Akshay Singh Thakur

Experienced Data Engineer with a Master's in Data Science, skilled in using AWS, Azure, and Databricks to enhance data workflows and analytics. Known for crafting effective solutions that boost data handling and decision-making across various industries.

• Rewarded as the "Best Employee of the Month" for three consecutive months in TCS.
• Secured a third position in the prestigious Tech Innovators Challenge 2022 by designing an advanced, AI-driven analytics tool on Python that significantly enhances data processing capabilities for small businesses.
• Nominated and voted for by colleagues for exceptional teamwork, collaboration, and contribution to projects.

UAV-Based Bridge Crack Detection Using Deep Learning, Deployed an advanced deep learning framework using UAV-captured imagery to identify structural cracks in bridges, applying the LeNet model for precise segmentation., Keras, ImageDataGenerator, Delivered a high model accuracy of 84% on both training and validation datasets, validating the robustness of the model and the effectiveness of the training approach.

Prediction of Downhole Equipment Using Sensor Data, Built a machine learning model using binary classification techniques to predict equipment failures, specifically focusing on oil pipeline leakages. This model analyzed data collected from targeted sensor readings, ensuring accurate failure prediction., ARIMA, Utilized feature ranking within the machine learning framework to pinpoint which sensors were most predictive of failures, directing maintenance efforts more efficiently and enhancing equipment reliability. 

Intrusion Detection Prediction Model, Produced machine learning pipeline to develop a predictive model capable of identifying intrusions in computer systems, significantly enhancing security measures., Pandas, Matplotlib, Sci-kit Learn, The model played a crucial role in bolstering system security by proactively detecting and addressing potential security breaches, thereby mitigating risks and safeguarding data integrity.