A competent SAP Technical Consultant with 3 years of experience in seamlessly integrating SAP solutions into business practices. As a budding data science enthusiast, I'm eager to explore the depths of data analytics. Delving into Python, Excel, and MySQL, I'm learning the ropes of data cleaning and visualization, eager to transform raw data into actionable insights. I'm excited to embark on this journey of learning and discovery in the field of data analytics.
0 + Projects completed
Managed multiple deliverables concurrently and facilated use of SAP for enterprise resource planning.Worked primarily as an ABAP(Advanced Business Application Programming) developer involving mostly RICEF objects and supported in various enhancements and monitoring activities.
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society.
Grade: 8.99 CGPA
Grade: 89.2%
Below are the sample Projects on Data Analysis, KPI Dashboard using Excel, SQL, Tableau and PowerBI
This dashboard designed to provide insights into various aspects of employee attrition and demographic distribution within an organization. It also offers a visual representation of key HR metrics through interactive charts, graphs and tables.
Dive into the world of sales analytics with our PowerBI Sales Dashboard.Explore dynamic visualizations and insightful analytics tailored for sales data, empowering businesses to make informed decisions and drive growth.
Embark on a journey through hotel bookings and cancellations with this data analytics project. Using Excel's pivot tables and charts, we unravel trends, booking patterns,providing actionable insights for optimizing hotel operations.
Delve into the world of music retail analytics through MySQL, exploring diverse metrics such as genre, artist, track and preferences. Uncover trends, patterns to enhance marketing strategies.
Here we explored and analyze weather patterns using Python Pandas. This project is designed to provide a hands-on experience in working with real-world weather datasets with data manipulation and analysis.
These projects showcase the application of machine learning techniques, including supervised learning (predictive analytics), unsupervised learning (clustering, association learning), and deep learning, to solve real-world business problems across various industries such as manufacturing, pharmaceuticals, and finance. The projects utilize classification and regression algorithms such as Logistic Regression, Random Forest, Support Vector Machine, K-Means Clustering, and Neural Networks.
Utilizing ML techniques, the project builds a predictive model for housing prices, leveraging diverse features like location, size, amenities, and neighborhood details. Using a rich dataset, it aims to deliver a precise and insightful tool for real estate professionals.
Using ColumnTransformer, pipelines, standardization, and encoding, we preprocess data and apply models like Logistic Regression, Decision Trees, Random Forest, and XGBoost to predict employee churn based on factors such as job satisfaction, promotion, and salary. This enables companies to reduce turnover and improve organizational stability.
About Created a customer segmentation project using K-Means clustering to categorize customers into distinct groups based on purchasing behavior and demographics. Implemented data preprocessing, clustering, and visualization to uncover patterns and tailor marketing strategies for targeted customer engagement.
This project uses machine learning algorithms (Random Forest Classifier and Decision Tree) to predict student placement likelihood based on age, gender, CGPA, internships, and backlogs. It provides actionable employability insights, aiding career planning. A user-friendly Flask web app will be deployed on Render for broad accessibility.
Utilizing MobileNet transfer learning in TensorFlow, this project classifies cat and dog images. With Kaggle data, it fine-tunes pre-existing weights, integrates OpenCV for preprocessing, and PIL for augmentation. The aim is to optimize model performance in distinguishing between the two, advancing computer vision for pet classification
Below are the details to reach out to me!
Kolkata, India