Advancing Weather Prediction with ML and Python

Project background

The challenge is to improve the accuracy and reliability of weather prediction in Karachi using machine learning techniques and Python programming. Despite advancements in meteorological models, predicting weather patterns in Karachi remains challenging due to its unique geographical location and complex atmospheric conditions. By leveraging machine learning algorithms and Python programming, we aim to develop a predictive model that can effectively forecast weather conditions in Karachi with higher precision and longer lead times. This will enable better preparedness for extreme weather events, enhance resource allocation, and support decision-making processes for various sectors, such as agriculture, transportation, and disaster management.

Project Goal

1. Develop a machine learning model using Python that can accurately predict weather conditions in Karachi.
2. Improve the lead time of weather forecasts, providing earlier warnings for extreme weather events.
3. Enhance the precision of weather predictions, reducing errors and increasing reliability.
4. Increase the understanding of the unique weather patterns and atmospheric conditions in Karachi.
5. Enable better resource allocation and planning for sectors such as agriculture, transportation, and disaster management.
6. Provide valuable insights and data-driven information to support decision-making processes related to weather-related risks and opportunities.
7. Evaluate and measure the performance of the developed model, comparing it with existing forecasting methods.
8. Continuously optimize and refine the model based on feedback and evolving weather patterns.
9. Foster collaboration and knowledge sharing within the meteorological community in Karachi by sharing the project's findings and methodologies.
10. Contribute to the advancement of weather prediction techniques and technologies, benefiting not only Karachi but also other regions facing similar challenges.

Contribution Guidelines

• Have a Look at the project structure and folder overview below to understand where to store/upload your contribution
• If you're creating a task, Go to the task folder and create a new folder with the below naming convention and add a README.md with task details and goals to help other contributors understand
  • Task Folder Naming Convention : task-n-taskname.(n is the task number) ex: task-1-data-analysis, task-2-model-deployment etc.
  • Create a README.md with a table containing information table about all contributions for the task.
• If you're contributing for a task, please make sure to store in relavant location and update the README.md information table with your contribution details.
• Make sure your File names(jupyter notebooks, python files, data sheet file names etc) has proper naming to help others in easily identifing them.
• Please restrict yourself from creating unnessesary folders other than in 'tasks' folder (as above mentioned naming convention) to avoid confusion.

Project Structure

├── LICENSE
├── README.md          <- The top-level README for developers/collaborators using this project.
├── original           <- Original Source Code of the challenge hosted by omdena. Can be used as a reference code for the current project goal.
│ 
│
├── reports            <- Folder containing the final reports/results of this project
│   └── README.md      <- Details about final reports and analysis
│ 
│   
├── src                <- Source code folder for this project
    │
    ├── data           <- Datasets used and collected for this project
    │   
    ├── docs           <- Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
    │
    ├── references     <- Data dictionaries, manuals, and all other explanatory references used 
    │
    ├── tasks          <- Master folder for all individual task folders
    │
    ├── visualizations <- Code and Visualization dashboards generated for the project
    │
    └── results        <- Folder to store Final analysis and modelling results and code.

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Folder Overview

• Original - Folder Containing old/completed Omdena challenge code.
• Reports - Folder to store all Final Reports of this project
• Data - Folder to Store all the data collected and used for this project
• Docs - Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
• References - Folder to store any referneced code/research papers and other useful documents used for this project
• Tasks - Master folder for all tasks
  • All Task Folder names should follow specific naming convention
  • All Task folder names should be in chronologial order (from 1 to n)
  • All Task folders should have a README.md file with task Details and task goals along with an info table containing all code/notebook files with their links and information
  • Update the task-table whenever a task is created and explain the purpose and goals of the task to others.
• Visualization - Folder to store dashboards, analysis and visualization reports
• Results - Folder to store final analysis modelling results for the project.