Focus : Data Analysis, Prompt Engineering
Specifications : Data Science
Skills
Problem Solving, Python, Django, Java, MySQL, Machine Learning, R programming, EDA, Excel, Power BI, Mathematics, Statistics,Languages
English, Malayalam, Hindi,Hobbies
Prompt Engineering
Summary
Motivated MSc Data Science student with a strong foundation in mathematics and a growing proficiency in programming languages, seeking opportunities to apply and expand upon data science skills in a professional environment.
Education History
Internships
During the internship at Cognifyz Technologies, extensive experience was gained in Data Science, focusing on advanced data analysis techniques and machine learning methodologies. The role involved hands-on tasks in data cleaning and visualization, applying effective problem-solving strategies to real-world challenges. This opportunity allowed for collaboration on diverse projects, enhancing skills and deepening knowledge in the field. The supportive mentorship received throughout the internship played a crucial role in professional growth and understanding industry practices, making this experience a valuable step in the journey within Data Science.
Projects
Django project is based on a travel company, highlighting the services and destinations, building credibility through reviews, and drive users to contact or browse trips to book. Provides users services like booking travel for their willing destination, and there is a blog page allowing users to view as well as add their own blog. User can share their experience through the feedback form. Every user can register to the site and can use any services according to their preferences.
In EDA project, a dataset was thoroughly analyzed to uncover underlying patterns and insights. The process began with data cleaning, which involved handling missing values and removing outliers to ensure data integrity. Descriptive statistics were calculated to summarize key features, while various visualizations, such as histograms, scatter plots, and correlation heatmaps, were created to explore relationships among variables. The analysis revealed significant trends and correlations, providing valuable insights that could inform future research or decision-making. The project was conducted using Python, utilizing libraries such as Pandas, Matplotlib, and Seaborn.
In Java-based Machine Learning project, the contributions focused on data cleaning and visualization. The process began with preprocessing the dataset by handling missing values, removing outliers, and normalizing features to ensure data quality. After cleaning the data, various visualizations were created to explore the dataset, utilizing charts and graphs to illustrate key patterns and relationships among variables. These visualizations aided in understanding the data distribution and informed the subsequent modeling process, making this work essential for the project's overall success.