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Results-oriented data professional with expertise in data analytics, machine learning, and predictive modeling, complemented by a strong foundation in insurance-specific tools and cloud platforms. Skilled in programming with Python, R, SQL, and SAS, as well as leveraging visualization tools like Tableau and Power BI for actionable insights. Proficient in managing complex databases using MySQL, PostgreSQL, and MongoDB, and experienced in deploying machine learning algorithms with Scikit-learn, Tidyverse, and H2O AutoML. Demonstrates strong problem-solving and analytical abilities, with a proven track record of collaboration, effective communication, and delivering data-driven solutions to optimize business processes.

• Programming & Tools: Python, R, SQL, SAS (insurance-specific), Pandas, NumPy, Tableau, Microsoft Excel (Advanced), Power BI, Git • Database Management: SQL, MySQL, PostgreSQL, MongoDB • Machine Learning & Statistics: Scikit-learn, Tidyverse, H2O AutoML, Regression Analysis, Clustering • Insurance-Specific Tools: Guidewire, Actuarial Models, Risk Analysis Software • Cloud Platforms: AWS, Azure, Google Cloud

• Data Science Techniques: Data Visualization, Predictive Modeling, Web Development, Automation • Soft Skills: Analytical Thinking, Problem-Solving, Effective Communication, Time Management, Collaboration

Data Import: readr & odbc Data Cleaning & Wrangling: dplyr & tidyr Time Series, Text, & Categorical Data: lubridate, stringr, & forcats Visualization: ggplot2 Functions & Iteration: purrr Modeling & Machine Learning: parnsip (xgboost, glmnet, kernlab, broom, & more) Business Reporting: rmarkdown Data Import: readr & odbc Data Cleaning & Wrangling: dplyr & tidyr Time Series, Text, & Categorical Data: lubridate, stringr, & forcats Visualization: ggplot2 Functions & Iteration: purrr Modeling & Machine Learning: parnsip (xgboost, glmnet, kernlab, broom, & more) Business Reporting: rmarkdown Skills: Business Analytics · Web Applications · Statistical Data Analysis · Interpreting Data · Deep Learning · Presentation Preparation · Data Management · Data Science · R (Programming Language) · Machine Learning

Courses: Python Programming, Statistics, Data Analysis & Visualization, Machine Learning, SQL & Analytics Highlights: 600+ hours of coursework, 14 coding assignments, 3 projects (Web scraping, EDA, ML)

R Programming, Tidyverse, H2O AutoML, High Performance Time Series Forecasting, Shiny Predictive Sales DashBoard, Feature Engineering, Amazon AWS EC2

Courses: Data Import: readr & odbc Data Cleaning & Wrangling: dplyr & tidyr Time Series, Text, & Categorical Data: lubridate, stringr, & forcats Visualization: ggplot2 Functions & Iteration: purrr Modeling & Machine Learning: parnsip (xgboost, glmnet, kernlab, broom, & more) Business Reporting: rmarkdown Data Import: readr & odbc Data Cleaning & Wrangling: dplyr & tidyr Time Series, Text, & Categorical Data: lubridate, stringr, & forcats Visualization: ggplot2 Functions & Iteration: purrr Modeling & Machine Learning: parnsip (xgboost, glmnet, kernlab, broom, & more) Business Reporting: rmarkdown Skills: Business Analytics · Web Applications · Statistical Data Analysis · Interpreting Data · Deep Learning · Presentation Preparation · Data Management · Data Science · R (Programming Language) · Machine Learning

Courses: Python Programming, Statistics, Data Analysis & Visualization, Machine Learning, SQL & Analytics Highlights: 600+ hours of coursework, 14 coding assignments, 3 projects (Web scraping, EDA, ML)

Master of Business Administration (MBA) Concentration: Project Management

Bachelor in Computer Sciences B.CSc

• The sales dashboard leverages Flexdashboard and Shiny to create a dynamic and responsive user experience. • Users can input various parameters, such as time periods, product categories, or geographical regions, to customize their analysis and view sales data from different perspectives.

Solution Summary The data Science team has identified 4 customer segments.The 4 customer segments were given descriptions based on the customer’s top product purchases.

Segment 1 Preferences: Road Bikes, Below $3200 (Economical Models)

Segment 2 Preferences: Mountain Bikes, Above $3200 (Premium Models)

Segment 3 Preferences: Road Bikes, Above $3200 (Premium Models)

Segment 4 Preferences: Both Road and Mountain Bikes, Below $3200 (Economical Models)

• Cleaned & analyzed retail store dataset with 58,000+ rows & 44 columns to predict the policyholder files a claim in the next 6 months or not.

• Trained logistic models, random forests, and gradient boosted trees using Scikit-Learn & XGBoost

• Achieved Test accuracy of 0.93 after hyperparameter tuning

• Used Pandas DataFrame to load 1.2 millions+ rows and 23 columns to check and explore

• Created visualizations (bar plots, scatter plots, pairplot, treemaps, geo heatmaps, wordcloud etc.) using Seaborn &Matplotlib & Plotly& Folium & WordCloud

• Discovered how unauthorized and illegitimate activities that take place over the internet

• Scraped software company listings from Goodfirms.co using Requests & BeautifulSoup4

• Builts functions to scrape company name, company info, focus, stars(reviews), company Urls

• Put all the data using Pandas DataFrame consisting 126 rows x 6 columns into 4software_company.csv

• Created average rating per Monthly checkout treemap that will show all the details about the sales etc.

• Counted and calculated the details of the bookstore's total sales and check out for every month

• Used bar plot to show how the sales is going on based on its monthly store's ratings

• Created Barchart for the amount of Loan that required by the Bureau based on the Total Income

• Showing the comparison for the Organization type and the amount required for the Credit

• Created chart based on the Home type that people usually picked based on their Occupation and Income Level

• Showing the Happiness Scores based on their Health, Economy, Generosity, Family

• Showing the Happiness Scores across the World's Map

• 🔭 I recently done working on Machine Learning Car Insurance Claims Prediction project!

• All my projects are available on Jovian Profile

• 🌱 I’m currently learning everything about Data Science and Machine Learning!

• 📫 How to reach me: contact email

• Proficient in the following skills: SQL, Python, R, scikit-learn, Numpy, Pandas, Excel, Power BI, Git, Web Development, Presentation

• 😄 Future Goals: I want to learn everything about Data Science and Machine Learning to become a data scientist for myself in the future.

• ⚡ Fun fact: I love to connect people through Github and LinkedIn to learn more about their professional experience and their projects.