Project Title: Integration of IoT and AI for Weather Prediction Introduction: The integration of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized various industries, including weather prediction. In this course project, we will explore the integration of IoT and AI to predict future weather using historical weather data from OpenWeatherMap API. The project aims to demonstrate how IoT devices can collect and transmit weather data, and how AI algorithms can be utilized to make accurate weather predictions. Objectives: 1. Retrieve historical weather data from OpenWeatherMap API for a specific city. 2. Extract relevant weather information, such as temperature and humidity, from the historical data. 3. Train a machine learning algorithm using the historical data to make predictions. 4. Implement a simple AI algorithm to predict future weather based on given input values of temperature and humidity. 5. Evaluate the accuracy of the weather predictions using the trained AI model. Methodology: 1. Retrieve historical weather data from OpenWeatherMap API for a specific city using Python and the requests library. 2. Extract relevant weather information, such as temperature and humidity, from the historical data. 3. Train a linear regression model using the historical data and the scikit-learn library. 4. Implement a simple AI algorithm using Python to predict future weather based on given input values of temperature and humidity. 5. Evaluate the accuracy of the weather predictions by comparing them with actual weather data from OpenWeatherMap API.
Project Title: Integration of IoT and AI for Weather Prediction
Introduction: The integration of Internet of Things (IoT) and
various industries, including weather prediction. In this course project, we will explore the integration of IoT
and AI to predict future weather using historical weather data from OpenWeatherMap API. The project aims
to demonstrate how IoT devices can collect and transmit weather data, and how AI algorithms can be utilized
to make accurate weather predictions.
Objectives:
1. Retrieve historical weather data from OpenWeatherMap API for a specific city.
2. Extract relevant weather information, such as temperature and humidity, from the historical data.
3. Train a machine learning
4. Implement a simple AI algorithm to predict future weather based on given input values of temperature
and humidity.
5. Evaluate the accuracy of the weather predictions using the trained AI model.
Methodology:
1. Retrieve historical weather data from OpenWeatherMap API for a specific city using Python and the
requests library.
2. Extract relevant weather information, such as temperature and humidity, from the historical data.
3. Train a linear regression model using the historical data and the scikit-learn library.
4. Implement a simple AI algorithm using Python to predict future weather based on given input values of
temperature and humidity.
5. Evaluate the accuracy of the weather predictions by comparing them with actual weather data from
OpenWeatherMap API.
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