Rock-Vs-Mine Prediction

A sonar rock vs mine predictive system using logistic regression aims to classify sonar signals as either coming from rocks or mines (metal cylinders).

Creating a predictive system involves several steps, including data preprocessing, building the logistic regression model, training the model, evaluating its performance, and making predictions. Here's a detailed approach to building a sonar rock vs. mine predictive system using logistic regression:

Step 1: Data Collection

• Obtain the dataset. For this example, we will use the "Sonar, Mines vs. Rocks" dataset from Kaggle.

Step 2: Data Preprocessing

1. Load the Data:

   • Import the necessary libraries.
   • Load the dataset into a Pandas DataFrame.

2. Normalize the Data:

   • Normalize the features to bring them to a similar scale.

3. Label Encoding:

   • Convert the categorical labels ('R' for rock, 'M' for mine) into binary numerical values (0 and 1).

Step 3: Building the Logistic Regression Model

• Use the LogisticRegression class from scikit-learn.

Step 4: Training the Model

• Split the dataset into training and testing sets.
• Train the model using the training data.

Step 5: Evaluating the Model

• Evaluate the model's performance using metrics such as accuracy score.

Step 6: Making Predictions

• Use the trained model to make predictions on new data.

• While on Training data you will be get the accuracy of 83.42245989304813 %

• And test data accuracy obtained is 76.19047619047619 %