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README.md

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----
-license: mit
----
+# 🍄 Mushroom Classification with Machine Learning
+
+This project uses machine learning to classify mushrooms as **edible** (`e`) or **poisonous** (`p`) based on various morphological features.
+
+---
+
+## 📁 Dataset
+
+- **Source**: [UCI Mushroom Dataset](https://archive.ics.uci.edu/dataset/73/mushroom)
+- **Samples**: 8124
+- **Original Features**: 22 categorical (e.g., cap-shape, odor, stalk-root)
+- **Preprocessing**: One-Hot Encoding applied for model compatibility
+
+---
+
+## 🧠 Model Information
+
+- **Algorithm**: Decision Tree Classifier  
+- **Training/Test Split**: 80% / 20%  
+- **Cross-Validation**: 5-Fold (Average Accuracy: ~96.6%)  
+- **Test Accuracy**: ~100%
+
+### 🔍 Feature Importance (Top 5)
+
+Based on the Decision Tree model:
+
+1. `odor=n`
+2. `stalk-root=c`
+3. `spore-print-color=r`
+4. `stalk-surface-below-ring=y`
+5. `habitat=d`
+
+---
+
+## ⚙️ How It Works
+
+You provide one-hot encoded features like `cap-shape=c`, `odor=n`, etc.  
+The model then predicts:
+
+- `"e"` → Edible  
+- `"p"` → Poisonous
+
+Sample input format is shown in `sample_input.json`.
+
+---
+
+## 🚀 Quick Usage (Python)
+
+```python
+import joblib
+import pandas as pd
+
+model = joblib.load("mushroom_model.pkl")
+
+sample = pd.DataFrame([{
+    "cap-shape=c": 1,
+    "cap-color=n": 1,
+    "odor=n": 1,
+    ...
+}])
+
+prediction = model.predict(sample)[0]
+print("Prediction:", prediction)
+
+
+📦 Project Files
+File Name	Description
+mushroom_model.pkl	Trained Decision Tree model
+sample_input.json	Example of one-hot encoded input
+model.py	Script for model training
+app.py	Streamlit web interface
+README.md	This project explanation file
+requirements.txt	Python dependencies
+
+
+ How to Run Locally
+Install dependencies:
+
+pip install -r requirements.txt
+
+Launch the Streamlit app:
+streamlit run app.py
+
+
+🌐 Live Demo and Deployment
+You can deploy this model to:
+
+Hugging Face for API access and hosting the model
+
+GitHub for open sharing and collaboration
+
+Streamlit Cloud for an interactive app
+
+
+🧪 Model Testing on Hugging Face
+You can test the model by uploading:
+
+mushroom_model.pkl
+
+sample_input.json
+
+requirements.txt
+
+README.md
+
+Visit: https://huggingface.co (yazodi)
+
+📄 License
+MIT License – for educational and non-commercial purposes.

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+
+# Modeli yükle
+model = joblib.load("mushroom_model.pkl")
+
+# Özellikler listesi
+features = [
+    'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
+    'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
+    'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
+    'stalk-surface-below-ring', 'stalk-color-above-ring',
+    'stalk-color-below-ring', 'veil-type', 'veil-color',
+    'ring-number', 'ring-type', 'spore-print-color',
+    'population', 'habitat'
+]
+
+# Başlık
+st.title("🍄 Mushroom Classification")
+st.write("Bu uygulama, verilen mantar özelliklerine göre yenilebilir mi yoksa zehirli mi olduğunu tahmin eder.")
+
+# Kullanıcı girişleri için alanlar
+user_input = {}
+for feature in features:
+    user_input[feature] = st.selectbox(f"{feature.replace('-', ' ').capitalize()}:", 
+                                       ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])
+
+# Tahmin butonu
+if st.button("Tahmin Et"):
+    input_df = pd.DataFrame([user_input])
+    prediction = model.predict(input_df)[0]
+
+    if prediction == 'e':
+        st.success("✅ Tahmin: Edible (Yenilebilir)")
+    else:
+        st.error("❌ Tahmin: Poisonous (Zehirli)")

model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+import joblib
+
+# Veriyi yükle
+df = pd.read_csv("mushroom.csv")
+
+# Hedef değişkeni oluştur (class=e sütunundan)
+df["target"] = df["class=e"].apply(lambda x: 'e' if x == 1 else 'p')
+
+# Özelliklerden class=e ve class=p sütunlarını çıkar
+X = df.drop(columns=["class=e", "class=p", "target"])
+y = df["target"]
+
+# Eğitim/test ayrımı
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model oluştur ve eğit
+model = DecisionTreeClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+# Kaydet
+joblib.dump(model, "mushroom_model.pkl")
+print("✅ Model eğitildi ve mushroom_model.pkl olarak kaydedildi.")

mushroom_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f65d8c3ac0f6f49c1232232e1c07760874e8bf14050a8daec3dea7d31e9adce
+size 6457

requirements.txt

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+pandas
+scikit-learn
+streamlit
+joblib

sample_input.json

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+{
+  "cap-shape=b": 0,
+  "cap-shape=c": 1,
+  "cap-shape=f": 0,
+  "cap-shape=k": 0,
+  ...
+  "habitat=u": 0,
+  "habitat=w": 1
+}