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Energy Sector Stock Movement Models

Capstone Project

This capstone project is to fulfill the graduation requirements of the CodingDojo Data Science Bootcamp.

Team Members

  • Salha Nasser @salohnana2018
  • NOUF ALJOHANI @NoufJoh
  • Amal Almutairi @Amal001001
  • Rawan Alsudias @Rawan-Alsudai
  • Rahaf Alzahrani @rhfaalzz1

About The Project



In this project, we analyzed the characteristics and trends of the Saudi energy sector stock market. The gained insight is then used to construct five machine learning models that predict the stock's movement and provide a buy or sell recommendation. The tested machine-learning algorithms include XGBoost,Random Forest classifier,SVM,logistic regression and RandOm Convolutional KErnel Transform (ROCKET) coupled with RidgeClassifierCV. The highest accuracy obtained using the Random Forest classifier.

Data-Science-Campus-Capstone-Project, Saudi energy sector stock market classification,Time-series classificationt

Built With

Prerequisites

Before using the jupyter notebook, ensure to install all the libraries stored in the requirements.txt file.

  • Requirements installation using pip
    pip install -r requirements.txt
    

Installation

  1. Install the required libraries from the requirements.txt

  2. Clone the repo

    git clone https://github.com/salohnana2018/Data-Science-Campus-Capstone-Project.git
  3. Open the part-one Jupyter notebook for data preprocessing and exploratory data analysis

  4. Open the part-two Jupyter notebook for machine-learning model training and evaluations

Note: For the first notebook, to see the plotly graphs offline, you have to run this code:

   import plotly.graph_objs as go 
   #To Set notebook mode to work in offline 
      pyo.init_notebook_mode()

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Links

Acknowledgments

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