetService_DSL': False,
+    'InternetService_Fiber optic': True,
+    'InternetService_No': False,
+
+    'OnlineSecurity_No': True,
+    'OnlineSecurity_Yes': False,
+    'OnlineSecurity_No internet service': False,
+
+    'OnlineBackup_No': True,
+    'OnlineBackup_Yes': False,
+    'OnlineBackup_No internet service': False,
+
+    'DeviceProtection_No': False,
+    'DeviceProtection_Yes': True,
+    'DeviceProtection_No internet service': False,
+
+    'TechSupport_No': False,
+    'TechSupport_Yes': True,
+    'TechSupport_No internet service': False,
+
+    'StreamingTV_No': False,
+    'StreamingTV_Yes': True,
+    'StreamingTV_No internet service': False,
+
+    'StreamingMovies_No': False,
+    'StreamingMovies_Yes': True,
+    'StreamingMovies_No internet service': False,
+
+    'Contract_Month-to-month': True,
+    'Contract_One year': False,
+    'Contract_Two year': False,
+
+    'PaymentMethod_Bank transfer (automatic)': False,
+    'PaymentMethod_Credit card (automatic)': False,
+    'PaymentMethod_Electronic check': True,
+    'PaymentMethod_Mailed check': False
+}
+
+# Eksik kalan tüm özellikleri sıfırla
+full_input = {col: customer.get(col, 0) for col in model_features}
+X_test = pd.DataFrame([full_input])
+
+# 🎯 Tahmin ve olasılık
+prediction = model.predict(X_test)[0]
+proba = model.predict_proba(X_test)[0][1]
+
+# 📝 Sonuç
+label = "CHURN edecek" if prediction == 1 else "Kalacak"
+print(f"📊 Tahmin: {label}")
+print(f"🎯 Churn olasılığı: %{proba * 100:.2f}")

