Anila Reddy Musku

Data science graduate with a Master's from CU Boulder. Skilled in statistical analysis, machine learning, and cloud technologies. Proven experience in diverse projects, including solar flare identification, travel optimization, and innovative neural network applications. Eager to contribute in an entry-level or internship role in data science.

LinkedIn: https://www.linkedin.com/in/anilareddymusku/

• Solar NanoFlares- LASP( Laboratory for Atmospheric Space and Physics) at CU | Current

Currently working on this project- utilizing EXIS instrument data(GOES-18 Satellite) to identify and extract solar flares from observations. To Estimate the energy flux of the identified flares and employed statistical methods to compute the power-law slope for the flare energy frequency distribution and compute α.

• Optifly | December 2023

Leveraged data center-scale computing technologies to optimize travel decisions for users, considering variables such as delays, departure/arrival locations, and travel months. Designed and implemented an end-to-end pipeline using aviation data from the aviationstack API. Integrated Google Cloud Storage, Spark, BigQuery, and visualization tools like Tableau and Looker to efficiently analyze and present insights on flight patterns, delays, and airline performance. Demonstrated expertise in handling large-scale data processing and visualization in a cloud environment. 

• LipReadify | November 2023

Developed a project integrating 3D Convolutional Neural Networks (3DCNN) and Bidirectional LSTM to analyze lip movements in video inputs. Implemented the Connectionist Temporal Classification (CTC) loss for accurate prediction of corresponding audio content. Modeled after the LipNet paper, the system excels in translating lip movements into accurate audio predictions. 

Website link - https://anilareddy058.wixsite.com/lipreadify

• Analyzing the short-term rental market: An Advanced Statistical Analysis | May 2023

Conducted comprehensive analysis of Portland Airbnb listings data using statistical techniques, including ANOVA,, Causality, and Linear Regression. Explored factors influencing listing distribution, pricing trends, availability patterns, and forecasted property popularity, contributing to data-driven insights for Airbnb hosts and stakeholders.

• Airbnb listings Analysis | Machine learning | March 2023

 Analyzed Airbnb data to understand pricing determinants and other influential factors. Developed supervised machine learning models for predictions and implemented a range of supervised and unsupervised algorithms. Evaluated models using metrics like RMSE and accuracy. Created interactive visualizations to effectively communicate insights and findings.

Website link - https://anmu4597.wixsite.com/airbnb