This Data Science project, implemented in Python 3, aims to study the stock market by using the Stock Market Dataset available on Kaggle.
The goal of this project is to predict the friday stock market price of a company based on the trends of the past 4 days.
The project consists of three main steps:
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Data preprocessing: In this step, we cleaned the data by removing missing values and replacing outliers. We also performed normalization of numerical variables and encoding of categorical variables.
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Data visualization: This involves exploring the distribution of data through visualizations to better understand the data distribution and analyze the relationships between different features. This also helps determine the most appropriate type of model for the problem.
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Model training: We used a machine learning model based on a Random Forest algorithm to predict a value. We adjusted the model's hyperparameters to get the best possible performance. The model was evaluated using MSE and MAE metrics.
Here are the steps to install the necessary dependencies and run the code for this project:
- Clone the GitHub.
- Make sure you have Python 3 installed on your system.
- Run the cells of the Jupyter notebooks.
This project was carried out as part of a university project with @tomasnp and @salahait35.