👋 Hi, I’m a Data Scientist with over 2 years of experience in the life insurance and BFSI sectors. My expertise lies in building predictive models, machine learning, data analytics, and cloud technologies like Google Cloud Platform (GCP). I’m passionate about solving complex business problems with data-driven insights and constantly learning new tools and technologies.

• 📊 Data Science & Analytics: I work on extracting meaningful insights from structured and unstructured data, creating predictive models for business use cases such as customer behavior prediction, churn analysis, and agent activation.
• 🧠 Machine Learning: I build and deploy models using libraries like Scikit-learn, CatBoost, and TensorFlow for predictive analytics and automation in the insurance industry.
• ☁️ Cloud Computing (GCP): I design scalable, cloud-native architectures for deploying data pipelines, machine learning models, and data analytics workflows.
• 🛠 Tech Stack:
  • Programming: Python, SQL, Bash
  • ML Libraries: Scikit-learn, CatBoost, TensorFlow
  • Cloud: GCP (BigQuery, Compute Engine, GKE, Cloud Storage)
  • Tools: Jupyter, Docker, Git, Kubernetes

Project Purpose: Developed a predictive model to forecast agent activity for a life insurance company, helping the business plan resource allocation and improve sales strategies.

• Key Features:
  • Predicts whether an agent will be active in selling policies in the next month.
  • Tackles seasonality with time-series features and rolling averages.
• Tech Stack: Python, CatBoost, GCP (BigQuery, Compute Engine)
• View Repository

Project Purpose: Created a machine learning model to predict customer churn, allowing the business to take proactive retention measures.

• Key Features:
  • Trained on historical customer behavior data.
  • Includes customer segmentation and risk profiling.
• Tech Stack: Python, Scikit-learn, Pandas
• View Repository

Project Purpose: I write blog posts about practical data science, machine learning concepts, and how they are applied in the BFSI sector.

• Topics: Feature engineering, dealing with imbalanced data, model evaluation.
• View Blog

1. Clone the Repository:

   git clone https://github.com/username/repository-name.git