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
• LinkedIn: https://www.linkedin.com/in/yourprofile
• Email: your.email@example.com

Feel free to explore the repository, raise issues, or contribute to the project. Let's connect and collaborate on exciting projects!