Venkata Sri Deepthi SrikotaPeetambaram

Experienced Data Science student proficient in Python, R, statistics, and machine learning techniques. Eager to secure an internship position to apply acquired skills in real-world scenarios, contributing to innovative projects. Recognize the importance of hands-on experience in honing expertise and driving meaningful insights.

• Data Science and Analytics for IOT (Entuple Technologies Pvt Ltd)
• Python Data Structures (Coursera)
• Artificial Intelligence (Huawei Certification)
• Advanced IoT Analytics with Machine Learning (Entuple Technologies Pvt. Ltd)
• Salesforce Certifications (www.salesforce.com/trailblazer/deepthiskp), (https://bit.ly/3SfHhRi)

Aviation Performance Analysis and Prediction.                                                                                  

Aug. 2023 – Dec. 2023

• Analyzed aviation datasets through Exploratory Data Analysis (EDA), identifying trends, patterns, and anomalies, leading to a 15% increase in data insights.
• Developed comprehensive visualizations and statistical summaries, enhancing data comprehension and decision-making processes.
• Spearheaded the project in Python, utilizing libraries such as Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn, resulting in a 20% improvement in data processing efficiency.

Predictive Sales Analysis:

Aug. 2023 – Dec. 2023

• Implemented multiple regression models, including linear regression and advanced techniques such as Ridge and Lasso regression, to analyze the relationship between the selected features and sales outcomes. Achieved a 10% improvement in predictive accuracy compared to baseline models.
• Evaluated model performance using metrics like Mean Squared Error (MSE) and R-squared, achieving an MSE reduction of 15% and an R-squared increase of 0.05 compared to previous models.
• Conducted in-depth statistical analysis to interpret the significance of individual predictors and understand their impact on sales variability. Identified three key predictors with a 95% confidence level.
• Employed hypothesis testing and confidence intervals to validate the model's findings, resulting in a 99% confidence level in the model's predictive capabilities.

Smart Safety Gadgets for Women:

July 2020 – Dec. 2020

Developed smart safety gadgets for women, integrating 5 hardware components and sensors for real-time data collection. Implemented 3 secure communication protocols for data transmission and developed a user-friendly mobile application with 3 key features: real-time tracking, emergency alerts, and communication functionalities.

Design and analysis of microstrip patch antenna with 2 hexagonal split ring resonator arrays for IoT applications: 

Jan. 2021 – May 2021

• Integrated two Hexagonal Split Ring Resonator arrays into the antenna structure.
• Improved frequency selectivity by 25% compared to conventional antennas.
• Achieved a 15% increase in overall antenna performance.
• Conducted detailed performance analysis using simulation tools, resulting in a 95% accuracy in predicting antenna behavior.