Intro

My journey in data science began with a rigorous academic foundation at the University of Essex and has been enriched by substantial professional experiences that have honed my skills in analytics and automation. As I seek to further my career, I am particularly interested in positions as a Junior Data Analyst or Junior Data Scientist. In these roles, I aim to leverage my expertise in Python, R, SQL, and machine learning to address and solve complex business challenges. My goal is to contribute to innovative projects where data-driven strategies enhance operational efficiency and decision-making. I am eager to bring my background in advanced analytics to a forward-thinking organization, supporting strategic goals while staying at the forefront of technological advancements in data science.

Work

rSutra Analytics and Consulting Pvt. Ltd.

Mumbai, India Sep 2019 - Sep 2022

• Leveraged my strong background in business intelligence, statistics, and mathematics to develop a sophisticated bank reconciliation system using Python and SQL, reducing transaction processing time by efficiently handling 250,000 daily transactions across four accounts, and employed Power BI for dynamic report generation, enhancing financial decision-making.

• Engineered an advanced inventory management solution for a pharmaceutical company, streamlining operations by automating SAP data analysis and reporting with SQL and Excel VBA, which improved inventory tracking and operational efficiency.

• Created an innovative NLP tool to analyse customer feedback from retail websites, using Python for data scraping and sentiment analysis, leading to a 1.5-star average increase in product ratings by enabling proactive customer support interventions.

• Revolutionised order processing for a logistics firm and several manufacturing clients by automating invoice data extraction using Python and SQL and developed a Power BI-based reporting system for weekly operational insights, significantly enhancing efficiency and strategic decision-making.

• Developed Proof of Concept (POC) for 7 projects, converting 5 into full-scale projects, demonstrating exceptional innovation and project initiation capabilities.

Projects

Movie Recommender System

This project aims to create a personalized movie recommendation model that suggests films based on an individual's previous watchlist, ratings, and movie genres. The approach incorporates various methodologies including Item-Based and User-Based Collaborative Filtering, Hybrid Collaborative Filtering, and Artificial Neural Networks, all implemented using Python. The data for this project comes from the MovieLens dataset, allowing for robust model training and testing. This recommender system is designed to enhance the viewing experience by tailoring suggestions to user preferences.

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HR Analytics: Attrition Rate Prediction

This project focuses on predicting employee attrition using logistic regression and visualizing key performance indicators with Power BI. Utilizing Python for data processing and model building, and Power BI for dashboard creation, the project taps into open-source data to develop insights. The outcome is an interactive dashboard that highlights attrition rates and other relevant KPIs.

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Stress Analysis

This project aims to predict a person's stress levels using data collected from a wearable watch, applying advanced machine learning techniques including an Artificial Neural Network and Random Forest Classifier. Utilizing Python for data manipulation and model implementation, the initiative leverages open-source datasets to train and test the models effectively. The goal is to develop a predictive tool that can offer insights into stress patterns, facilitating better stress management.

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Credit Risk Analysis

This project is focused on developing a predictive model to assess the probability of loan default using a combination of Random Forest Classifier and Logistic Regression. Utilizing Python, the models classify individuals based on their likelihood to default, leveraging datasets from Kaggle to train and validate the effectiveness of the predictions. This initiative aids in enhancing financial decision-making processes by providing a clearer understanding of credit risk.

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Contact

• LinkedIn
• Email
• GitHub
• Phone

Elements

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