Python /Data Science
Resume:
Anand Raj Naidu
**************@*****.***
linkedin.com/in/anand-raj-naidu-pythonds
github.com/AnandSavvy
Professional Summary:
• I have 3+ years of Creative and detail-oriented Data Researcher/Analyst with practical experience and highly utilizing technology to complete process validation and business needs analysis.
• Exceptional interpersonal and analytical skills, with the ability to examine and understand unique client requirements and translate them into actionable project plans.
• Practical experience in Statistical Modelling, Data Cleaning, Data Exploration, and Data Visualization
• Proficient in Python to manipulate data for data loading and extraction and worked with python libraries like NumPy, and Pandas for data analysis
• Focus on the design and implementation with various Relational Database Management Systems like MySQL for various structured and unstructured data analysis.
• Well versed with Data Visualization using: Pandas, Matplotlib and other data visualization tools like Tableau for creating dashboards.
• Well explored GitHub working environment for version control system.
• Good in managing projects in fast-paced, deadline-driven environments while always delivering on time and exceeding expectations.
• Good industry knowledge, analytical & problem-solving skill and ability to work well in team as well as an individual.
• Academic qualifications include a bachelor’s in computer science engineering field from JNTU university, India.
• Academic qualifications include a master’s in information technology and p roj ect management f rom I ndiana Wesleyan University f lorence KY. Technical Skills:
Operating System : Windows, Linux
Database Management : MySQL
Programming Languages : Python, Data Science
Machine Learning Regression : Linear, Multi-linear, logistic regression Visualization Tableau, Python : Matplotlib, Seaborn and Pandas Environment : Jupyter, Spyder, Sublime and Pycharm Education:
Indiana Wesleyan University, Florence, KY.
Master’s in information technology and Project Management. 2023-2024 Kasireddy Narayanreddy College of Engineering and Research, JNTUH, India. Computer Science and Engineering - 70%
Project Experience:
Project-1 Description and Role: (Jan 22 to May 2023) NSL-KDD (for network-based intrusion detection systems (IDS)) is a dataset suggested to solve some of the inherent problems of the parent KDD'99 dataset. This IDS basically helps to determine security of systems and alarming when intrusion is noticed or detected. Choosing NSL-KDD provides insightful analysis using various machine learning algorithms for intrusion detection. Myself expecting to explore intuitive insights of intrusion detection and work on various machine learning algorithms that is reasonable to understand future instance of attacks and its types.
Project skills:
Well researched NSL-KDD website to perform data extraction to fit the analytical requirements.
• Worked on data cleaning and ensured data quality, consistency, integrity using Pandas, NumPy.
• Dealt with missing values by removing unnecessary features and replacing missing data with the statistical result. Converted unstructured records data to a structured dataset using feature engineering
• Explored and visualized the data to get descriptive statistics and inferential statistics for a better understanding of the dataset. Data analysis with help of graphs using Matplotlib and Seaborn library
• Participated in feature engineering such as feature generating, feature normalization, and label encoding with Scikit-learn preprocessing.
• Performed K-fold cross-validation for a train-test split before building machine learning models.
• Built predictive models including Logistic Regression, Random forest to predict the intrusion behavior by using python Scikit-learn.
• Implemented training process using cross-validation and evaluated the result based on different performance matrices.
• Enforced F-Score, ROC, Confusion Matrix, Precision, and Recall evaluating different models’ performance.
• Collected feedback and retrained the model to improve the performance.
• Created and maintained reports for the various data visualizations and results using Tableau and Matplotlib. Model : Logistic Regression, Decision Trees, Random Forest Evaluation Metrics : Confusion Matrix, Recall, Precision, ROC-AUC, Cross-Validation Technologies : Python, MySQL, Machine Learning Libraries – NumPy, Pandas, Scikit, Stats-mode.
Project-2 Description and Role: ( Apr 20 to Nov 21) As COVID-19 has spread around the world, people have become seriously familiar with the death tolls that their governments publish each day. Unfortunately, these tend to under-count the true number of fatalities that the disease has already caused. In many places, official daily figures exclude anybody who did not die in hospital or who did not test positive. Often the cause of death takes several days to establish and report, which creates a lag in the data. And even the most complete covid-19 records will not count people who were killed by conditions that might normally have been treated, had hospitals not been overwhelmed by a surge of patients needing intensive care.
A better way to measure the damage caused by such a medical crisis is to look at “excess mortality”: the gap between the total number of people who died from any cause, and the historical average for the same place and time of year.
Project Responsibilities:
• Communicated and coordinated with other enthusiasts to gather effective data for problem statements.
• Data preprocessing which includes checking missing values, unnecessary columns, and aggregating retail sales by date was carried out using NumPy and Pandas libraries.
• Participated in live cases track such as performing post and pre-covid data gathering and understanding the scenarios for normalization and label encoding with Scikit-learn preprocessing.
• EDA Explored on the different datasets using Matplotlib and Seaborn and analyzed whether death cases have trending or seasonality patterns.
• Experimented and built predictive models such as Linear Regression using Scikit-learn and Stats-model library.
• Conducted analysis and pattern on customers’ needs in a different location, different categories, and different months by using time series modeling techniques.
• Used RMSE/MSE to evaluate different model’s performance
• Designed rich data visualizations of outputs of different predictive models into human-readable form with Tableau and Matplotlib
Model : Linear Regression and Classification
Evaluation Metrics : RMSE/MSE
Technologies : Python, MySQL, Tableau, Machine Learning Libraries – NumPy, Pandas, Scikit, Stats-model
Declaration
I hereby declare that the above particulars of facts and information stated are correct to the best of my belief and knowledge.
Place: Redmond, Washington.
(Anand Raj Naidu)
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