fixed issue #422 (#546)

## Pull Request for PyVerse 💡

### Requesting to submit a pull request to the PyVerse repository.

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#### Issue Title
**Please enter the title of the issue related to your pull request.**   
#422  SONAR Rock vs Mine Prediction with Machine learning 
- [x] I have provided the issue title.

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#### Info about the Related Issue
**What's the goal of the project?**  

This project is a machine learning solution for classifying objects
detected by a SONAR signal as either a rock or a mine. SONAR (Sound
Navigation and Ranging) is often used in underwater detection systems,
and it can be challenging to accurately identify whether the detected
object is a harmless rock or a dangerous mine. This project aims to
build a machine learning model to predict the classification of the
object based on the characteristics of the SONAR signal.


- [x] I have described the aim of the project.

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#### Name
**Please mention your name.**  
Shrishti Singh

- [x] I have provided my name.

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#### GitHub ID
**Please mention your GitHub ID.**  
@ShrishtiSingh26

- [x] I have provided my GitHub ID.

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#### Email ID
**Please mention your email ID for further communication.**  
[email protected]

- [x] I have provided my email ID.

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#### Identify Yourself
**Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC,
SWOC).**
contributor at GSSOC- Extd, Hacktoberfest
- [x] I have mentioned my participant role.

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#### Closes
**Enter the issue number that will be closed through this PR.**  
Closes: #422 

- [x] I have provided the issue number.

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#### Describe the Add-ons or Changes You've Made
**Give a clear description of what you have added or modified.**  
Added a machine learning model to predict SONAR Rock Vs. Mine


- [x] I have described my changes.

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#### Type of Change
  
- [ ] Bug fix (non-breaking change which fixes an issue)
- [x] New feature (non-breaking change which adds functionality)
- [ ] Code style update (formatting, local variables)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] This change requires a documentation update

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#### How Has This Been Tested?
The testing process for the model involved splitting the dataset into
training and testing sets, scaling the data, and training the model on
the training set. Predictions were made on the test data, and
performance was evaluated,

- [x] I have described my testing process.

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#### Checklist
**Please confirm the following:**  
- [x] My code follows the guidelines of this project.
- [x] I have performed a self-review of my own code.
- [x] I have commented my code, particularly wherever it was hard to
understand.
- [x] I have made corresponding changes to the documentation.
- [x] My changes generate no new warnings.
- [x] I have added things that prove my fix is effective or that my
feature works.
- [x] Any dependent changes have been merged and published in downstream
modules.

Machine_Learning/SONAR_Rock_ Mine_Prediction/sonar.py

@@ -0,0 +1,69 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load the dataset
+data = pd.read_csv('sonar_data.csv')
+
+# Display the shape of the dataset
+print(data.shape())
+
+# Display information about the dataset
+data.info()
+
+# Check for missing values in the dataset
+print(data.isnull().sum())
+
+# Get statistical summary of the dataset
+print(data.describe())
+
+# Display the columns of the dataset
+print(data.columns)
+
+# Plot the count of the target variable (assuming it is at index 60)
+sns.countplot(data[60])
+plt.show()
+
+# Calculate the mean of each feature grouped by the target variable
+data.groupby(60).mean()
+
+# Separate features and target variable
+x = data.drop(60, axis=1)  # Features
+y = data[60]                # Target variable
+
+# Split the dataset into training and testing sets (80% train, 20% test)
+from sklearn.model_selection import train_test_split
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+
+# Logistic Regression model
+from sklearn.linear_model import LogisticRegression
+lr = LogisticRegression()
+lr.fit(x_train, y_train)             # