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.