Tagged python

Documenting my iterative process on creating a multi-model

A comparative study of neural network models (LSTM, CNN-LSTM) for S&P 500 index prediction. The analysis evaluates multiple architectures against baseline strategies like buy-and-hold and naive forecasting, finding that while specialized models like LSTM with Batch Normalization and Regularization improve technical error metrics (MSE, MAE). However, when tested with a simulated trading, beating market returns remains a significant challenge requiring further architectural and strategy refinement.

This project involves building a credit score classification model using PySpark's machine learning library. By leveraging distributed computing, we compare the performance of Multilayer Perceptron, Decision Tree Classifier, and Random Forest Classifier to predict creditworthiness based on selected customer and financial features.

This project explores various capabilities of distributed computing across three distinct analytical domains by processing large datasets to perform descriptive statistics and clustering as well as image classification. The implementation includes Hadoop MapReduce jobs for weather data analysis on a dataset of hourly weather observations and unsupervised learning using Apache Mahout with several distance metrics, on a dataset of french plays. This work also showcases a scalable cat and dog classifier using the CLIP model within a Hadoop Streaming framework.

A comprehensive comparative study of time series forecasting methods across six diverse datasets (Gold, S&P 500, FTSE, USDC/USDT, StackOverflow questions, and cinema tickets). This analysis evaluates 10+ forecasting techniques—from naive baselines to ARIMA/SARIMA models—with quantitative performance metrics (MSE, RMSE, MAE), explores stationarity testing, seasonality decomposition, Monte Carlo simulation, and implements a simulated trading strategy.

Developing a Decision Support System to predict financial literacy and behaviour along with well being based on socioeconomic factors using a Bayesian network.

An exploration into traditional investment strategies, comparing the performance of 'Buy and Hold' and 'Dollar Cost Averaging' across volatile stock datasets.

Part 2 of the analysis focuses on High Risk Financial Vehicles (HRFV), data collected with the Malaysian dataset were inspired by and referenced from a Ghanaian study. We explore how financial literacy influence perception and participation in activities like sports betting and cryptocurrency.

We compare the Romanian dataset with our own surveyed dataset and explore the relationship between the sociodemographic and financial behaviour, with financial well being and financial literacy.