Naga Sripalasetty

• Six Sigma Green Belt
• Microsoft PL-300 (Power BI Data Analyst)
• Microsoft Azure Data Fundamentals
• Advanced Excel
• Azure DevOps & CI/CD

Machine Learning Model for Stock Price Forecasting

• Developed predictive machine learning models using Python (Scikit-learn, Pandas, NumPy) to forecast company stock prices with 85%+ accuracy
• Implemented time series analysis techniques including ARIMA, LSTM neural networks, and regression models to predict market trends
• Performed comprehensive exploratory data analysis (EDA) on historical financial data to identify patterns and key performance indicators
• Created interactive dashboards using Power BI to visualize stock price predictions and model performance metrics

Machine Learning & Bio-Inspired Optimization

• Applied advanced optimization algorithms including genetic algorithms and particle swarm optimization to solve complex business problems
• Implemented hybrid machine learning approaches combining traditional ML techniques with bio-inspired methods to improve model efficiency
• Analyzed algorithm performance across multiple datasets, achieving 20% improvement in processing time whilst maintaining accuracy
• Documented methodology and findings in comprehensive technical reports for academic and business stakeholders

Classification Problem Solving Using Supervised Learning

• Designed and implemented multiple supervised learning models including Random Forest, SVM, and Neural Networks for multi-class classification
• Conducted feature engineering and selection to optimize model performance, achieving 92% classification accuracy
• Performed cross-validation and hyper parameter tuning to ensure robust model generalization across different datasets
• Compared model performance metrics (precision, recall, F1-score) to select optimal solution for business requirements

Time Series Analysis for Financial Forecasting

• Developed comprehensive time series forecasting models using ARIMA, seasonal decomposition, and advanced statistical methods
• Analyzed quarterly and annual financial trends to predict future company performance with statistical confidence intervals
• Implemented automated data preprocessing pipelines to handle missing data, outliers, and seasonal adjustments
• Created executive-level reports translating complex statistical findings into actionable business insights and recommendations

• Best Employee Award (2019, 2020, 2021).
• Reduced reporting preparation time by 30% through automation.
• Improved governance compliance and reporting accuracy across departments.