I am P Sumanth Reddy, a recent B.Tech graduate with practical experience in data analysis through projects like "Pizza Sales Analysis" and "Employee Attendance Analysis" during my training with Learnbay. I'm eager to apply my skills and knowledge in a data science or data analysis role, ready to contribute and grow in the field.
CodSoft's Fraud Detection project developed a precise machine learning model, addressing imbalances in transaction data to detect financial fraud with high accuracy. Overcoming challenges of imbalanced datasets, the project strategically sampled and continuously monitored, providing a reliable solution for businesses.
Utilized historical sales data for exploratory analysis, addressing missing values and applying time-specific preprocessing techniques. Employed feature engineering to enhance predictive models, selecting ARIMA, SARIMA, Random Forest, and Gradient Boosting based on performance metrics. Achieved accurate sales predictions through rigorous model training, hyperparameter tuning, and addressing challenges like seasonality, concluding a successful project with appreciation for guidance from CodSoft.
Engaged in comprehensive data handling by collecting and exploring the Titanic dataset, followed by preprocessing involving missing value handling and feature engineering. Explored diverse modeling algorithms, selecting based on cross-validation performance, and further trained and evaluated the model on a dataset subset. Successfully addressed challenges like imbalanced data and missing values, achieving high accuracy. The model's results were visually interpreted through feature importance analysis, marking the completion of a successful project with gratitude to CodSoft for guidance and support.
This project analyzes pizza sales data to derive insights into total revenue, top-selling pizzas, and sales trends by size, month, day, and hour. Utilizing transaction records, pizza menu details, and a calendar, it aims to optimize pricing, sizes, and promotional strategies. Employing tools like Python, a database system, and visualization tools, it delivers data cleaning, analysis, and interactive dashboards. The outcome includes recommendations to enhance resource allocation during peak periods, ultimately boosting profitability and customer satisfaction. Overall, the project enhances decision-making for the pizza business.
This Employee Attendance Analysis project leverages Power BI to provide comprehensive insights into attendance metrics, key ratios, and trends. By collecting and integrating data from diverse sources, it aims to enhance decision-making through interactive dashboards and reports. The project calculates key ratios, offers day-wise attendance analysis, implements filtering for customized exploration, and visualizes historical trends. With a focus on data-driven decision-making, efficiency improvement, resource allocation, and compliance monitoring, the project empowers management and HR for optimized attendance management and improved organizational performance.