Big data analysis with pyspark on google-colab

Job Interview Project

Обучение нейросети для распознавания выражения лица

PySpark analysis of AWS Reviews Dataset for wireless products

PyTorch version of https://github.com/GilesStrong/LIP_DSS_Keras_Tutorial_2019

Data analysis project using PySpark to perform the ETL process to extract data, transform it, connect it to AWS RDS, and load it into pgAdmin. Then performed an analysis via PySpark, Pandas, & SQL to determine biases on reviews.

The project uses python in google colab . It uses logistic regression algorithm

Introduction Course to Deep Learning using Google Colab.

Кваліфікаціна робота магістра. Тема: "Формування дивiдендної полiтики корпорацiї (на прикладi компанiй, що входять до iндексу Standard & Poor’s 100)"

A CNN architecture used to predict handwritten Hindi Digits.

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

A machine learning project using logistic regression to predict diabetes based on health indicators. Includes EDA, preprocessing, model tuning, and evaluation. Built under the guidance of Priya Bhatia Ma'am during the GeeksforGeeks ML & DS Program.

Analysis of COVID-19 infection rates in various countries, correlating them with factors such as GDP per capita and social support.

Stores the basic Deep Learning models that I've worked on and learned from using Kaggle, Google Colab and Jupyter Notebook

The primary goal of this project is to improve driver situational awareness at a low cost by providing real-time alerts about nearby traffic signs, crosswalks, and speed limits using dash-cam footage and machine learning to reduce motor vehicular accidents.

Projects with the implementations of various types of ML systems

a version of stable diffusion, which hides all types of image generation history, using the ngrok service, "cheating" google colab

Projeto da Imersão de IA da Alura + Google 2024

Este projeto realiza uma análise detalhada do nível médio global do mar (GMSL), utilizando uma base de dados pública que abrange medições históricas. O objetivo é explorar tendências, calcular a taxa média de elevação e visualizar os dados por meio de gráficos.