Santosh Verma

Aspiring Data Scientist with a strong aptitude for learning and applying new technologies. Seeking an opportunity to contribute to the world of Machine Learning and data-driven decision-making.

• Google Cloud Data Analytics
• IBM Professional Data Science Certification from Coursera.
• Natural Language Processing Certification from NPTEL.
• Intellectual Property Certification from NPTEL.
• Get Started with Tableau
• Microsoft AI Skills Challenge.
• Cricket Analytics using Python from Mad About Sports in association with Rajasthan Royals.
• Data Science Bootcamp from AI Planet
• Feature Engineering and Data Cleaning from Kaggle.

1. Generative AI Integrated Programming Learning Portal  

• Implemented a learning portal with Gen-AI Integrated capabilities for learners that empowers them to take control of their education and achieve their full potential.
• The portal contains learning videos, assignments, resources, practice and graded assignments, and programming quizzes.
• The learning environment provides opportunities for self-learning and enabling learners to learn at their own pace and schedule.

2. Classification on SpaceX Data for Falcon-9 Successful landings -

• In this capstone project of SpaceX, we predicted if the Falcon 9 first stage will land successfully.
• This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.
• From our data analysis and application of machine learning models we observed that Classifier models such as Logistic regression, SVM and KNN gave score of 83% accuracy which is a good score to build up the model further.
• Link - https://github.com/santo-mantras/IBM_CapstoneProject.git

3.  Linear Regression Predicting Supermarket Sales Project -

• Built a predictive model and predict the sales of each product at a particular outlet on Bigmart Data
• Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales.
• Link - https://github.com/santo-mantras/Machine_Learning/tree/master_mlp/LinearReg

4. Classification Case Predicting Student Enrollment -  

• Cleaned & enhanced Kaggle Student enrollment dataset by implementing data visualization techniques & EDA, increasing accuracy of predicted values by 13%. Built & trained Classification regression model using pandas, NumPy and Decision tree classifier library in Python to classify student enrollment.
• Generated detailed classification reports with improved metrics such as 95% precision and recall rates for proper rating of algorithm performance.
• Link - https://github.com/santo-mantras/Machine_Learning/tree/master_mlp/Classification

5. Business Analysis of a Furniture Warehouse -

• Data from a furniture retail shop was analyzed using Microsoft Excel and Tableau.
• The analysis focused on both monthly trends and overall shop performance.
• Pareto charts, bar graphs, and area-specific pivot tables were created to assess monthly business performance.
• Link - https://docs.google.com/presentation/d/19CtBrRD1Z64b1_zinMXKHrw30FoBQ2Vl/edit?usp=sharing&ouid=101185160452057200390&rtpof=true&sd=true