https://github.com/mohamedsebaie/flask-deployment_solar-radiation-prediction_project-by-machinelearning-models
Deployment_PROJECT by MachineLearning Models
https://github.com/mohamedsebaie/flask-deployment_solar-radiation-prediction_project-by-machinelearning-models
Last synced: 7 months ago
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Deployment_PROJECT by MachineLearning Models
- Host: GitHub
- URL: https://github.com/mohamedsebaie/flask-deployment_solar-radiation-prediction_project-by-machinelearning-models
- Owner: MohamedSebaie
- Created: 2020-11-01T16:36:00.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-23T16:23:46.000Z (almost 5 years ago)
- Last Synced: 2025-01-21T12:28:33.331Z (9 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 6.13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Deployment_PROJECT(Solar_Radiation_Prediction)
### The `Web Application` I Created found in `This Link`.
#### The Data used here can be found through `This Link` on Kaggle Website.
## Description of Data:
#### The dataset contains such columns as: `wind direction`, `wind speed`, `humidity` and `temperature`.
#### The response parameter that is to be predicted is: `Solar_radiation`.
#### It contains measurements for the past 4 months and you have to `predict the level of solar radiation`.
### `Just imagine that you've got solar energy batteries and you want to know will it be reasonable to use them in future`.
## About The Data:
#### These datasets are meteorological data from the `HI-SEAS weather station` from `four months` (September through December 2016) between Mission IV and Mission V.
### For each dataset, the fields are:
- A row number (1-n) useful in sorting this export's results
- The `UNIX time_t` date (seconds since Jan 1, 1970). Useful in sorting this export's results with other export's results
- The date in yyyy-mm-dd format
- The local time of day in hh:mm:ss 24-hour format
- The numeric data, if any (may be an empty string)
- The text data, if any (may be an empty string)
### The Units Of Each Dataset are:
- `Solar radiation`: watts per meter^2
- `Temperature`: degrees Fahrenheit
- `Humidity`: percent
- `Barometric pressure`: Hg
- `Wind direction`: degrees
- `Wind speed`: miles per hour
- `Sunrise/sunset`: Hawaii time
### - `'Solar radiation'`: The target variable.
## Finally, After creating the `ML Model` and save as `PKL` file, Deploy the model with `FLASK` and upload it to`Heroku Platform`..
## The `Web Application` I Created found in `This Link`.