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+# Practical-2b (Classification using Deep Neural Network - IMDB Dataset)
+
+Problem Statement: Binary classification using Deep Neural Networks Example: Classify movie reviews into positive" reviews and "negative" reviews, just based on the text content of the reviews. Use IMDB dataset
+
+> [!NOTE]
+> Dataset available in [Datasets](../Datasets/IMDB%20Dataset.csv) directory.
+
+---
+
+## Pre-requisities
+
+1. Install packages using `pip`: `pip install tensorflow keras pandas numpy scikit-learn matplotlib seaborn` (`tensorflow` requires Python 3.9 - 3.12)
+2. Copy the `IMDB Dataset.csv` dataset in the same directory as the Jupyter notebook.
+
+## Steps
+
+1. Import Libraries
+2. Load Dataset
+3. Exploratory Data Analysis (EDA)
+4. Data Cleaning - Strip HTML Tags
+5. Encode Labels and Separate Features
+6. Tokenize and Pad Text Sequences
+7. Split into Training and Testing Sets
+8. Build the Neural Network Model
+9. Compile the Model
+10. Train the Model
+11. Evaluate the Model on Test Data
+12. Plot Training vs Validation Accuracy
+13. Plot Training vs Validation Loss
+14. Confusion Matrix and Classification Report
+
+---
+
+## Code
+
+### 1. Import Libraries:
+
+```python3
+import re
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import confusion_matrix, classification_report
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+```
+
+### 2. Load Dataset:
+
+```python3
+data = pd.read_csv('IMDB Dataset.csv')
+print(data.head())
+```
+
+### 3. Exploratory Data Analysis (EDA):
+
+```python3
+print("Shape:", data.shape)
+print("\nMissing Values:\n", data.isnull().sum())
+print("\nClass Distribution:\n", data['sentiment'].value_counts())
+
+# Visualize class distribution
+sns.countplot(x='sentiment', data=data)
+plt.title('Sentiment Class Distribution')
+plt.show()
+
+# Sample reviews
+print("\nSample positive review:\n", data[data['sentiment'] == 'positive']['review'].iloc[0][:300])
+print("\nSample negative review:\n", data[data['sentiment'] == 'negative']['review'].iloc[0][:300])
+```
+
+### 4. Data Cleaning - Strip HTML Tags:
+
+```python3
+def clean_text(text):
+    text = re.sub(r'<.*?>', '', text)   # remove HTML tags like <br />
+    text = text.lower().strip()         # lowercase and trim whitespace
+    return text
+
+data['review'] = data['review'].apply(clean_text)
+print("Sample cleaned review:\n", data['review'].iloc[0][:300])
+```
+
+### 5. Encode Labels and Separate Features:
+
+```python3
+label_encoder = LabelEncoder()
+data['sentiment'] = label_encoder.fit_transform(data['sentiment'])  # positive=1, negative=0
+
+X = data['review'].values    # input: review text
+y = data['sentiment'].values # output: 0 or 1
+```
+
+### 6. Tokenize and Pad Text Sequences:
+
+```python3
+vocab_size = 10000   # keep only top 10,000 most frequent words
+max_length = 200     # truncate/pad all reviews to 200 words
+
+tokenizer = Tokenizer(num_words=vocab_size, oov_token='<OOV>')  # <OOV> handles unknown words
+tokenizer.fit_on_texts(X)                        # build word index from training text
+
+sequences = tokenizer.texts_to_sequences(X)      # convert each word to its integer index
+padded_sequences = pad_sequences(sequences, maxlen=max_length,
+                                 padding='post', truncating='post')  # pad/truncate to fixed length
+```
+
+### 7. Split into Training and Testing Sets:
+
+```python3
+X_train, X_test, y_train, y_test = train_test_split(padded_sequences, y, test_size=0.2, random_state=42)
+```
+
+### 8. Build the Neural Network Model:
+
+```python3
+model = Sequential()
+model.add(Embedding(vocab_size, 16))        # maps each word index to a 16-dim vector
+model.add(GlobalAveragePooling1D())          # averages all word vectors into one vector
+model.add(Dense(24, activation='relu'))      # hidden layer: 24 neurons
+model.add(Dense(1, activation='sigmoid'))    # output: probability between 0 and 1 (binary)
+
+model.summary()
+```
+
+### 9. Compile the Model:
+
+```python3
+# binary_crossentropy: standard loss for binary classification; sigmoid output
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+```
+
+### 10. Train the Model:
+
+```python3
+history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
+```
+
+### 11. Evaluate the Model on Test Data:
+
+```python3
+loss, accuracy = model.evaluate(X_test, y_test)
+print(f"Test Loss: {loss:.4f}")
+print(f"Test Accuracy: {accuracy*100:.2f}%")
+```
+
+### 12. Plot Training vs Validation Accuracy:
+
+```python3
+plt.plot(history.history['accuracy'], label='Training Accuracy')
+plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+plt.title('Model Accuracy Over Epochs')
+plt.ylabel('Accuracy')
+plt.xlabel('Epoch')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+### 13. Plot Training vs Validation Loss:
+
+```python3
+plt.plot(history.history['loss'], label='Training Loss')
+plt.plot(history.history['val_loss'], label='Validation Loss')
+plt.title('Model Loss Over Epochs')
+plt.ylabel('Loss')
+plt.xlabel('Epoch')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+### 14. Confusion Matrix and Classification Report:
+
+```python3
+y_pred = (model.predict(X_test) > 0.5).astype(int)  # threshold 0.5: prob > 0.5 = positive
+
+cm = confusion_matrix(y_test, y_pred)
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=['Negative', 'Positive'],
+            yticklabels=['Negative', 'Positive'])
+plt.title('Confusion Matrix')
+plt.ylabel('Actual')
+plt.xlabel('Predicted')
+plt.show()
+
+print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=['Negative', 'Positive']))
+```
+
+---
+
+## Miscellaneous
+
+- [Dataset source](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)
+
+---
+