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README.md

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----
-license: mit
----
+online-retail-segmentasyon/
+├── kmeans_model.pkl
+├── scaler.pkl
+├── README.md
+├── sample_input.json         ← Örnek: {"Recency": 20, "Frequency": 5, "Monetary": 1000}
+
+
+
+# 🧠 Online Retail Müşteri Segmentasyon Modeli (K-Means)
+
+Bu model, RFM (Recency, Frequency, Monetary) bilgilerine göre müşterileri segmentlere ayırır. Eğitilmiş bir K-Means modelidir.
+
+## 🔢 Girdi
+```json
+{
+  "Recency": 20,
+  "Frequency": 5,
+  "Monetary": 1000
+}
+
+
+🛠️ Nasıl Kullanılır?
+import joblib
+import numpy as np
+
+model = joblib.load("kmeans_model.pkl")
+scaler = joblib.load("scaler.pkl")
+
+data = scaler.transform([[20, 5, 1000]])
+segment = model.predict(data)[0]
+print("Segment:", segment)

app.py

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+# app.py
+
+import streamlit as st
+import numpy as np
+import joblib
+
+# Model ve scaler yükle
+kmeans = joblib.load("kmeans_model.pkl")
+scaler = joblib.load("scaler.pkl")
+
+st.title("🛍️ Online Retail Müşteri Segmentasyonu")
+
+st.markdown("Müşterinin Recency, Frequency, Monetary bilgilerini girin:")
+
+# Kullanıcı girişi
+recency = st.number_input("Recency (Son alışveriş gün farkı)", min_value=0)
+frequency = st.number_input("Frequency (Sipariş sayısı)", min_value=0)
+monetary = st.number_input("Monetary (Toplam harcama)", min_value=0)
+
+if st.button("Segmenti Tahmin Et"):
+    input_data = np.array([[recency, frequency, monetary]])
+    input_scaled = scaler.transform(input_data)
+    cluster = kmeans.predict(input_scaled)[0]
+    st.success(f"🧠 Bu müşteri Segment {cluster} grubuna aittir.")

kmeans_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d9278499823dc9ce92269be5f28871c40c472771ffa4a361a16f6fa35cbdf68
+size 18191

requirements.txt

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+streamlit
+scikit-learn
+pandas
+numpy
+joblib
+openpyxl

sample_input.json

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+{
+  "Recency": 20,
+  "Frequency": 5,
+  "Monetary": 1000
+}

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f36879a99ffed17fac8a9008db6805218a60a78b63704cf090339875119cb23
+size 1007