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Lokesh Reddy

@lokeshreddy

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Motivated Computer Science graduate passionate about software development.

India

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What I'm looking for

I am seeking an entry-level position that fosters collaboration and innovation.

I am a motivated Computer Science graduate with a strong passion for software development and problem-solving. My academic journey has equipped me with a solid foundation in various programming languages and technologies, enabling me to tackle complex challenges effectively. I am eager to contribute to projects that involve writing clean, efficient, and maintainable code while adhering to best practices and coding standards.

Throughout my education, I have engaged in several projects that showcase my technical skills and creativity. For instance, I designed and implemented a recipe blog web application using Node.js, Express, and MongoDB, creating a dynamic platform for users to explore and share recipes. Additionally, I conducted exploratory data analysis on sales data, utilizing tools like Pandas, NumPy, and PowerBI to derive actionable insights. My project on detecting IoT botnet attacks highlights my ability to apply machine learning techniques to enhance cybersecurity measures.

Experience

Work history, roles, and key accomplishments

Software Developer Intern

N/A

Developed a recipe blog web application using Node.js, Express, and MongoDB, incorporating features like session management and file uploads. Focused on creating a dynamic platform for users to explore and share recipes.

Node Express MongoDB HTML CSS JavaScript Session Management Web Development Software Design

Data Analyst Intern

N/A

Conducted exploratory data analysis on sales data using Pandas and NumPy. Created visualizations with Matplotlib and Seaborn, and developed interactive dashboards in PowerBI to present insights to stakeholders.

Data Analysis Pandas NumPy Matplotlib SeaBorn Power BI Data Visualization Data Wrangling

Machine Learning Intern

N/A

Developed a machine learning-based detection system for IoT botnet attacks using the Random Forest Classifier. Evaluated model performance with accuracy, precision, and F1-score metrics.