Data Python

Akhila Bolisetty

adjmxt@r.postjobfree.com Ph:408-***-**** /in/Akhila-bolisetty

Objective:

To work in an organization with professional work driven environment, where I can apply and utilize my analytical and methodological skills and relevant expertise, which would enable me to grow while fulfilling the organizational goals.

Tools: SQL Server, Tableau, Advanced Excel, AWS, MySQL, Pig, Hive, Sqoop, NoSQL Programming: SQL, R Studio, Python, HTML, PHP

Concepts: Data Collection, Data Analysis, Data Processing, DBMS, Data Mining, Big Data Technology and Applications – HDFS/ MapReduce.

Work Experience:

Data Science Intern, Akshaya Info Pvt Ltd Feb 2017 – Mar 2018

• Performed data analysis by using Pandas and NumPy libraries in Python, provided strategic insights upon the existing data metrics.

• Collected, cleaned, transformed data as a process for arriving at optimized manufacturing conclusion using SQL & python.

• Analyzed SQL generated reports that included customer Master data, meta data.

• Created time series dashboard using Tableau to generate insight on the product services.

• Optimized the forecasted data creating advanced regression models using R, MS Excel and Tableau.

• Analyzed, forecasted, reported and validated data using Oracle, SQL and Tableau.

• Worked with users and stakeholders to gather requirements, analyze them and subsequently use design tools to model the requirements.

Academic Projects:

House Price Prediction using Python CSU East Bay

• Collected information of all the listed houses in Zillow by web scraping using Beautiful Soup. Preprocessed, visualized the data and built models using Pandas & Mat plot libraries.

• Analyzed data/charts to interpret the characteristics of house, types of parameters and correlation between them. Implemented Linear Regression to predict the house price. Predicting the survival of Titanic passengers using Python CSU East Bay

• Explored the data by checking out missing values and identified the important features. Visualized the data using Seaborn and Matplotlib. Preprocessed the data by computing missing values.

• Trained the Titanic data set by implementing Logistic Regression model on it. Implemented confusion matrix and computed the model’s precision, recall and F- score. Predicting weekly COVID-19 cases in the month of December using R CSU East Bay

• Identified country with highest number of cases & deaths based on visualization. Experimented time series techniques namely Naïve, Regression with Linear Trend, Holts Exponential Smoothing, ARIMA and Auto-ARIMA forecast models based on time series components. Selected the best forecast model using MAE & RMSE errors. Predicted weekly COVID-19 cases and deaths in the month of December, 2020. Education:

M.S. Business Analytics (California State University, East Bay), GPA: 3.71 May 2021

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