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Arslan2003/README.md

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πŸ™‹β€β™‚οΈ About Me

First-Class Graduate Data Scientist

Welcome to my GitHub! Here, I explore how Machine Learning and AI can revolutionise the Finance sector by wrangling data and building models. When I am not tapping away at the keyboard, crushing bugs or crunching numbers, you can catch me on a tennis court, sipping a cup of coffee or geeking out over the latest developments in the Tech industry.


Kaggle       LinkedIn       Discord       Email


πŸ§‘β€πŸ’» How I Would Define Myself... Literally

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   β”‚_β”‚β”‚_β”‚    β•²_____β”‚_β”‚  β•²___β”‚β•²__,_β”‚β•²__β•²___β”‚    β”‚_β”‚β•²___β•± β•²__,_β”‚_β”‚    β”‚_____β•± β•²__,_β”‚β•²__β•²__,_β”‚ β”‚_____β•± β•²___β”‚_β”‚β•²___β”‚_β”‚ β”‚_β”‚β•²__β”‚_β”‚___β•±β•²__β”‚
                                                                                                                                    
                                                                                                                                    
                                                                                                                         

class Me:
    
    def __init__(self):
        # Basic info
        self.name = "Arslonbek Ishanov"
        self.role = "Data Scientist & AI/ML Enthusiast"
        self.skills = ["Statistics", "Programming Proficiency", "Data Wrangling", "Machine Learning", "Communication & Story Telling"]
        self.tools = ["Python", "SQL", "Jupyter Notebooks", "Version Control", "Tableau", "Power BI"]
        self.hobbies = ["Tennis", "Open Source Contribution", "Knowledge Sharing", "Experimenting with New Tools"]

    def introduce(self):
        print(f"Hi, I'm {self.name}.")
        print(f"My role: {self.role}.")
        print(f"My main skills: {', '.join(self.skills)}.")
        print(f"My favorite tools: {', '.join(self.tools)}.")
        print(f"My hobbies: {', '.join(self.hobbies)}.")

# Create an instance
me = Me()

# Introducing myself
me.introduce()



🧩 My Open-Source Contributions

Kaggle

Published a Dataset of LSE-domiciled Companies with Their Financial Metrics
on Kaggle


GitHub

Completed the Scikit-Learn Documentation
on GitHub


Wikipedia

Expanded the Wikipedia Article about Scikit-Learn
on Wikipedia


πŸ“Œ What I am up to

🌱 Currently Learning

IBM AI/ML Engineer

IBM AI/ML Engineer Professional Certificate
via Coursera


πŸ“œ Certifications

Google_Certificate


πŸ› οΈ Tools

Programming & Markup Languages

Python R SQL Markdown HTML5 CSS3 LaTeX

Frameworks & Libraries

NumPy Pandas SciPy Matplotlib Seaborn Plotly Scikit--Learn XGBoost LightGBM TensorFlow Keras PyTorch TabNet Optuna RAPIDS.ai yfinance Requests BeautifulSoup4

AI

ChatGPT Claude Gemini Copilot OpenAI API Hugging Face LangChain LlamaIndex

Databases & Cloud

MySQL PostgreSQL Oracle Azure Google Cloud BigQuery

Software

Tableau MS PowerPoint MS Teams Power BI PyCharm Google Colab Anaconda Jupyter Notebook VS Code Discord Kaggle Overleaf GitHub Desktop


🀝 Ask Me About

  • Python, Pandas, NumPy, SciPy
  • Machine Learning (Random Forest, XGBoost, TabNet)
  • Deep Learning & Neural Networks
  • Data Analysis & Visualisation
  • Stock Market Analysis & Financial Modelling
  • SQL, Databases, BigQuery
  • Data Scraping & APIs (yfinance, BeautifulSoup, Requests)
  • Collaborations
  • Tennis
  • Coffee
  • Anything else you think is interesting!

πŸ“Š Activity & Statistics

Total Contributions on GitHub

GitHub Snake Dark GitHub Snake Light

Statistics

Note: The languages displayed here are based on the code in my public repositories and do not fully reflect my overall experience or proficiency.


πŸ˜‚ Error 404: Boredom Not Found

Here are a couple of jokes to brighten your day! ;)

Joke 1: There are two types of people in the world:

  1. Those who can extrapolate from incomplete data.

Joke 2: There are 10 types of people: those who understand binary, and those who do not.




I am always happy to connect, collaborate, and discuss interesting ideas. Feel free to check out my projects below, reach out on LinkedIn, DM me on Discord or just send me an email!


Visit the References file to check out all the resources used to create this profile README file.

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  1. Final_Year_Project Final_Year_Project Public

    A machine learning pipeline that combines financial fundamentals and historical stock trends to deliver more informed stock recommendations for London-listed companies.

    Python 1

  2. Exploring_ML_Models_for_Regression_and_Classification Exploring_ML_Models_for_Regression_and_Classification Public

    A beginner-to-intermediate friendly project exploring machine learning models for regression and classification tasks. Includes a fully documented Jupyter Notebook, Python script, and step-by-step …

    Jupyter Notebook 1

  3. Uz_Inflation_TS Uz_Inflation_TS Public

    Time series analysis of Uzbekistan's monthly inflation (2010–2024) using R. Includes detrending, stationarity testing, ACF/PACF analysis, SARIMA modeling, and 1-step ahead forecasting. Dataset sour…

    R 1

  4. AML_with_FNN AML_with_FNN Public

    Python 1

  5. Statistical_Hypothesis_Testing_in_R Statistical_Hypothesis_Testing_in_R Public

    A coursework project for my Statistical Techniques with R module. The aim is to identify channleges in data analytics and select appropriate solutions, demonstrate an understanding of core mthods a…

    R 1