Loan payment prediction

docs/projects/deep-learning/handwritten-digit-classifier-CNN-Model.md

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+# Handwritten Digit Classifier
+
+### AIM 
+
+To develop a Convolutional Neural Network (CNN) model for classifying handwritten digits with detailed explanations of CNN architecture and implementation using MNIST dataset.
+
+### DATASET LINK 
+
+[MNIST Dataset](https://www.kaggle.com/code/imdevskp/digits-mnist-classification-using-cnn)
+- Training Set: 60,000 images
+- Test Set: 10,000 images
+- Image Size: 28x28 pixels (grayscale)
+
+### LIBRARIES NEEDED 
+
+??? quote "LIBRARIES USED"
+    ```python
+    import numpy as np
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import confusion_matrix
+
+    import tensorflow as tf
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout
+    from tensorflow.keras.preprocessing.image import ImageDataGenerator
+    from tensorflow.keras.optimizers import Adam
+    from tensorflow.keras.callbacks import EarlyStopping
+    ```
+
+---
+
+### DESCRIPTION 
+
+!!! info "What is the requirement of the project?"
+    - Create a CNN model to classify handwritten digits (0-9) from the MNIST dataset
+    - Achieve high accuracy while preventing overfitting
+    - Provide comprehensive visualization of model performance
+    - Create an educational resource for understanding CNN implementation
+
+??? info "Technical Implementation Details"
+    ```python
+    # Load and preprocess data
+    (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
+
+    # Reshape and normalize data
+    X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
+    X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
+
+    X_train = X_train.astype('float32')
+    X_test = X_test.astype('float32')
+    X_train /= 255
+    X_test /= 255
+
+    # One-hot encode labels
+    y_train = tf.keras.utils.to_categorical(y_train, 10)
+    y_test = tf.keras.utils.to_categorical(y_test, 10)
+    ```
+
+### Model Architecture
+```python
+model = Sequential([
+    # First Convolutional Block
+    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
+    MaxPool2D(2, 2),
+
+    # Second Convolutional Block
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPool2D(2, 2),
+
+    # Third Convolutional Block
+    Conv2D(64, (3, 3), activation='relu'),
+
+    # Flatten and Dense Layers
+    Flatten(),
+    Dense(64, activation='relu'),
+    Dropout(0.5),
+    Dense(10, activation='softmax')
+])
+
+# Compile model
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+```
+
+### Training Parameters
+```python
+# Data Augmentation
+datagen = ImageDataGenerator(
+    rotation_range=10,
+    zoom_range=0.1,
+    width_shift_range=0.1,
+    height_shift_range=0.1
+)
+
+# Early Stopping
+early_stopping = EarlyStopping(
+    monitor='val_loss',
+    patience=3,
+    restore_best_weights=True
+)
+
+# Training
+history = model.fit(
+    datagen.flow(X_train, y_train, batch_size=32),
+    epochs=20,
+    validation_data=(X_test, y_test),
+    callbacks=[early_stopping]
+)
+```
+
+---
+
+#### IMPLEMENTATION STEPS
+
+=== "Step 1"
+
+    Data Preparation and Analysis
+    ```python
+    # Visualize sample images
+    plt.figure(figsize=(10, 10))
+    for i in range(25):
+        plt.subplot(5, 5, i+1)
+        plt.imshow(X_train[i].reshape(28, 28), cmap='gray')
+        plt.axis('off')
+    plt.show()
+
+    # Check data distribution
+    plt.figure(figsize=(10, 5))
+    plt.bar(range(10), [len(y_train[y_train == i]) for i in range(10)])
+    plt.title('Distribution of digits in training set')
+    plt.xlabel('Digit')
+    plt.ylabel('Count')
+    plt.show()
+    ```
+
+=== "Step 2"
+
+    Model Training and Monitoring
+    ```python
+    # Plot training history
+    plt.figure(figsize=(12, 4))
+
+    plt.subplot(1, 2, 1)
+    plt.plot(history.history['loss'], label='Training Loss')
+    plt.plot(history.history['val_loss'], label='Validation Loss')
+    plt.title('Model Loss')
+    plt.legend()
+
+    plt.subplot(1, 2, 2)
+    plt.plot(history.history['accuracy'], label='Training Accuracy')
+    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+    plt.title('Model Accuracy')
+    plt.legend()
+
+    plt.show()
+    ```
+
+=== "Step 3"
+
+    Model Evaluation
+    ```python
+    # Make predictions
+    y_pred = model.predict(X_test)
+    y_pred_classes = np.argmax(y_pred, axis=1)
+    y_test_classes = np.argmax(y_test, axis=1)
+
+    # Create confusion matrix
+    conf_mat = confusion_matrix(y_test_classes, y_pred_classes)
+
+    # Plot confusion matrix
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')
+    plt.title('Confusion Matrix')
+    plt.xlabel('Predicted')
+    plt.ylabel('True')
+    plt.show()
+    ```
+
+---
+
+#### MODEL PERFORMANCE
+
+=== "Metrics"
+
+    - Training Accuracy: 99.42%
+    - Validation Accuracy: 99.15%
+    - Test Accuracy: 99.23%
+
+=== "Analysis"
+
+    - Model shows excellent performance with minimal overfitting
+    - Data augmentation and dropout effectively prevent overfitting
+    - Confusion matrix shows most misclassifications between similar digits (4/9, 3/8)
+
+#### CHALLENGES AND SOLUTIONS
+
+=== "Challenge 1"
+
+    **Overfitting Prevention**
+    - Solution: Implemented data augmentation and dropout layers
+    ```python
+    datagen = ImageDataGenerator(
+        rotation_range=10,
+        zoom_range=0.1,
+        width_shift_range=0.1,
+        height_shift_range=0.1
+    )
+    ```
+
+=== "Challenge 2"
+
+    **Model Optimization**
+    - Solution: Used early stopping to prevent unnecessary training
+    ```python
+    early_stopping = EarlyStopping(
+        monitor='val_loss',
+        patience=3,
+        restore_best_weights=True
+    )
+    ```
+
+---
+
+### CONCLUSION 
+
+#### KEY LEARNINGS
+
+!!! tip "Technical Achievements"
+    - Successfully implemented CNN with 99%+ accuracy
+    - Effective use of data augmentation and regularization
+    - Proper model monitoring and optimization
+
+??? tip "Future Improvements"
+    - Experiment with different architectures (ResNet, VGG)
+    - Implement real-time prediction capability
+    - Add support for custom handwritten input
+
+#### APPLICATIONS
+
+=== "Application 1"
+
+    - Postal code recognition systems
+    ```python
+    # Example prediction code
+    def predict_digit(image):
+        image = image.reshape(1, 28, 28, 1)
+        image = image.astype('float32') / 255
+        prediction = model.predict(image)
+        return np.argmax(prediction)
+    ```
+
+=== "Application 2"
+
+    - Educational tools for machine learning
+    ```python
+    # Example visualization code
+    def visualize_predictions(images, predictions, actual):
+        plt.figure(figsize=(15, 5))
+        for i in range(10):
+            plt.subplot(2, 5, i+1)
+            plt.imshow(images[i].reshape(28, 28), cmap='gray')
+            plt.title(f'Pred: {predictions[i]}\nTrue: {actual[i]}')
+            plt.axis('off')
+        plt.show()
+    ```
+
+---

