Done additional data preparation for modelling the input for promotional analytics of CPG. Analyzing the forecast series and bring out meaningful insights about how other variables are impacting sales and find out volume lift and return on investment.

Performed data querying and analytics on large data sets and provided value-added insights and recommendations using data with a rule-based model for stock trading using different criteria and conduct critical research on changing buying, selling criteria, and built a clear understanding of how stock changes behavior in trading.

Worked on cutting edge machine learning and deep learning to build algorithms to solve client needs and provide them powerful solutions:
• Developed Machine learning models for predicting customer lifetime value using RFM score and worked on customer segmentation using machine learning algorithms
• Performed anomaly/outlier detection and fraud detection using clustering approaches
• Performed forecasting using Machine Learning Models and Time Series Model for sales in stores
• Built a simple recommender system using movie rating data and using a number of users who rated a particular movie
• Worked on Human resource data and predict the attrition rate

I focus on system perspective to assess the health of controllers i.e. how well the control loop is performing. We at SENAI group develop the sophisticated algorithm. Moreover, I am also interesed in

• Data wrangling and exploratory data analysis to have better insights into data and for better decision making

• Building Machine learning models and predictive analysis

• Customer segmentation and lifetime value prediction

• Detection of process variability due to increased variance and oscillations in process variables

• The root cause identification in interactive control loops and diagnose the cause for such variation

• Build approaches that can easily identify problems in process industries

• Improve the performance of the process by troubleshooting the cause of the problem

• Preventive maintenance to avoid the cost of downtime due to malfunction in manufacturing industries