Update README.md

README.md

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-license: unknown
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+---
+license: unknown
+---
+
+# **Cats, Dogs, and Snakes Dataset**
+
+## **Dataset Overview**
+
+The dataset contains images of **three animal classes**: Cats, Dogs, and Snakes. It is **balanced and cleaned**, designed for supervised image classification tasks.
+
+| Class  | Number of Images | Description                                    |
+| ------ | ---------------- | ---------------------------------------------- |
+| Cats   | 1,000            | Includes multiple breeds and poses             |
+| Dogs   | 1,000            | Covers various breeds and backgrounds          |
+| Snakes | 1,000            | Includes multiple species and natural settings |
+
+**Total Images:** 3,000
+
+**Image Properties:**
+
+* Resolution: 224×224 pixels (resized for consistency)
+* Color Mode: RGB
+* Format: JPEG/PNG
+* Cleaned: Duplicate, blurry, and irrelevant images removed
+
+---
+
+## **Data Split Recommendation**
+
+| Set        | Percentage | Number of Images |
+| ---------- | ---------- | ---------------- |
+| Training   | 70%        | 2,100            |
+| Validation | 15%        | 450              |
+| Test       | 15%        | 450              |
+
+---
+
+## **Preprocessing**
+
+Images in the dataset have been standardized to support machine learning pipelines:
+
+1. **Resizing** to 224×224 pixels.
+2. **Normalization** of pixel values to [0,1] or mean subtraction for deep learning frameworks.
+3. **Label encoding**: Integer encoding (0 = Cat, 1 = Dog, 2 = Snake) or one-hot encoding for model training.
+
+---
+
+## **Example: Loading and Using the Dataset (Python)**
+
+```python
+import os
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Path to dataset
+dataset_path = "path/to/dataset"
+
+# ImageDataGenerator for preprocessing
+datagen = ImageDataGenerator(
+    rescale=1./255,
+    validation_split=0.15  # 15% for validation
+)
+
+# Load training data
+train_generator = datagen.flow_from_directory(
+    dataset_path,
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='categorical',
+    subset='training',
+    shuffle=True
+)
+
+# Load validation data
+validation_generator = datagen.flow_from_directory(
+    dataset_path,
+    target_size=(224, 224),
+    batch_size=32,
+    class_mode='categorical',
+    subset='validation',
+    shuffle=False
+)
+
+# Example: Iterate over one batch
+images, labels = next(train_generator)
+print(images.shape, labels.shape)  # (32, 224, 224, 3) (32, 3)
+```
+
+---
+
+## **Key Features**
+
+* **Balanced:** Equal number of samples per class reduces bias.
+* **Cleaned:** High-quality, relevant images improve model performance.
+* **Diverse:** Covers multiple breeds, species, and environments to ensure generalization.
+* **Ready for ML:** Preprocessed and easily integrated into popular deep learning frameworks.