gradio_app.py

@@ -0,0 +1,237 @@
+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.metrics.pairwise import euclidean_distances
+import random
+
+model = joblib.load("churn_model.pkl")
+model_features = joblib.load("model_features.pkl")
+
+# Load the customer data
+customer_df = pd.read_csv("Telco-Customer-Churn.csv")
+
+customer_df['MonthlyCharges'] = pd.to_numeric(customer_df['MonthlyCharges'], errors='coerce').fillna(0)
+customer_df['TotalCharges'] = pd.to_numeric(customer_df['TotalCharges'], errors='coerce').fillna(0)
+customer_df['tenure'] = pd.to_numeric(customer_df['tenure'], errors='coerce').fillna(0)
+
+value_map = {
+    "Aydan aya": "Month-to-month",
+    "1 yıllık": "One year",
+    "2 yıllık": "Two year",
+    "Elektronik çek": "Electronic check",
+    "Posta çeki": "Mailed check",
+    "Banka havalesi (otomatik)": "Bank transfer (automatic)",
+    "Kredi kartı (otomatik)": "Credit card (automatic)",
+    "Hayır": "No",
+    "Evet": "Yes",
+    "Yok": "No internet service",
+    "Telefon hizmeti yok": "No phone service",
+    "Fiber optik": "Fiber optic",
+    "Erkek": "Male",
+    "Kadın": "Female"
+}
+
+def customer_to_features(row):
+    # Build a feature dict for a customer row, using the same logic as predict_churn
+    input_dict = {}
+    tenure = row['tenure']
+    monthly = row['MonthlyCharges']
+    total = row['TotalCharges']
+    input_dict["tenure"] = tenure
+    input_dict["PhoneService"] = row['PhoneService'] == "Yes"
+    input_dict["avg_charge_per_month"] = total / tenure if tenure > 0 else 0
+    input_dict["charge_ratio"] = total / (monthly * tenure) if monthly > 0 and tenure > 0 else 1
+    tenure_label = "0-12" if tenure <= 12 else "12-24" if tenure <= 24 else "24+"
+    for bin_label in ["0-12", "12-24", "24+"]:
+        input_dict[f"tenure_bin_{bin_label}"] = (tenure_label == bin_label)
+    contract_value = row['Contract']
+    input_dict["is_long_term_contract"] = contract_value in ["One year", "Two year"]
+    categorical_fields = [
+        "gender", "SeniorCitizen", "Partner", "Dependents", "PaperlessBilling",
+        "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
+        "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
+        "Contract", "PaymentMethod"
+    ]
+    for field in categorical_fields:
+        raw_value = row[field]
+        mapped_value = value_map.get(raw_value, raw_value)
+        for col in model_features:
+            if col.startswith(f"{field}_"):
+                input_dict[col] = (col == f"{field}_{mapped_value}")
+    for col in model_features:
+        if col not in input_dict:
+            input_dict[col] = 0 if col == "tenure" or "charge" in col or "avg" in col else False
+    return [input_dict[col] for col in model_features]
+
+# Precompute all customer feature vectors
+customer_feature_matrix = np.vstack([customer_to_features(row) for _, row in customer_df.iterrows()])
+
+def autofill_random_customer():
+    row = customer_df.sample(1).iloc[0]
+    # Map English values back to Turkish for dropdowns
+    reverse_map = {v: k for k, v in value_map.items()}
+    def rev(val):
+        return reverse_map.get(val, val)
+    # Ensure dropdown values are valid
+    def safe(val, allowed):
+        v = rev(val)
+        return v if v in allowed else allowed[0]
+    return [
+        float(row['tenure']),
+        float(row['MonthlyCharges']),
+        float(row['TotalCharges']),
+        safe(row['PhoneService'], phone_service_options),
+        safe(row['gender'], gender_options),
+        'Evet' if row['SeniorCitizen'] == 1 else 'Hayır',
+        'Evet' if row['Partner'] == 'Yes' else 'Hayır',
+        'Evet' if row['Dependents'] == 'Yes' else 'Hayır',
+        'Evet' if row['PaperlessBilling'] == 'Yes' else 'Hayır',
+        safe(row['MultipleLines'], multiple_lines_options),
+        safe(row['InternetService'], internet_service_options),
+        safe(row['OnlineSecurity'], online_security_options),
+        safe(row['OnlineBackup'], online_backup_options),
+        safe(row['DeviceProtection'], device_protection_options),
+        safe(row['TechSupport'], tech_support_options),
+        safe(row['StreamingTV'], streaming_tv_options),
+        safe(row['StreamingMovies'], streaming_movies_options),
+        safe(row['Contract'], contract_options),
+        safe(row['PaymentMethod'], payment_method_options)
+    ]
+
+def find_similar_customers_vector(input_vector, n=5):
+    dists = euclidean_distances(customer_feature_matrix, input_vector.reshape(1, -1)).flatten()
+    top_idx = np.argsort(dists)[:n]
+    print("Top distances:", dists[top_idx])
+    print("Top indices:", top_idx)
+    return customer_df.iloc[top_idx][['customerID','gender','SeniorCitizen','Partner','Dependents','tenure','Contract','PaymentMethod','MonthlyCharges','TotalCharges','Churn']]
+
+def predict_churn(
+    tenure, monthly, total, PhoneService, gender, SeniorCitizen, Partner, Dependents, PaperlessBilling,
+    MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+    StreamingTV, StreamingMovies, Contract, PaymentMethod
+):
+    # Ensure numeric types
+    tenure = float(tenure)
+    monthly = float(monthly)
+    total = float(total)
+    input_dict = {}
+    input_dict["tenure"] = tenure
+    input_dict["PhoneService"] = PhoneService == "Evet"
+    input_dict["avg_charge_per_month"] = total / tenure if tenure > 0 else 0
+    input_dict["charge_ratio"] = total / (monthly * tenure) if monthly > 0 and tenure > 0 else 1
+    tenure_label = "0-12" if tenure <= 12 else "12-24" if tenure <= 24 else "24+"
+    for bin_label in ["0-12", "12-24", "24+"]:
+        input_dict[f"tenure_bin_{bin_label}"] = (tenure_label == bin_label)
+    contract_value = value_map.get(Contract, Contract)
