*Designed and implemented scalable ETL processes, reducing data processing time by 30% and developed Python scripts to automate manual testing processes, significantly enhancing efficiency and reducing manual efforts, thereby streamlining workflows and ensuring a more accurate and timely testing environment.*Conducted a comprehensive comparative analysis between traditional machine learning models and Long Short-Term Memory (LSTM) deep learning model using a Human Activity Recognition dataset. The study involved evaluating the performance metrics, including accuracy, precision, recall, and F1-score, to assess the effectiveness of each approach. The results highlighted the superior predictive capabilities of the LSTM model, showcasing its ability to capture temporal dependencies and outperform classical models in accurately recognizing and classifying human activities.𝐓𝐨𝐨𝐥𝐬 & 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 :-1. Python (Pandas, Numpy, Sklearn, Matplotlib, Seaborn)2. Machine Learning (Regression, Classification, NLP)3. Databases (MYSQL, SQL Server)4. Analytics Tools (Power BI, Microsoft Excel, Jupyter Notebook, Visual Studio)