Extremely motivated towards converting business requirements into technical solutions. Strong in design and integration with intuitive problem-solving skills. Experienced in the complete product development lifecycle of software applications. A fast learner focusing on end-to-end implementation of ideas rather than learnings only. Looking for an opportunity to work in a challenging environment to prove my skills and utilize my knowledge & intelligence in the growth of the organization.
-
I am BE Computer Science Engineering Graduate passed out in 2020. I'm having an extensive experience in working with Technologies like Data Science, Machine Learning and additionally I have web developement skills.
-
Data Science practitioner with the hands-on Experience in Python, Flask, Streamlit, SQL
-
Experienced in developing Data Science web applications with Machine Learning using Python. Machine Learning Techniques includes Linear Regression, Logistic Regression, Random Forest and some advanced Machine Learning Algorithms.
-
Skilled in libraries such as Sklearn, Scipy, Numpy, Pandas, Matplotlib, Plotly, Seaborn, Imblearn, Tableau for Data Visualization.
-
Additional skills are HTML & CSS, Bootstrap and basic Data Structures and algorithms.
-
Possessing an ability to be a good data storyteller.
-
Team Player with good communication skills and self-motivated attitude.
-
Contributor to the Data Science Community.
Currently, I am looking for a full-time role in Data Science, Machine Learning or related field.
-
Technology : Data Analytics, Machine Learning, Data Visualization, NLP
-
Programming : Python, C, C++, Data Structures and Algorithms, OOPs
-
Packages/Frameworks : Numpy, Pandas, Scikit-learn, Statsmodels, Scipy, NLTK, Tensorflow
-
Visualization Libraries : Matplotlib, Seaborn, Plotly
-
Software / IDE : Jupyter Notebook, Spyder IDE, Sublime Text
-
Deployment Frameworks : Flask, Streamlit, Dash
-
Cloud Platforms : Heroku Cloud Platform, AWS
-
Exposure : R, RStudio, Ouantum Computing
- Multi-class Classification — One-vs-All & One-vs-One
- How to find the optimal value of K in KNN?
- Data Science Model Building Life Cycle
- Text2emotion: Python package to detect emotions from textual data
- Introduction to CNN & Image Classification Using CNN in PyTorch
- Textfeatures: Library for extracting basic features from text data
- Twitter Sentiment Analysis using Vader & Tweepy
- Beginner’s guide to build Recommendation Engine in Python
- Exploratory Data Analysis in Python
- The default of Credit Card Clients Dataset: Classification & Evaluation
