Create animated charts easily! A ipyvizzu wrapper for intuitive usage of ipyvizzu functions and Streamlit embed support.

Space launch statistics, charts & infographics made in Python with the Launch Library 2 API.

My Exploratory Data Analysis (EDA), Product Analysis and Statistical projects are stored here

This project performs **exploratory data analysis (EDA)** and **multidimensional visualizations** on the UCI Wine Quality dataset (Red & White wines).

Generates 230+ figurate number sequences across multiple dimensions in Python. Based on Figurate Numbers (2012) by Elena Deza and Michel Deza.

Explore scalable Plotly Dashboards with dynamic capabilities. Organized structure for multiple dashboards, each with Plotly graphs and algorithms. Share common components and utilities for efficient development.

Code that follows tasks from a worksheet to study tidal spin-up in WR stars. It includes functions for several physical quantities, as well as the mean and median of a continuous function, an ODE solver using solve_ivp, and matplotlib plotting made into more comprehensive functions.

collection of examples / demos for python 'datadraw' package, organized as a flask app

LASSO and Boosting for Regression on Communities and Crime data

Python's Matplotlib Library for Data Analysis, Machine Learning, Data Science and many more...

Matplotlib is a powerful plotting library in Python that allows you to create static, animated, and interactive visualizations. It’s widely used for representing data graphically, making it easier to analyze and understand.

This project utilizes various metrics to analyze a graph network based on data of ENZYMES_g295

Unified Oscillatory Theory novel aproach- COM (Collatz Octave Model) plots

Everlytics Internship Test in Python

Python's Seaborn Library for Data Analysis, Machine Learning, Data Science and many more...

EDA performed on timeseries data and different plots to get insights from data visualizations

Stocks Visualization of top tech companies (Apple, Google, IBM, Tesla, Amazon, sp500) from years 2012-2020.

A machine learning project to predict bike rental demand using historical data. This project uses an end-to-end workflow including data preprocessing, feature engineering, and a tuned XGBoost Regressor model to achieve high predictive accuracy.

Variety of codes developed while training python during graduation period.