Data Analyst | MSc Data Analytics
The motivation behind this set of YouTube videos is to showcase the applications I’ve been developing. I find Streamlit functionalities fascinating, and on top of that, it’s free. In these videos, I will demonstrate how to parse JSON APIs and visualise the data using Folium, as well as how to model time series data with neural networks, such as AutoReg for stocks and LSTM for cryptocurrencies. Hopefully, these tutorials will inspire like-minded individuals and spark new ideas!
Learn how to deploy your Streamlit app in minutes! This step-by-step tutorial covers GitHub repository setup, Streamlit Cloud deployment, and running apps locally. Perfect for data scientists, Python developers, and ML enthusiasts looking to build and share interactive web apps easily!
Predict cryptocurrency prices with machine learning! CryptoPredictor uses LSTM neural networks, Yahoo Finance API, and CoinGecko API to provide real-time data and interactive charts. The app was developed on Ubuntu using python and deployed on Streamlit!
Predict S&P 500 stock prices with machine learning. SP500Forecaster leverages historical stock data, Yahoo Finance API, and AutoReg models to generate real-time forecasts. It offers trend analysis, predictive modeling, and market insights based on historical data.
Perform key statistical tests with Python and Streamlit. The app enables normality checks, confidence interval calculations, and significance testing, leveraging NumPy, SciPy, Matplotlib, and Pandas for data analysis and visualisation.