Update README.md

README.md

@@ -1,3 +1,99 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: image-classification
+---
+
+## 📂 Project Structure
+
+```
+.
+├── image_classifier.h5     # Trained model
+├── main.py                 # Training & prediction script
+└── README.md               # Project description
+```
+
+---
+
+## ⚙️ Technologies Used
+
+* Python 3.10+
+* TensorFlow / Keras
+* NumPy
+* Matplotlib
+
+---
+
+## 📊 Dataset
+
+The dataset is from **Kaggle Multi-Cancer Dataset**:
+
+```
+/kaggle/input/multi-cancer/Multi Cancer/Multi Cancer/Breast Cancer
+```
+
+Images are split into **90% training** and **10% validation** using `ImageDataGenerator`.
+
+---
+
+## 🏗️ Model Architecture
+
+* **Conv2D (32 filters, 3x3, ReLU)**
+* **MaxPooling2D (2x2)**
+* **Conv2D (64 filters, 3x3, ReLU)**
+* **MaxPooling2D (2x2)**
+* **Conv2D (128 filters, 3x3, ReLU)**
+* **MaxPooling2D (2x2)**
+* **Flatten**
+* **Dense (512, ReLU)**
+* **Dense (Softmax output, # of classes)**
+
+Optimizer: **Adam**
+Loss: **Categorical Crossentropy**
+Metric: **Accuracy**
+
+---
+
+## 🚀 Training
+
+```python
+model.fit(train_generator, validation_data=validation_generator, epochs=10)
+```
+
+After training, the model is saved as:
+
+```python
+model.save("image_classifier.h5")
+```
+
+---
+
+## 🔮 Prediction Example
+
+```python
+def guess(image_path, model, class_indices):
+    img = load_img(image_path, target_size=(150, 150))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction)
+    class_labels = {v: k for k, v in class_indices.items()}
+    predicted_label = class_labels[predicted_class]
+
+    plt.imshow(img)
+    plt.title(f"Model guess: {predicted_label}")
+    plt.axis("off")
+    plt.show()
+```
+
+---
+
+## ✅ Results
+
+* Trains a CNN model for breast cancer image classification
+* Provides a simple **guess() function** to visualize predictions
+* Model is reusable via `image_classifier.h5`