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Click here or scroll down to respond to this candidateCandidate's Name
EMAIL AVAILABLE PHONE NUMBER AVAILABLEhttps://LINKEDIN LINK AVAILABLE EDUCATIONMASTER OF SCIENCE IN COMPUTER SCIENCE April Street Address
Wright State University, Dayton, Ohio GPA: 3.7/4.0 Courses included: Advance Database Management system, Algorithm and Design Analysis, Machine learning. BACHELOR OF TECHNOLOGY IN ELECTRONICS AND COMMUNICATION ENGINEERING May 2022 Sreenidhi institute of science and technology, Hyderabad, India GPA: 3.3/4.0 Courses included: RDBMS, Data structures and Algorithms, Java, Computer Networks and C programming. PROFESSIONAL SUMMARYI'm a data enthusiast with professional experience in data analytics and machine learning demonstrated through impactful internships and diverse projects. Experienced in analyzing large datasets, with efficient ETL processes and developing dashboards and reports during an internship at Taylor & Francis. As a Business Analyst Intern at Sparks Foundation, I utilized Python, SQL, and popular machine learning libraries to develop predictive models, and dashboards and worked on natural language processing techniques. My work expertise extends to database management, data analysis, data visualization, and machine learning, with proficiency in programming languages like Python, Java, and SQL. Skilled in building interactive dashboards with Power BI, and Tableau and implementing Machine learning algorithms using TensorFlow and scikit-learn. Eager to leverage my skills and knowledge to drive data-driven insights and solutions as a Data Analyst. WORK EXPERIENCEDATA ANALYST Nov 2021-Jun 2022Taylor & Francis Group Collaborated with cross-functional teams to understand business requirements and deliver data-driven solutions. Utilized ETL tools such as Talend and Pentaho Data Integration to efficiently extract, transform, and load data into MySQL databases, ensuring high data quality and integrity across large datasets. Supported ETL processes using AWS Glue, transforming raw data into structured formats for further analysis, reducing data processing time by 20%. Applied SQL language with MySQL framework to perform efficient querying against large business databases. Developed and maintained dashboards and reports with the extracted data using visualization tools like Power BI and tableau. BUSINESS ANALYST May-Oct 2021Sparks Foundation Utilized SQL to extract, manipulate, and analyze large datasets from relational databases, providing key insights to support business decisions. Developed automated data pipelines using Python scripts, streamlining data processing and ensuring data accuracy and consistency, reducing manual effort by 25%. Created interactive dashboards and reports in Power BI to visualize data trends and key performance indicators (KPIs) for stakeholders. Performed data cleaning and transformation using Python libraries such as Pandas and NumPy, improving data quality for analysis. Implemented machine learning models using scikit-learn to predict customer behavior and improve marketing strategies. SKILLSTechnical skills - Database Management, Data Analytics, Data structures and Algorithms, Data Visualization, ETL, Machine learning, and Deep learning.Databases - MySQL, PostgreSQL, and Microsoft SQL server. Data Visualization - Power BI, Tableau, Microsoft Excel, Matplotlib, ggplot and Seaborn Big Data technologies: Hadoop, Hive, Apache spark. Cloud platform: AWS.Programming languages - Python, SQL, C Programming, C++, Java, HTML, CSS, and JavaScript. Libraries - Sckitlearn, Tensorflow, Pandas, Keras and Numpy. Other skills - Statistics, Predictive modeling.PROJECTSSales Analysis Dashboard using PowerBI January 2024 Designed a user interactive Sales Dashboard consisting of slicers, filters, and drill-through capabilities to enhance user experience and data exploration using Power BI tool. Extracted data from multiple sources including Excel, SQL database and integrated them into one database. Transformed the loaded dataset in Power query editor as per requirements and established relationships between multiple tables. Added extra measures and columns using DAX formulae, then created appropriate charts, graphs and plots on the Dashboard which provide deep insights about the sales performance and trends. Credit Card fraud detection using Random Forest and parallelizing it with DASK October 2023 Created a machine learning model for detecting credit card fraud transactions. Selected a big data set and performed data cleaning and manipulation using various techniques to make the data readily available for further use. Applied Random Forest algorithm to train the model and increased its performance by successfully parallelizing the algorithm using DASK and Joblib. Reduced the time taken to train and test the model by 4 times after parallelization of random forest. Fantasy Cricket application using Python and MySQL April 2023 Programmed a GUI fantasy application where users can track the points scored by their team considering the stats of a particular match. Utilized MySQL for robust data storage and retrieval, managing player information, team data, match data, and player statistics. Implemented efficient SQL queries to handle large datasets, ensuring quick and efficient access to player and match information. Developed user friendly GUI using Qt designer, then finally integrated front-end design and back-end database using python script file.Customer Segmentation Analysis using Tableau May 2022 Utilized Tableau to visualize and analyze customer data from transactional databases and CRM systems, identifying key variables such as purchase frequency, average order value, and customer demographics. Employed clustering algorithms and advanced analytics techniques within Tableau to segment customers into homogenous groups based on their buying patterns and preferences. Created interactive dashboards and visualizations to present the segmentation results, allowing stakeholders to explore and understand the characteristics of each customer segment and tailor marketing campaigns accordingly. Emotion Analysis detection using LSTM and NLP December 2021 Built an LSTM 3-layer sequential model to predict emotion in a sentence. Preprocessed the data using NLP and statistical analytic techniques and developed predictive model using LSTM algorithm. Achieved an accuracy score of 93% by effectively utilizing parameters in LSTM sequential model. CERTIFICATIONS Programming with Python, Internshala. Problem-solving with 5 stars, HackerRank. Machine learning algorithms, Simplilearn. PowerBI, Programmatic works. SQL, HackerRank Data Analytics and Visualization Virtual experience in Accenture, Forage. PAPER PUBLICATIONS Candidate's Name (2022), Image Caption Generator using CNN and LSTM, IRJMETS Publishers. Candidate's Name (2022), Hand Digit Recognition in GUI, IRJMETS Publishers- |