+    input_dict["is_long_term_contract"] = contract_value in ["One year", "Two year"]
+    categorical_fields = [
+        "gender", "SeniorCitizen", "Partner", "Dependents", "PaperlessBilling",
+        "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
+        "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
+        "Contract", "PaymentMethod"
+    ]
+    form = {
+        "gender": gender,
+        "SeniorCitizen": SeniorCitizen,
+        "Partner": Partner,
+        "Dependents": Dependents,
+        "PaperlessBilling": PaperlessBilling,
+        "MultipleLines": MultipleLines,
+        "InternetService": InternetService,
+        "OnlineSecurity": OnlineSecurity,
+        "OnlineBackup": OnlineBackup,
+        "DeviceProtection": DeviceProtection,
+        "TechSupport": TechSupport,
+        "StreamingTV": StreamingTV,
+        "StreamingMovies": StreamingMovies,
+        "Contract": Contract,
+        "PaymentMethod": PaymentMethod
+    }
+    for field in categorical_fields:
+        raw_value = form[field]
+        mapped_value = value_map.get(raw_value, raw_value)
+        for col in model_features:
+            if col.startswith(f"{field}_"):
+                input_dict[col] = (col == f"{field}_{mapped_value}")
+    for col in model_features:
+        if col not in input_dict:
+            input_dict[col] = 0 if col == "tenure" or "charge" in col or "avg" in col else False
+    input_df = pd.DataFrame([[input_dict[col] for col in model_features]], columns=model_features)
+    prediction = model.predict_proba(input_df)[0][1]
+    score = round(prediction * 100, 2)
+    if score >= 50:
+        comment = "Müşteri Kaybedilebilir."
+    else:
+        comment = "Müşteri Kayıp Riski Taşımıyor."
+    result = f"Churn Riski: %{score} — {comment}"
+    # Vector similarity
+    similar_customers = find_similar_customers_vector(input_df.values[0], n=5)
+    return result, similar_customers
+
+# Define options for dropdowns (Turkish values)
+phone_service_options = ["Evet", "Hayır"]
+gender_options = ["Erkek", "Kadın"]
+senior_citizen_options = ["Evet", "Hayır"]
+partner_options = ["Evet", "Hayır"]
+dependents_options = ["Evet", "Hayır"]
+paperless_billing_options = ["Evet", "Hayır"]
+multiple_lines_options = ["Hayır", "Evet", "Telefon hizmeti yok"]
+internet_service_options = ["DSL", "Fiber optik", "Yok"]
+online_security_options = ["Hayır", "Evet", "Yok"]
+online_backup_options = ["Hayır", "Evet", "Yok"]
+device_protection_options = ["Hayır", "Evet", "Yok"]
+tech_support_options = ["Hayır", "Evet", "Yok"]
+streaming_tv_options = ["Hayır", "Evet", "Yok"]
+streaming_movies_options = ["Hayır", "Evet", "Yok"]
+contract_options = ["Aydan aya", "1 yıllık", "2 yıllık"]
+payment_method_options = [
+    "Elektronik çek", "Posta çeki", "Banka havalesi (otomatik)", "Kredi kartı (otomatik)"
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Müşteri Churn Tahmini")
+    with gr.Row():
+        tenure = gr.Number(label="Kullanım Süresi (tenure)", value=1)
+        monthly = gr.Number(label="Aylık Ücret (MonthlyCharges)", value=1)
+        total = gr.Number(label="Toplam Ücret (TotalCharges)", value=1)
+    with gr.Row():
+        PhoneService = gr.Dropdown(phone_service_options, label="Telefon Hizmeti (PhoneService)")
+        gender = gr.Dropdown(gender_options, label="Cinsiyet (gender)")
+        SeniorCitizen = gr.Dropdown(senior_citizen_options, label="Kıdemli Vatandaş (SeniorCitizen)")
+        Partner = gr.Dropdown(partner_options, label="Partner")
+        Dependents = gr.Dropdown(dependents_options, label="Bağımlılar (Dependents)")
+        PaperlessBilling = gr.Dropdown(paperless_billing_options, label="Kağıtsız Fatura (PaperlessBilling)")
+    with gr.Row():
+        MultipleLines = gr.Dropdown(multiple_lines_options, label="Çoklu Hat (MultipleLines)")
+        InternetService = gr.Dropdown(internet_service_options, label="İnternet Servisi (InternetService)")
+        OnlineSecurity = gr.Dropdown(online_security_options, label="Online Güvenlik (OnlineSecurity)")
+        OnlineBackup = gr.Dropdown(online_backup_options, label="Online Yedekleme (OnlineBackup)")
+        DeviceProtection = gr.Dropdown(device_protection_options, label="Cihaz Koruma (DeviceProtection)")
+        TechSupport = gr.Dropdown(tech_support_options, label="Teknik Destek (TechSupport)")
+        StreamingTV = gr.Dropdown(streaming_tv_options, label="TV Yayını (StreamingTV)")
+        StreamingMovies = gr.Dropdown(streaming_movies_options, label="Film Yayını (StreamingMovies)")
+    with gr.Row():
+        Contract = gr.Dropdown(contract_options, label="Sözleşme (Contract)")
+        PaymentMethod = gr.Dropdown(payment_method_options, label="Ödeme Yöntemi (PaymentMethod)")
+    autofill_btn = gr.Button("Rastgele Müşteri ile Doldur")
+    submit_btn = gr.Button("Tahmin Et")
+    output = gr.Textbox(label="Sonuç")
+    similar_customers_table = gr.Dataframe(label="Benzer Müşteriler (İlk 5)")
+    autofill_btn.click(
+        autofill_random_customer,
+        inputs=[],
+        outputs=[tenure, monthly, total, PhoneService, gender, SeniorCitizen, Partner, Dependents, PaperlessBilling,
+                MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+                StreamingTV, StreamingMovies, Contract, PaymentMethod]
+    )
+    submit_btn.click(
+        predict_churn,
+        inputs=[tenure, monthly, total, PhoneService, gender, SeniorCitizen, Partner, Dependents, PaperlessBilling,
+                MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+                StreamingTV, StreamingMovies, Contract, PaymentMethod],
+        outputs=[output, similar_customers_table]
+    )
+
+if __name__ == "__main__":
+    demo.launch() 

