Entry-Level Software / IT Professional Resume

Hyderabad, TG, India

******

Contact no -

+917*********

Email- *************@*****.***

Linkedin- nagaraju

Professional summary

Entry-level software/IT professional with strong fundamentals in programming, data structures and databases, and experience building small projects using Python libraries such as Numpy, pandas and visualization libraries scuh as Matplotlib and seaborn, Power Bi, My Sql, and Machine Learning. Focused on dragging more effective insights from the data.

Education

Bachelor of Science MPCs July2018

Satavahana University, Karimnagar.

• I had completed the graduation with the subjects as Mathematics, Physics and Computer science. Work history

E-commerce Analyst

TELEPERFORMANCE, Hyderabad July 2022 - November 2022

• It is an e-commerce operations job, even though I have worked less time, but it gave me an immense experience to habituated to work with the coordination with the team. Skills

• Jupiter Notebook

• Python programing

• Numpy

• Pandas

• Matplotlib.

• Seaborn.

• Power Bi.

• Web scraping.

• Beautiful Soup.

• Machine learning.

• My sql.

Projects

1) Project on the Exploratory Data Analysis on Travel Website’s Indian Tour Packages.

• Collected data through web scrapping, with using BeautifulSoup package from the the Jupyter notebook.

• Created dataset with 500 rows and 8 columns and converted the data set into comma separated value(CSV) file

• With the data I have performed the univariate and bivariate analysis.

• Visualizations of the data with various plots such as violin plot, barplot and scatter plots, with keen observed and dragged the insights from the data.

• Collected data through web scrapping, with using BeautifulSoup package from the the Jupyter notebook.

• Created dataset with 500 rows and 8 columns and converted the data set into comma separated value (CSV) file.

• With the data I have performed the univariate and bivariate analysis.

• Visualizations of the data with various plots such as violin plot, barplot and scatter plots, with keen observed and dragged the insights from the data

NAGARAJU NUNAGONDA

2) Machine learning project on the MT cars dataset.

• Dataset: Cylinders, hp, weight, disp MPG (10.4-33.9) of the cars.

• Preprocessing: No nulls/duplicates, StandardScaler, 78/22 split.

• EDA: Weight (-0.87), hp (-0.78), disp (-0.80) strongest negative MPG correlations such as heat map histogram, and other plots were dragged the several outputs.

• Models: KNN R =0.72 observed as best in terms of R, Random Forest’s R =0.60 which is second in line of the prediction, Mean Standard Error=23.05

3) Machine learning project on the Diamond Price Prediction (53,940 rows)

• Dataset: Carat, cut, color, clarity Price ($326-$18,823) of the diamonds.

• Preprocessing: Removed 146 duplicates, LabelEncoder on categoricals, 80/20 split.

• EDA: Carat strongest price driver, Premium/Ideal cuts highest value.

• Models: Random Forest regressor is the best in terms of R value 0.982 and RMSE=517 (best), Decision Tree 0.968, KNN 0.947, Linear 0.887.

4) Power BI BlinkIT Grocery Analytics Project

• Designed and implemented a star schema data model connecting fact tables with dimension tables for efficient reporting.

• Performed data cleaning and transformation in Power Query: handled missing values, standardized categories, removed duplicates, and ensured correct data types.

• Built interactive dashboards to visualize key KPIs such as sales trends, top-selling items, customer ratings, and category growth.

• Applied advanced DAX measures to calculate Top N products, average ratings, and correlation metrics for actionable insights.

• Delivered a polished, business-ready report that enabled clear decision-making and demonstrated expertise in data modeling, visualization, and storytelling with Power BI.

Certifications

• Secured Certification on Exploratory Data Analysis from Innomatics Research Labs.

• Secured Certification on Power BI from Innomatics Research Labs. Activities and Interests

Reading novels, listening music, physical excericise and frequently watch movies.

Contact this candidate