Projects Developed: Campaign Analytics, Credit UnderWriting, Cab Driver Insurance Prediction, Sentiment Analysis, Spam Filtering, Movie Recommendation, Stock Prediction
Areas: NLP, Recommendation, TimeSeries, DeepLearning
Statistical Learning Expertise:
Descriptive, Inferential and Prescriptive Statistics and Exploratory Data Analysis.
Regression Models - Simple, Multiple and Logistic Regression
Classification Problems using KNN, Decision Trees - Bagging(Random Forest) and Boosting(GBM and Xgboost), Naive Bayes, SVM and Neural Networks.
Unsupervised Learning: PCA, K-means & Hierarchical Clustering.
Model Building: Python (Numpy, Pandas, Scikit-Learn, Theano, Keras, Tensorflow and BeautifulSoup)
Visualization: Python (Matplotlib, Plotly), Kibana (Elastic Search)
Cloud Computing: AWS, GCP
Version Control: GIT
Achievements in data science competitions:
- Top 2 % finish (Placed 4/ 380) ( Vortex Machine Learning Challenge, organized by NIT Trichy on HackerEarth)
- Top 5 % finish (Placed 21 /504) (Recommendation Hackathon, organized by IIT(BHU) on Analytics Vidhya)