Predict anomalous vendor transactions in procurement work-flow, and hence strengthen client audit team.

• Challenges: Several possible cross-functional reasons for anomalous transactions. Possible reasons can come from accountings, country-wise regulations, BU practices etc. We need to reveal all those possibilities from volume of data.
• EDA, Feature Engineering & Data Transformation
• POC: Research on suitable algorithms (Isolation Forest, LOF, Elliptical Envelope)
• Educating client on ML models
• Results: Customer Penetration
  • Received wide acceptance from senior management from client
  • Won project extension
  • Cross selling / Up selling: Won another project from the client

Predict demand fulfillment likelihood & prioritize the same, and help the client to increase business efficiency.

• Challenge: Imbalanced dataset (extremely low conversion rate), unclean & unstructured textual data
• EDA, Data cleaning using NLP & Data Transformation, Oversampling (SMOTE)
• POC: Research on algorithms (Supervised: Tree-based, SVM, Naïve-Bayes etc)
• Customer Communication & Education: Abandon accuracy measure and use more appropriate Precision, Recall & ROC curve to measure model performance
• Result: Recruitment work-flow enhancement: Customer is able to make informed decision

• Building model to prepare dataset for Triplet search algorithm
• Built python generator to feed image samples to Neural Network for training on limited memory environment
• Used Google’s Inception model & transfer learning method

• Built LSTM on characters (for language modeling) &
• Seq-2-Seq LSTM model for Language translation

Performed EDA to uncover various patterns in dataset and detect outliers Feature engineering to extract hidden features Grid search to discover suitable machine learning algorithm and its parameters

Perform EDA on NYC Flight data Explored various business questions related to Airport operations (capacity utilization, flight delay pattern throughout a day) and patterns for favored destinations Uncovered a pattern which emphasizes on process improvement to streamline efficiency.

Built CNN model and achieved approx. 99% accuracy.

Built LSTM on characters (for language modeling) & Seq-2-Seq LSTM model for Language translation