model_features.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc7c1d59e2bd2dfce4329ee3949ad58f3e2723bc32b8263cc1a81bec702d4b6b
+size 324

requirements.txt

@@ -0,0 +1,10 @@
+gradio
+Flask
+pandas
+joblib
+scikit-learn
+xgboost
+imbalanced-learn
+matplotlib
+seaborn
+numpy

templates/index.html

@@ -0,0 +1,149 @@
+<!DOCTYPE html>
+<html lang="tr">
+<head>
+    <meta charset="UTF-8">
+    <title>Müşteri Churn Tahmini</title>
+    <style>
+        body {
+            background-color: #d9dafa;
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            color: #564dbf;
+            padding: 40px;
+        }
+
+        h2 {
+            color: #1a1636;
+        }
+
+        form {
+            background-color: #c2baff;
+            border: 2px solid #8780bf;
+            border-radius: 12px;
+            padding: 30px;
+            width: 450px;
+            box-shadow: 2px 2px 10px #3c30e3;
+        }
+
+        label {
+            display: block;
+            margin-top: 12px;
+            font-weight: bold;
+        }
+
+        input, select {
+            width: 100%;
+            padding: 8px;
+            margin-top: 5px;
+            border: 1px solid #564dbf;
+            border-radius: 6px;
+        }
+
+        input[type="submit"] {
+            background-color: #7791d9;
+            color: white;
+            font-weight: bold;
+            cursor: pointer;
+            margin-top: 20px;
+        }
+
+        input[type="submit"]:hover {
+            background-color: #1a1636;
+        }
+
+        h3 {
+            margin-top: 30px;
+            color: #1a1636;
+        }
+    </style>
+</head>
+<body>
+    <h2>Müşteri Bilgileri</h2>
+    <form method="POST">
+        <label>Cinsiyet:</label>
+        <select name="gender"><option>Kadın</option><option>Erkek</option></select>
+
+        <label>65 yaş ve üstü mü?</label>
+        <select name="SeniorCitizen"><option>Hayır</option><option>Evet</option></select>
+
+        <label>Evli mi?</label>
+        <select name="Partner"><option>Hayır</option><option>Evet</option></select>
+
+        <label>Bakmakla yükümlü olduğu kişi var mı?</label>
+        <select name="Dependents"><option>Hayır</option><option>Evet</option></select>
+
+        <label>Telefon hizmeti var mı?</label>
+        <select name="PhoneService"><option>Hayır</option><option>Evet</option></select>
+
+        <label>Kağıtsız fatura kullanıyor mu?</label>
+        <select name="PaperlessBilling"><option>Hayır</option><option>Evet</option></select>
+
+        <label>Abonelik süresi (ay):</label>
+        <input type="number" step="1" name="tenure" value="0" required>
+
+        <label>Aylık ücret ($):</label>
+        <input type="number" step="0.01" name="MonthlyCharges" value="0.00" required>
+
+        <label>Toplam ücret ($):</label>
+        <input type="number" step="0.01" name="TotalCharges" value="0.00" required>
+
+        <label>Çoklu hat durumu:</label>
+        <select name="MultipleLines">
+            <option>Hayır</option><option>Evet</option><option>Telefon hizmeti yok</option>
+        </select>
+
+        <label>İnternet hizmeti:</label>
+        <select name="InternetService">
+            <option>DSL</option><option>Fiber optik</option><option>Yok</option>
+        </select>
+
+        <label>Online güvenlik:</label>
+        <select name="OnlineSecurity">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>Online yedekleme:</label>
+        <select name="OnlineBackup">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>Cihaz koruma:</label>
+        <select name="DeviceProtection">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>Teknik destek:</label>
+        <select name="TechSupport">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>TV yayını:</label>
+        <select name="StreamingTV">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>Film yayını:</label>
+        <select name="StreamingMovies">
+            <option>Hayır</option><option>Evet</option><option>İnternet hizmeti yok</option>
+        </select>
+
+        <label>Abonelik türü:</label>
+        <select name="Contract">
+            <option>Aydan aya</option><option>1 yıllık</option><option>2 yıllık</option>
+        </select>
+
+        <label>Ödeme yöntemi:</label>
+        <select name="PaymentMethod">
+            <option>Elektronik çek</option>
+            <option>Posta çeki</option>
+            <option>Banka havalesi (otomatik)</option>
+            <option>Kredi kartı (otomatik)</option>
+        </select>
+
+        <input type="submit" value="Tahmin Et">
+    </form>
+
+    {% if result %}
+        <h3>{{ result }}</h3>
+    {% endif %}
+</body>
+</html>