Data Science

Current work and projects in development:

• Data cleaning and preparation
• Exploratory data analysis
• Predictive modeling
• Data visualization (histograms, correlation plots, heatmaps)
• Utilization of libraries such as Pandas, NumPy, and Matplotlib
• Neural network implementation for image analysis and flood detection

The main objective of this project is to apply these skills to solve real-world problems through data analysis and machine learning techniques.

Project Structure

• utils/: Contains utilities for each project.
• scripts/: Python scripts used for data processing.
• output/: Generated results and visualizations.
• Red Neuronal/: Neural network projects, including FloodNet for flood detection.

Development Environment

To work on this project, we use a virtual environment (venv). Here are the steps to set it up:

1. Create a virtual environment:

   python -m venv venv
   

2. Activate the virtual environment:

   .\venv\Scripts\activate    
   

3. Install the necessary dependencies:

    pip install numpy matplotlib pandas scikit-learn tensorflow keras
   

Additional Details

Data Normalization

Data normalization is a crucial step in our data analysis process. In this project, we have employed techniques such as standardization and min-max normalization to ensure our models function correctly.

Data Training and Testing

In the Testing phase, we trained a normalized data model using INEGI statistics, both for visual representation and backend processing. Our neural network projects, particularly FloodNet, involve training on satellite imagery for flood detection and segmentation.

Tools and Technologies Used

• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.
• Matplotlib: For data visualization.
• Scikit-learn: For predictive modeling and machine learning algorithms.
• TensorFlow & Keras: For building and training neural networks, especially in our flood detection project.

Neural Network Projects

Our repository includes advanced neural network projects, with a focus on:

• FloodNet: A convolutional neural network designed for flood detection and segmentation in satellite imagery.
• Image Analysis: Implementing various architectures for image classification and segmentation tasks.