I transform complex data into actionable insights through machine learning and statistical analysis. Passionate about building intelligent systems and solving real-world problems with data-driven solutions.
Built an NLP classifier using TF-IDF and Logistic Regression to detect fake news with over 95% accuracy.
Used Isolation Forest and XGBoost to classify fraud cases in an imbalanced dataset (ROC-AUC > 97%).
Performed EDA and visualization to explore Netflix content trends by country, genre, and year.
Developed a logistic regression model on UCI dataset to predict heart disease risks with feature importance analysis.
Implemented LSTM-based RNN to predict stock closing prices using historical time-series data.
Built a content-based recommendation system using cosine similarity on movie metadata for personalized suggestions.
Developed an interactive Streamlit dashboard to visualize real-time COVID-19 trends and global statistics with maps and plots.
Trained a Convolutional Neural Network to classify CIFAR-10 images achieving over 85% accuracy using Keras and TensorFlow.
I'm always open to discussing new opportunities, collaborations, or just chatting about data science!