BreathInfo contains the code and documentation for a comprehensive regression analysis of respiratory signals using various machine-learning algorithms. The analysis facilitates the early prediction of respiratory disease biomarkers, facilitating to taking of proactive measures to prevent the diseases.

[1] Random Forest Regression (RFR): Implemented in Python using libraries such as NumPy and Matplotlib. The dataset underwent preprocessing, one-hot encoding, and was split into training and testing sets.
[2] Gradient Boosting Regression (GBR): Utilized boosting techniques and similar preprocessing steps for evaluation.
[3] Linear Regression (LR): Focused on Python, NumPy, and Matplotlib, employing one-hot encoding and visualizing the regression line.
[4] Support Vector Regression (SVR): Implemented in Python using NumPy and Matplotlib, with one-hot encoding and visualizations for assessing performance.
[5] Ridge Regression (RR): Implemented using Python, NumPy, and Matplotlib, with separate X and Y data frames and one-hot encoding.

The regression analysis and implementation of machine learning algorithms in this repository utilize the following libraries:
 Python: The primary programming language for algorithm implementation.
 NumPy: Used for numerical operations and array manipulations.
 Matplotlib: Employed for data visualization and creating informative plots.

For each algorithm, the following steps were performed:
 Data Preprocessing: Appropriate preprocessing steps were applied to the dataset.
 One-Hot Encoding: Categorical variables were encoded using one-hot encoding.
 Training and Testing Sets: The dataset was split into training and testing sets.
 Algorithm Execution: Each algorithm was executed using the specified technologies.
 Performance Evaluation: The performance of each algorithm was evaluated using appropriate regression metrics.
 Visualization: Visualizations were created to provide insights into the effectiveness of each algorithm in the respective tasks.

Detailed results, including performance metrics and visualizations, have been carried out for effective and accurate prediction of respiratory diseases.

• Anand Babu