Project Title: cs913-dissertation-project

Project Overview

Code and necessary files for the dissertation project for Data Analytics MSc @Warwick Uni. The project aims to analyze olive yield prediction using meteorological, climatic, and drought data.

Repository Structure

The repository is organized as follows:

cs913-dissertation-project/
├── data/                        # Contains project data
│   ├── processed/               # Preprocessed datasets
│   └── raw/                     # Raw, unprocessed data
├── notebooks/                   # Jupyter notebooks for preprocessing, analysis, and modeling
├── results/                     # Results from analysis and models
│   ├── figs/                    # Generated figures
│   └── models/                  # Trained machine learning models
├── scripts/                     # Scripts for data processing and analysis
│   ├── google_earth_engine/     # Scripts related to Google Earth Engine
│   └── r_scripts/               # R scripts for analysis
├── .gitignore                   # Git ignore file
├── msc_dissertation_report.pdf  # MSc dissertation report
├── requirements.txt             # Python dependencies for the project
└── README.md                    # Project README file

Detailed Description of Each Component

• data/: This directory contains all the datasets used in the project.

  • raw/: Unprocessed, original data files as obtained from various sources.
  • processed/: Preprocessed datasets ready for analysis.

• notebooks/: This folder contains Jupyter notebooks used for data preprocessing, exploratory data analysis, and model training.

• results/: Stores the outputs of the analysis.

  • figs/: Generated figures such as plots, charts, and visualizations.
  • models/: Contains saved versions of the trained machine learning models.

• scripts/: Directory for storing code scripts used for processing and analysis.

  • google_earth_engine/: Scripts specifically for handling data retrieval and processing via Google Earth Engine.
  • r_scripts/: R scripts used for additional analyses that require the R programming language.

• .gitignore: Specifies files and folders that Git should ignore, such as temporary files or data not to be included in version control.

• msc_dissertation_report.pdf: The final report of the dissertation, detailing methodology, results, and conclusions.

• requirements.txt: Lists all Python dependencies required to run the project. Use pip install -r requirements.txt to install them.

• README.md: This file, providing an overview of the repository and its contents.

Getting Started

Prerequisites

• Python 3.11

Installation

Clone the repository and install the required dependencies:

git clone <repository-url>
cd cs913-dissertation-project
pip install -r requirements.txt

Steps to Activate Virtual Environment and Install Packages

1. Create a virtual environment:

   python -m venv venv

2. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Now you are ready to run the project. Make sure to activate the virtual environment each time you work on the project.

Usage

1. Data Preparation: Start with the Jupyter notebooks in notebooks/ for data preprocessing.
2. Model Training: Use the relevant notebooks to train models using the datasets.
3. Results: View generated figures and saved models in the results/ directory.

Contact

For any questions, please contact reslan3121@gmail.com