Rishitha Mallepally

Talented Junior Software Engineer adept at working with customers, team members and project leaders to accomplish software engineering and updating tasks according to timelines. Solid understanding of full development lifecycle. Skilled working with wide range of front- and back-end technologies to produce and evaluate code. Proficiency in performing statistical analysis, causal inference, and statistical modeling in R and Python.Working knowledge of working with various Python IDE such as PyCharm and Visual Studio Code.Good experience in Python and R packages such as NumPy, Pandas, Matplotlib, SciPy, and ggplot2. Knowledge of using visualization tools such as Tableau and Power BI to create insightful dashboards, resulting in a 30% improvement in reporting efficiency and a 40% increase in data-driven decision-making.

• Credit Card Fraud Detection:
  Skills: Predictive Analytics, Data Modelling and Analytical Skills
  The project aims to detect fraud in credit card transactions using machine learning techniques. The dataset includes past transaction information and labels indicating whether each transaction was fraudulent. The goal of this project is to detect 100% of the fraudulent transactions while minimizing false positives. Possible algorithms include logistic regression, decision trees, random forests, support vector machines, and neural networks. Model performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. The final model can be deployed in a production environment for real-time fraud detection.

• Convenience Store Dashboard:
  Skills: data analysis, database design, Mongo database, Python programming, dashboard development, and effective communication with stakeholders.
  Collected and analyzed data from the store, including working shifts and inventory data. Designed a no SQL database using Mongo database to process all the unstructured data. Integrated the dashboard with Python for data analysis, providing real-time insight into sales and inventory performance and enabling store managers to make informed decisions.