Tolbaev Muntasir's Projects
A basic implementation of a blockchain in Python using the SHA-256 hashing algorithm. This project provides a simplified example of how a blockchain works, with a focus on the core concepts of block creation, hashing, and linking blocks together.
One of the main challenges of Data Science and more specifically in Machine Learning is the performance measure. How to measure performance efficiently so that our model predictions meet the business objectives?
Using python libraries requests and BeautifulSoup4, return a CSV of the TOP 25 trending repositories from Github.
Translate from one format to another. Work with two popular formats: SQL and CSV.
Creating a garbage collector in the C programming language
Sparring with a Python Language. 100 Exercises
Find tendencies and to generalize.A linear model makes predictions by computing a weighted sum of the features (plus a constant term called the bias term)
Here we harness the power of Big Data to unlock insights, drive decision-making, and transform data into value. Our project is dedicated to tackling the complexities of large-scale data processing and analysis, providing tools and resources to make data-driven solutions accessible to all.
Memory Allocation
This project involves analyzing the "My MobApp Studio" dataset to uncover valuable insights into the mobile app landscape. The challenge lies in navigating the dataset, cleaning and preprocessing the data, visualizing trends, and interpreting the correlations and relationships between attributes.
Data cleaning
MasterMind game for pleasure
Data and tools for analyzing the games of the National Basketball Association (NBA). It contains information about various game statistics such as points, shots, assists, baskets, etc.
another project of the Qwasar platform where we use ScikitLearn technology
wassup world ?
About Using python libraries requests and BeautifulSoup4, return a CSV of the TOP 25 trending repositories from Github.
Educational Platform
Select sorting/ Bubble sorting
Upgrading wine recomendation system. We have collected and cleaned wine-related data, explored its patterns and correlations, and developed a machine learning model to provide wine recommendations based on user preferences.