Quora Question Pair Similarity

Quora Question Pair Similarity 

1. Introduction


Objective: Build a machine learning model to determine if two questions on Quora are duplicates.
Purpose: Help reduce duplicate content and improve user experience by identifying similar questions.

2. Project Workflow


1. Problem Definition:
   - Identify whether a given pair of questions are duplicates.
   - Key questions:
     - What features help in determining similarity between two questions?
     - Which ML models are effective for this task?
2. Data Collection:
   - Dataset: Quora Question Pair dataset (available on Kaggle).
   - Fields: Question1, Question2, IsDuplicate.
3. Data Preprocessing:
   - Text cleaning, tokenization, and vectorization.
4. Feature Engineering:
   - Extract semantic, syntactic, and statistical features.
5. Model Development:
   - Train classification models (Logistic Regression, Random Forest, or Neural Networks).
6. Evaluation:
   - Use metrics like accuracy, precision, recall, and F1-score.

3. Technical Requirements


- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Text Processing: NLTK, spaCy
  - Vectorization: TF-IDF, Word2Vec, Sentence Transformers
  - Machine Learning: Scikit-learn, XGBoost, TensorFlow/PyTorch
  - Visualization: Matplotlib, Seaborn

4. Implementation Steps

Step 1: Setup Environment


Install required libraries:
```
pip install pandas numpy nltk spacy scikit-learn xgboost matplotlib seaborn sentence-transformers
```
Download spaCy model:
```
python -m spacy download en_core_web_sm
```

Step 2: Load and Explore Dataset


Load the Quora Question Pair dataset:
```
import pandas as pd

data = pd.read_csv("quora_question_pairs.csv")
print(data.head())
```
Check for missing values:
```
print(data.isnull().sum())
```

Step 3: Preprocess Data


Clean and preprocess text:
- Remove punctuation, convert to lowercase, and tokenize:
```
import re
from nltk.tokenize import word_tokenize

def preprocess_text(text):
    text = re.sub(r'[^a-zA-Z0-9]', ' ', text.lower())
    tokens = word_tokenize(text)
    return ' '.join(tokens)

data['question1_clean'] = data['question1'].apply(preprocess_text)
data['question2_clean'] = data['question2'].apply(preprocess_text)
```

Step 4: Feature Engineering


Extract features:
1. TF-IDF Vectorization:
```
from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(max_features=5000)
tfidf_q1 = vectorizer.fit_transform(data['question1_clean'])
tfidf_q2 = vectorizer.transform(data['question2_clean'])
```
2. Cosine Similarity:
```
from sklearn.metrics.pairwise import cosine_similarity

cosine_sim = [cosine_similarity(q1, q2) for q1, q2 in zip(tfidf_q1, tfidf_q2)]
data['cosine_similarity'] = cosine_sim
```
3. Sentence Embeddings (Optional for advanced models):
```
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
embeddings_q1 = model.encode(data['question1_clean'].tolist())
embeddings_q2 = model.encode(data['question2_clean'].tolist())
```

Step 5: Train Classification Model


Split data into training and testing sets:
```
from sklearn.model_selection import train_test_split

X = data[['cosine_similarity']]
y = data['is_duplicate']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
Train a Logistic Regression model:
```
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)
```
Evaluate the model:
```
from sklearn.metrics import accuracy_score, classification_report

y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))
```

5. Expected Outcomes


1. A trained machine learning model capable of identifying duplicate questions.
2. Insights into the features that are most predictive of question similarity.
3. Performance evaluation using metrics like accuracy, precision, recall, and F1-score.

6. Additional Suggestions


- Explore Advanced Models:
  - Use deep learning architectures like Siamese Networks for improved accuracy.
- Hyperparameter Tuning:
  - Optimize model parameters using GridSearchCV or RandomizedSearchCV.
- Deployment:
  - Build a web interface to allow users to input question pairs and get similarity predictions in real-time.