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Solar Farm Data Analysis FOR 10Academy KIM 0 Challenge

Overview This repository is the hub for a challenge centered around analyzing solar farm data from three countries: Benin, Sierra Leone, and Togo. The challenge is part of a selection process for a 12-week training program, where my skills in Data Engineering (DE), Financial Analytics (FA), and Machine Learning Engineering (MLE) will be put to the test.

Objective The goal of this challenge is to explore and analyze the provided solar farm data to extract meaningful insights. Participants are expected to use data engineering techniques to clean and prepare the data, apply financial analytics to evaluate financial performance, and leverage machine learning to generate predictions and insights regarding the solar farms' operations.

Details of the Challenge

  • Data Source: Datasets from solar farms in Benin, Sierra Leone, and Togo.
  • Focus Areas:
    • Data Engineering (DE): Involves cleaning, transforming, and preparing the data for analysis.
    • Financial Analytics (FA): Focuses on evaluating the financial metrics and performance of the solar farms.
    • Machine Learning Engineering (MLE): Involves developing models to predict and analyze outcomes.

Getting Started

  1. Clone the Repository:

    git clone https://github.com/AschalewMathewosDamtew/MoonLightEnergy.git

  2. Navigate to the Project Directory:

    cd MoonLightEnergy

  3. Set Up Your Environment: Install the necessary dependencies listed in either requirements.txt.

  4. Explore the Data: The data needed for analysis is available in the data directory. Use these datasets for your exploration and analysis.

File Structure

  • data/ - Directory containing both raw and processed datasets.
  • src/notebooks/ - Jupyter notebooks used for analysis and model development.
  • scripts/ - Python scripts dedicated to data processing and analysis.
  • reports/ - Folder for reports and visualizations generated during the analysis.
  • README.md - This file.