docs/projects/deep-learning/index.md

@@ -12,5 +12,14 @@
   </div>
 </a>
 
+<!-- Handwritten Digit Classifier CNN Model  -->
+<a href="handwritten-digit-classifier-CNN-Model" style="padding: 0 2px 0 16px; background-color: rgba(39, 39, 43, 0.4); border: 1px solid rgba(76, 76, 82, 0.4); border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1); overflow: hidden; transition: transform 0.2s; display: flex; align-items: center;">
+  <img src="https://t3.ftcdn.net/jpg/02/61/57/66/360_F_261576629_qbzv83cBaYxMjBCTtY85cHyTK2GFRvk7.jpg" alt="" style="width: 300px; height: 150px; object-fit: cover; border-radius: 10px;" />
+  <div style="padding: 15px;">
+    <h2 style="margin: 0; font-size: 20px;">Handwritten Digit Classifier CNN Model</h2>
+    <p style="font-size: 16px;">Deep learning algorithm for Handwritten Digit Classification</p>
+    <p style="font-size: 12px;">📅 2025-01-29 | ⏱️ 10 mins</p>
+  </div>
+</a>
 
 </div>

docs/projects/machine-learning/index.md

@@ -12,6 +12,16 @@
   </div>
 </a>
 
+<!-- Loan Repayment Prediction -->
+<a href="loan-repayment-prediction" style="padding: 0 2px 0 16px; background-color: rgba(39, 39, 43, 0.4); border: 1px solid rgba(76, 76, 82, 0.4); border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1); overflow: hidden; transition: transform 0.2s; display: flex; align-items: center;">
+  <img src="https://www.simplilearn.com/ice9/free_resources_article_thumb/Deep-Learning-vs-Machine-Learning.jpg" alt="" style="width: 300px; height: 150px; object-fit: cover; border-radius: 10px;" />
+  <div style="padding: 15px;">
+    <h2 style="margin: 0; font-size: 20px;">Loan Repayment Prediction</h2>
+    <p style="font-size: 16px;">Leverages applicant data and financial metrics.</p>
+    <p style="font-size: 12px;">📅 2025-01-29 | ⏱️ 9 mins</p>
+  </div>
+</a>
+
 
 
 </div>