sjapanjots / rock-and-mine-ml-logistic-model-

Rock and Mine ML(logistic regression ) It is a initial level machine learning project for once who want to improve the skills in machine learning the source code and data is being given in repository

Rock-and-Mine-ML-logistic-model-

The primary objective of this model is to enhance the detection and classification of objects in underwater environments, particularly in scenarios where distinguishing between rocks and mines is crucial for safety and security purposes. By leveraging a logistic regression algorithm, the model can learn from labeled data and identify patterns that differentiate rocks from mines.

The training process involves providing the model with a comprehensive dataset comprising sonar readings from various underwater objects, including both rocks and mines. The model learns to extract relevant features from the data and assigns weights to each feature, which collectively contribute to the classification decision.

During the prediction phase, the trained model takes as input new sonar readings from unseen objects and calculates the probability of each object being a rock or a mine. By applying a threshold, typically 0.5, the model classifies the object based on the higher probability. The performance of the Rock and Mine Machine Learning Logistic Model is evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics measure the model's ability to correctly classify rocks and mines and provide insights into its effectiveness.

The logistic model has demonstrated promising results in real-world scenarios, showing a high level of accuracy in classifying underwater objects. However, it is important to note that the performance of the model heavily relies on the quality and representativeness of the training data, as well as the selection and tuning of relevant features.

The Rock and Mine Machine Learning Logistic Model holds great potential for enhancing underwater object classification tasks, such as mine detection for naval operations or environmental monitoring of underwater ecosystems. With further refinement and optimization, this model can contribute to improving safety, security, and scientific exploration in underwater environments.

#MachineLearning #logisticalgorithm #ML