Chetanaya Aggarwal

— For Fabrication Client:

• Gathered, cleaned, and analyzed historical data using Python to build a budget estimation model that considered multiple variables.
• Employed machine learning techniques, including regression analysis and ensemble methods, for backend model development.
• Developed a user-friendly web application using Dash for frontend integration, making it accessible for non-technical users.
• Utilized visualization tools to showcase model performance metrics and budget estimation outputs, facilitating easier decision-making for the client.

— For Pharma Client:

• Utilized machine learning algorithms to develop propensity models aimed at predicting the likelihood of a patient purchasing prescribed medicine.
• Worked extensively with Python, leveraging libraries such as scikit-learn for building and tuning predictive models.
• Implemented data preprocessing techniques to handle imbalanced classes and missing data, improving model accuracy and robustness.
• Deployed the pipeline on Microsoft Azure.

— For Retail Client:

• Engineered demand response models using advanced mathematical techniques; honed these models through rigorous testing to ensure highly accurate predictions.
• Built a seamless end-to-end data pipeline on AWS, leveraging tools like EMR, Airflow, ML-flow, Glue, and S3, thereby automating the entire process from data gathering to delivering final insights.
• Crafted clear, impactful visualizations to present findings, making it easy for stakeholders to grasp the outcomes and make informed decisions.

— For FMCG Client:

• Delved into the specialized area of pet food within the FMCG sector, focusing on understanding the sensory preferences and buying behaviors related to pet nutrition.
• Designed multiple NLP algorithms to decipher comments collected during sensory tests, bringing out invaluable insights that otherwise would have been buried in unstructured text.
• Created a comprehensive Power BI dashboard to visualize these insights, making the data easily accessible and understandable for stakeholders.
• Achieved seamless backend deployment through Microsoft Azure, with the dashboard set up for scheduled refreshes, ensuring stakeholders always had access to the latest data.

• Researched and adopted new technologies to add value to existing offerings.
• Analyzed total share of client in Pet Food Industry leveraging Consumer Insights data collected from various third-party services and used this to forecast future growth Emerging Trends across multiple categories.
• Performed data pre-processing and validation for clients and built robust framework with exhaustive list of different time-series modeling techniques.
• Built and Managed end-to-end data solutions and provided substantial business value.

• Cleaned up and backed up data to maintain data integrity during extraction, manipulation and processing.
• Completed data cleaning and data validation of existing spreadsheets to promote robust data management platform, resulting in accurate data analysis and entry.
• Identified, analyzed and interpreted trends or patterns in complex data sets by finding correlations and visualizing with charts.