This project is a machine learning application that predicts whether an object is a rock or a mine based on its features.The model is trained using the Sonar dataset.
I am currently learning machine learning, and this is my first project in the field. The objective is to apply what I've learned and build a predictive model from scratch.
Data Loading and Preprocessing: Load and preprocess the Sonar dataset. Model Training: Train a Logistic Regression model to classify objects. Prediction: Predict whether a new object is a rock or a mine. Evaluation: Evaluate the model's performance using various metrics.
input_data = (0.641, 0.2757, 0.2698, 0.3994, 0.4576, 0.3940, 0.2522, 0.1782, 0.1354, 0.0516, 0.0337, 0.0894, 0.0861, 0.0872, 0.0445, 0.0134, 0.0217, 0.0188, 0.0133, 0.0265, 0.0224, 0.0074, 0.0118, 0.0026, 0.0092, 0.0009, 0.0044, 0.0264, 0.0071, 0.0342, 0.0793, 0.1043, 0.0783, 0.1417, 0.1176, 0.0453, 0.0945, 0.1132, 0.0840, 0.0717, 0.1968, 0.2633, 0.4191, 0.5050, 0.6711, 0.7922, 0.8381, 0.8759, 0.9422, 1.0000, 0.9931, 0.9575, 0.8647, 0.7215, 0.5801, 0.4964, 0.4886, 0.4079, 0.2443, 0.1768)
input_data = (0.2472, 0.3518, 0.3762, 0.2909, 0.2311, 0.3168, 0.3554, 0.3741, 0.4443, 0.3261, 0.1963, 0.0864, 0.1688, 0.1991, 0.1217, 0.0628, 0.0323, 0.0253, 0.0214, 0.0262, 0.0177, 0.0037, 0.0068, 0.0121, 0.0077, 0.0078, 0.0066, 0.0717, 0.1968, 0.2633, 0.4191, 0.5050, 0.6711, 0.7922, 0.8381, 0.8759, 0.9422, 1.0000, 0.9931, 0.9575, 0.8647, 0.7215, 0.5801, 0.4964, 0.4886, 0.4079, 0.2443, 0.1768, 0.2472, 0.3518, 0.3762, 0.2909, 0.2311, 0.3168, 0.3554, 0.3741, 0.4443, 0.3261, 0.1963, 0.0864)