Hello there, fellow DEVELOPER'S

I'm a passionate Software Developer from INDIA.

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🙋‍♂️ About Me

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A Joke for you!!

Here are some of the technologies and tools I work with:

• Programming Languages: Python, HTML5, Tailwind CSS, JavaScript, C++, C, R, SCALA

• Frameworks: Django, Flask, ReactJS, NodeJS, ExpressJS, ReactNative

• Developer Tools: GoogleColab Notebook, VSCode, R-Studio, Jupyter Notebook, Anaconda, Git, Razorpay, Cloudinary, Expo

• Tech Expertise: OOPS, API, Payment Gateway System, Image/Video Upload System, Secure Authentication (jsonwebtoken)

• Databases:

  • MongoDB (Hybrid/Cloud - MERN Projects, ML projects with Databases)
  • Relational Database - MySQL (Queries, Joins, Trigger, Functions, Stored Procedures)
  • DynamoDB
  • SQLite (Connecting Python with Databases)
  • IBM DB2

• Hosting Platforms: AWS (EC2, ECR, S3, Sagemaker), Render, Vercel, Heroku, PythonAnywhere, 000webhostapp, GoDaddy, Shinnyapp, GitHub Pages

• IBM Tools: IBM Watson, IBM SPSS Modeler, IBM Cloud, IBM Cognos BI, IBM InfoSphere BigInsights, IBM Chatbot, IBM DB2

• Data Science Expertise:

  • Predictive Modeling, Statistical Analysis, MLOps, Deep Learning, Data Cleaning, Data Visualization (PowerBI, Tableau)
  • Data Mining and Warehousing (Data Cube, OLAP, Multidimensional Data Models)
  • Data Transformation, Standardization, Normalization, Handling Missing Data
  • Data Lake, JSON Data Format, ETL, Data Pipeline, Data Wrangling, Data Summarization
  • Correlation Analysis, Grouping and Aggregating Data, Web Scraping (BeautifulSoup, Scrapy)

• Machine Learning / AI:

  • Supervised and Unsupervised Learning, Dimensionality Reduction, Neural Networks (ANN, RNN, CNN), NLP (Natural Language Processing)
  • Inferential Statistics, Association Rules, Classification and Prediction (K-means, Decision Tree, Bayesian Classification)
  • Clustering (KNN), Exploratory Data Analysis (EDA), ARIMA Models, Model Evaluation/Validation, Hyperparameter Tuning

• Big Data:

  • PySpark, Scala, Hive, HBase, Hadoop Ecosystem (HDFS, MapReduce, Yarn, PIG, HIVE, HBASE, Zookeeper)
  • Apache Spark, Statistical Analysis (Hypothesis Tests - Z-test, T-test, Chi-square Test)
  • Probability and Statistics (Measures of Central Tendency and Dispersion, Random Variables), Linear Algebra, Calculus

• NLP Techniques: Bag of Words (BoW), Word2Vec, Transformers, BERT, Sentiment Analysis

• Libraries / Algorithms:

  • A* Algorithm, Scikit-learn (Regression and Classification), Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
  • TensorFlow, Keras (Deep Learning), LSTM (Time Series Prediction, NLP, Speech Recognition)
  • NLTK (Text Processing - Classification, Tokenization, Stemming, Tagging, Parsing, Semantic Reasoning)
  • Plotly, Matplotlib (Interactive Visualizations - Line Plots, Scatter Plots, Bar Charts, Histograms)
  • Seaborn (High-level Interface for Statistical Graphics, Visualization of Univariate/Bivariate Data)
  • SciPy (Scientific Computing), Pandas (Data Analysis/Manipulation), NumPy (Numerical Computing)