Results-driven Business Analytics graduate with expertise in data analysis, statistical modeling, and data visualization. Proficient in Python, SQL, Excel, and Power BI for extracting actionable insights. Experienced in academic projects focused on forecasting, clustering, and decision-making models. Committed to leveraging data to enhance business strategies and performance outcomes.
This project aimed to forecast Microsoft stock prices from 2010 to 2023, using time series and machine learning models. The analysis helped identify patterns in historical stock behavior and evaluate predictive performance across multiple techniques.
Technologies: Python (yfinance, statsmodels, Keras), Jupyter Notebook.
Role/Responsibilities:
• Collected and preprocessed historical stock data using yfinance and handled missing values.
• Built and evaluated ARIMA, Linear Regression, and Stacked LSTM models for forecasting.
• Identified overfitting in LSTM models and optimized hyperparameters.
• Achieved the best forecasting accuracy using ARIMA with an RMSE of 3.33.
• Visualized stock trends and model predictions using Matplotlib and Seaborn.
This project focused on identifying wage inequality patterns and their impact on employee job satisfaction and progression, using statistical and data analysis tools.
Technologies: R, Python, SPSS, SQL, Tableau.
Role/Responsibilities:
• Queried and prepared employee survey and wage datasets using SQL (CTEs, subqueries).
• Conducted regression analysis and ANOVA to explore compensation differences across demographics. • Used SPSS and R for statistical testing and validation of findings.
• Visualized key insights through Tableau dashboards to communicate patterns clearly.
• Highlighted significant disparities in pay and career outcomes linked to age, gender, and job role.