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https://github.com/ahmad-ali-rafique/weather-prediction-fcnn
This project demonstrates a complete pipeline for weather prediction using a Fully Connected Neural Network (FCNN). The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation.
https://github.com/ahmad-ali-rafique/weather-prediction-fcnn
ai artificial-intelligence data-analysis data-science deep-learning deep-neural-networks fully-connected-network machine-learning machine-learning-algorithms weather-information
Last synced: 2 days ago
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This project demonstrates a complete pipeline for weather prediction using a Fully Connected Neural Network (FCNN). The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation.
- Host: GitHub
- URL: https://github.com/ahmad-ali-rafique/weather-prediction-fcnn
- Owner: Ahmad-Ali-Rafique
- Created: 2024-06-15T07:24:08.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-06-15T07:37:47.000Z (5 months ago)
- Last Synced: 2024-06-15T08:35:59.519Z (5 months ago)
- Topics: ai, artificial-intelligence, data-analysis, data-science, deep-learning, deep-neural-networks, fully-connected-network, machine-learning, machine-learning-algorithms, weather-information
- Language: Jupyter Notebook
- Homepage:
- Size: 4.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Weather Prediction using Fully Connected Neural Networks (FCNN)
## Overview
This project demonstrates a complete pipeline for weather prediction using a Fully Connected Neural Network (FCNN). The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation.
## Project Description
The project uses a weather dataset that includes various meteorological features such as temperature, humidity, wind speed, and precipitation. The pipeline includes:
1. **Data Loading**: Loading the weather dataset from a public source.
2. **Data Preprocessing**: Normalizing and preparing the data to be suitable for the FCNN model.
3. **Model Training**: Building and training a Fully Connected Neural Network using TensorFlow/Keras.
4. **Performance Evaluation**: Evaluating the model's accuracy and other metrics on the test set, and visualizing the results.
### Key Features
- **Data Preprocessing**: Techniques such as normalization and feature engineering for optimal model performance.
- **Model Architecture**: Details of the FCNN layers, activation functions, and optimization techniques.
- **Evaluation Metrics**: Accuracy, loss, RMSE, and visualizations to assess the model's performance.
## About Me
Hi, I'm Ahmad Ali, a passionate data scientist and machine learning enthusiast with a knack for solving complex problems using data-driven approaches. I have a strong background in [your field of study or work], and I enjoy working on projects that involve deep learning, computer vision, and natural language processing.
### Get in Touch
- **GitHub**: [https://github.com/yourusername](https://github.com/yourusername)
- **LinkedIn**: [https://www.linkedin.com/in/yourprofile](https://www.linkedin.com/in/yourprofile)
- **Email**: [email protected]
Feel free to explore the repository, raise issues, or contribute to the project. Let's connect and collaborate on exciting projects!