Here are some of the Tools and Languages that I use:

I have completed Masters in AI & ML and focusing Data Science with expertising in data analysis and reporting, query optimization, data modelling and data visualizations.

• Started my career with tech like Unix, Linux and now moving forward with latest tech - Microsoft Azure, Azure DevOps, Shell Scripting, Python, NumPy, Pandas, Seaborn, Matplotlib, SciPy, scikit-learn, Tableau, Excel, Visualization, Probability and Statictics, Math Fundamentals and Git, .... [ May 2019 - Present ]
• Generate reports and perform data analysis by querying data using SQL.
• Developing dashboards on Tableau and Excel.
• Data analysis and reporting using Google Sheets and Microsoft Excel.
• Utlizing Python and its libraries for data manipulation and wrangling.
• A brief EDA using Hypothesis Testing and Feature Engg using Scipy for Statistical Analysis.
• Currently Learning the Machine learning algorithms(Supervised and Unsupervised).
• Experienced and Azure Certified DevOps engineer with a experience in build, release pipeline and passion for developing innovative programs.
• Innovative creator to develop automation for monitoring and cloud.
• Working on configuring and maintaining Azure CI/CD Pipelines for existing and adding new features.
• Hands-on experience in Shell and Batch scripting to create automations for monitoring and alerting.
• Hands-on experience in Ansible for creating and modifying configuration for our applications.
• Hands-on experience in Python and Flask. Created several automations and dashboards in python and won several SRE awards for the same in different quarters.
• Created Ticket Auto Resolution Tool using Python and got featured in Amdocs Magazine in June 2021. Got featured in one of director level Town Hall for the same.
• Basic understanding and hands-on experience on Docker platform.

Worked on below Cloud Platform :

Microsoft Azure Services -

• Virtual Machine, Virtual Machine Scale Set, Load Balancer, Azure Key Vault, Storage Account, App Service, Azure Function, Log Analytics Workspace and more.

Project Link	Tools Used	Area of Analysis	Project Description
(Click Icon to View)	Numpy, Pandas, Seaborn, scipy, sklearn, Normalization, LogisticRegression, Model optimization	EDA, Feature Engineering, Feature selection, MachineLearning, Predictive Modelling, Hyperparameter Tuning(λ), Cross_validation	This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explored model optimization techniques to effectively mitigate default risks.
(Click Icon to View)	Numpy, Pandas, Matplotlib, Seaborn, scipy, statsmodels, Standarization, Linear,Ridge,Lasso,ElasticNet Regression.	Exploratory Data Analysis , Data Visualization, probability & Stats, Hypothesis Testing, FeatureEngineering, MachineLearning, Predictive Modelling.	This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

Project Link	Tools Used	Area of Analysis	Project Description
(Click Icon to View)	Numpy, Pandas, Seaborn, scipy , boxcox , Ttest , Chisquare , ANOVA , Kruskal-walis , Shapiro-wilk , Mannwhitney_u.	Data analysis, Data Exploration , Data Visualization, Statistics & probability,Hypothesis Testing	This repository provides a detailed exploration of Yulu Bike rentals data using Hypothesis testing, employing statistical techniques, we delve into the nuances of customer behavior, E-bikes rental patterns offering insights & key metrics to enhance understanding and inform strategic decisions.
(Click Icon to View)	Numpy, Pandas, Matplotlib.plotpy, Seaborn, Central Limit Theorem, scipy.stats, boxcox	Data analysis, Data Exploration , Data Visualization, Statistics & probability, confidence interval	This repository provides a detailed exploration of WALMART's BlackFridaySales data using the Central Limit Theorem (CLT) coupled with Confidence Interval Analysis. Leveraging statistical techniques, we delve into the nuances of customer behavior, purchase patterns.
(Click Icon to View)	Numpy, Pandas, Matplotlib.plotpy, Seaborn, statistics , probability	Data analysis, Data Exploration , Data Visualization, Descriptive Statistics & probability	This repository serves as a comprehensive solution for AEROFIT Company's Exploratory Data Analysis (EDA), employing descriptive statistics and probability to gain insights and inform strategic decisions.
(Click Icon to View)	Numpy, Pandas, Matplotlib.plotpy, Seaborn	Data cleaning , Data analysis, Data Exploration , Data Visualization	This repository conducts an Exploratory Data Analysis (EDA) on the OTT streaming platform NETFLIX, offering insights into user behavior, content trends, and key metrics to enhance understanding and inform strategic decisions.

Project Link	Tools Used	Area of Analysis	Project Description
(Click Icon to View)	Google BigQuery	Data analysis, Data Exploration	This repo serves as the solution for the Business Case Study for the giant retail store Target in Brazil. It showcases my ability to tackle SQL challenges and demonstrates my proficiency in SQL query writing and problem-solving skills and conferring Insights and Recommendations.

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Here are some of the platforms where I Practice :

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