Rock vs. Mine Classification Project

This project aims to classify underwater objects as either rocks or mines using sonar data. We will follow a structured workflow to achieve this goal, which includes collecting the sonar data, preprocessing the data, performing train-test split, and feeding the data to a logistic regression model for classification.

Workflow

1. Collecting Sonar Data

Data Source

Dataset: The sonar data for this project was obtained from the Kaggle dataset.

Data Description: The dataset consists of sonar signals, where each signal corresponds to a specific object in the water, which is either a rock or a mine (metal cylinder).

Data Description

The dataset consists of sonar signals, where each signal corresponds to a specific object in the water, which is either a rock or a mine (metal cylinder).

Data Collection Steps

• Download the dataset from the provided source or any other suitable source.
• Explore the dataset to understand its structure and features.

2. Preprocessing the Data

Data Cleaning :

• Check for missing values in the dataset and decide how to handle them.
• Remove any irrelevant or redundant features if necessary.

Feature Engineering :

• Create relevant features or transform existing features to improve model performance.

Data Scaling and Normalization :

• Scale and normalize the features to ensure that all input variables have the same scale.

3. Train-Test Split

Data Splitting :

• Split the dataset into two subsets: a training set and a testing (or validation) set. A common split ratio is 80% for training and 20% for testing.

Purpose :

• The training set is used to train the logistic regression model.
• The testing set is used to evaluate the model's performance and assess its ability to generalize to unseen data.

4. Building the Logistic Regression Model

Model Selection :

• Choose the logistic regression algorithm as the classification model for this project.

Model Training :

• Train the logistic regression model using the training data.

Model Evaluation :

• Evaluate the model's performance on the testing dataset using appropriate metrics such as accuracy, precision, recall, and F1-score.
• Visualize the results, e.g., using confusion matrices or ROC curves, to gain insights into the model's performance.

Hyperparameter Tuning :

• Fine-tune hyperparameters of the logistic regression model, if necessary, using techniques like cross-validation.

5. Model Deployment (Optional)

• Deploy the trained logistic regression model for real-world predictions if the project requires it.

Running the Code

You can run the code for this project by following these steps:

1. Clone this repository to your local machine.

2. Navigate to the Project Directory

   cd Rock_vs_Mine_Prediction
   

3. Ensure you have the required Python libraries installed.You can use the requirements.txt file to install them using pip:

   pip install -r requirements.txt
   

4. Open the Jupyter Notebook

   jupyter notebook
   

5. Execute the cells of the Jupyter Notebook

6. Customize the code and parameters as needed for your specific dataset and requirements.

Conclusion

This README provides an overview of the Rock vs. Mine Classification Project, including the workflow from data collection to model evaluation. You can adapt and expand upon this project to solve similar classification problems or explore more advanced machine learning techniques for better results.