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flowchart TD;
    sp([<b>Sandip Palit</b>])

    b("{\n\n&nbsp;&nbsp;&nbsp;&nbspRole: <b>Data Engineer</b>,\n\n&nbsp;&nbsp;&nbsp;&nbspWork Experience: <b>2.5 years</b>,\n\n&nbsp;&nbsp;&nbsp;&nbspSkills: <b>SQL, Python, Azure Databricks, Microsoft Power BI</b>\n\n}")
    style b text-align:left

    subgraph Portfolio
    p1("<a href='https://pypi.org/project/relocate/'><b>Relocate</b></a> python package")
    p2("<a href='https://github.com/SandipPalit/Data-Engineering/tree/main/Temperature%20Data%20Scraping/'><b>Temperature Data Scraping</b></a> project")
    p3("<a href='https://github.com/SandipPalit/Data-Engineering/tree/main/Movie%20Data%20API%20Calls/'><b>Movie Data API Calls</b></a> project")
    p1 -.- p2 -.- p3
    end

    subgraph Achievements_and_Recognitions
    a1("Research article on <a href='https://www.igi-global.com/chapter/real-time-sentiment-analysis/308478/'><b>Real Time Sentiment Analysis</b></a>")
    a2("Speech on <a href='https://www.youtube.com/watch?v=UGeV37z7kjs&t=5589s'><b>Movie Recommender System using NLP</b></a>")
    a1 -.- a2
    end

    subgraph Currently_Contributing_to;
    c1("<a href='https://github.com/SandipPalit/NewsFeed'><b>NewsFeed</b></a> project")
    c2("<a href='https://linkfree.eddiehub.io/'><b>LinkFree</b></a> project")
    c1 -.- c2
    end

    sp --- b;
        b --> Portfolio;
        b --> Achievements_and_Recognitions;
        b --> Currently_Contributing_to;

My Career Journey

%%{init:{'logLevel': 'debug', 'theme': 'default', 'gitGraph': {'mainBranchName': 'My_Career'}} }%%
      gitGraph
        commit id:"2020"
        branch TCS
        commit id:"August, 2020" tag: "Joined TCS"
        commit id:"2021"
        commit id:"2022"
        commit id:"2023"
        checkout My_Career
        merge TCS
        branch PwC
        commit id:"May, 2023" tag: "TBA"
        commit id: "2023 "

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Sandip Palit's Projects

credit-card-fraud-detection icon credit-card-fraud-detection

Credit card fraud detection is the process of identifying purchase attempts that are fraudulent and rejecting them rather than processing the order. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In this notebook, we will use Unsupervised Machine Learning techniques like Isolation Forest and Local Outlier Factor to detect the outliers.

dsa-bootcamp-java icon dsa-bootcamp-java

This repository consists of the code samples, assignments, and the curriculum for the Community Classroom complete Data Structures & Algorithms Java bootcamp.

jaggu-mood-bot icon jaggu-mood-bot

Jaggu is a mood bot, developed by Sandip Palit and powered by RASA.

linkfree icon linkfree

Connect to your audience with a single link. Showcase the content you create and your projects in one place. Make it easier for people to find, follow and subscribe.

machine-learning icon machine-learning

I have impleted the popular Machine Learning algorithms in Python. Click on "Open in Colab" for better experience.

movie-recommender icon movie-recommender

I have developed this Recommendation engine on the TMDB dataset using Python programming language. After doing the pre-processing, I have used Cosine Similarity to find the movie recommendations. Finally, for the UI part, I have used Streamlit.

online-banking-system-with-flask icon online-banking-system-with-flask

This is a simplified version of an online banking system, entirely with python Flask for the driver code and Html and css for the webpage design.

resume-template icon resume-template

Crafting a stunning resume is now easier than ever with our Resume Template. Powered by the versatility of Python and the user-friendly Streamlit framework, this template empowers you to create an impressive online resume effortlessly.

simple-ml-for-sheets icon simple-ml-for-sheets

About This repository is for my session on "Simple ML for Sheets" at Google I/O Extended Kolkata 2023.

sp_distributions icon sp_distributions

This is a statistical Python library, to compute Mean and Standard deviation on of various Probability distributions.

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