https://github.com/4702chahat/rock-vs-mine
This Project is based on Machine Learning which uses Logistic Regression model for predicting whether the object detected by Submarine is Rock or Mine
https://github.com/4702chahat/rock-vs-mine
accuracy-score data-science deep-learning jupyter-notebook logestic-regression machine-learning numpy-arrays pandas-dataframe predicitve predictive-model python rock-vs-mine sckit-learn sklearn-classifier sklearn-library sklearn-metrics
Last synced: about 1 month ago
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This Project is based on Machine Learning which uses Logistic Regression model for predicting whether the object detected by Submarine is Rock or Mine
- Host: GitHub
- URL: https://github.com/4702chahat/rock-vs-mine
- Owner: 4702chahat
- Created: 2024-07-29T09:06:01.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-29T09:40:07.000Z (almost 2 years ago)
- Last Synced: 2025-03-24T08:49:48.858Z (about 1 year ago)
- Topics: accuracy-score, data-science, deep-learning, jupyter-notebook, logestic-regression, machine-learning, numpy-arrays, pandas-dataframe, predicitve, predictive-model, python, rock-vs-mine, sckit-learn, sklearn-classifier, sklearn-library, sklearn-metrics
- Language: Jupyter Notebook
- Homepage:
- Size: 48.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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 %