Aagam

ABOUT ME

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.

Machine Learning:
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)

RDBMS: MySQL

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)

SKILLS