Sonar data in a CSV file undergoes preprocessing to prepare it for model training. The processed data is then split into training and testing sets. A Logistic Regression model is built and trained using the training data, and the model is subsequently tested on new data for prediction.
The Dataset has been taken from UC Irvine Machine Learning Repository. This dataset contains patterns obtained by bouncing sonar signals off a metal cylinder and rocks at various angles and conditions. The data set contains signals obtained from a variety of different aspect angles, spanning 90 degrees for the cylinder and 180 degrees for the rock. The Last column in this dataset indicates that, whether it's a mine or a rock, which is useful while making prediction model.
DataSet is included in the repository by the name "Sonar_Dataset.csv".
Libraries: Numpy, Pandas, Scikit-Learn
Model: Logistic Regression
Tool: Google Colaboratory
This is my first machine learning project which is done entirely on Google Colaboratory. During the build I learned how to read CSV file from Google Drive and loading that to a Pandas dataframe, seprating data and label, training & testing data using train_test_split, model training using Logistic Regression, accuracy on training & test data, finally predicting the result by pushing new data into the model.