Rashveen Kaur

In dynamic and fast-paced environment, developed skills in data analysis, problem-solving, and strategic thinking. Excel in interpreting complex data sets and delivering actionable insights to support business objectives. Seeking to transition into new field where these transferrable skills can drive meaningful impact and innovation.

Project 1: Predicting Auto-Fraud Claims  

• Analyzed insurance data using Python, Pandas,  Machine Learning Algorithms and Matplotlib  to support fraud detection.  

• Built Machine Learning models to predict  fraud outcomes, enhancing fraud prevention.  

• Created data visualizations to identify and  communicate patterns in auto-fraud claims.  

Project 2: Telecom Churn Prediction  

• Predicted customer churn using Feature  Engineering and Principal Component  Analysis (PCA) for dimensionality reduction.  

• Applied Machine Learning algorithms and  Data visualization techniques to help  understand and address customer retention.  

Project 3: Identifying Entities in Healthcare Data  

• Implemented Natural Language Processing  (NLP) tasks, including POS tagging, stop word  removal, Bag-of-Words, and TFIDF  Vectorization.  • Used Conditional Random Fields (CRF) for  Named Entity Recognition (NER) to identify  entities like treatments and diseases within  healthcare data.  

Project 4: Automatic Ticket Classification  

• Developed a model to classify customer  complaints by product/service using NLP.  

• Applied techniques such as POS tagging,  lemmatization, TFIDF Vectorization, n-gram  analysis, and Topic Modeling for efficient text  classification.  

Project 5: Sentiment-Based Product  Recommendation System  

• Led the development of a Recommendation  System using Sentiment Analysis to suggest  products based on customer reviews.  

• Deployed the model with Flask and Heroku to  ensure accessibility and scalability.  

• Enhanced usability by creating a user-friendly  interface, making the system accessible to a  wider audience

• Date of Birth: 11/04/95
• Nationality: Indian
• Marital Status: Married