In the world of finance, predicting stock prices has always been a challenge that captures the imagination of investors, researchers, and data scientists alike. The ability to anticipate future price movements could potentially lead to significant gains, but it’s no secret that the stock market is notoriously unpredictable. In this blog post, we delve into a machine learning project aimed at predicting stock prices using historical data and the insights gained from the process.
The Project’s Purpose
This project’s main goal was to develop a predictive model that could forecast stock prices for a given future date. To achieve this, we turned to historical stock data available from Yahoo Finance. With this data, we embarked on a journey of data analysis, preprocessing, model selection, and evaluation.
Strategy for Solving the Problem
Our approach involved several key steps: acquiring data from a trustworthy source, identifying pertinent features for training, selecting the optimal model, and fine-tuning its parameters to achieve the highest accuracy score. This strategy was devised to effectively tackle the intricate task of predicting stock prices.
Description of Input data
As mentioned before we turned to Yahoo Finance for historical stock data, by using yfinance API to download the information related to stocks. The definition of each input data is presented int he table below.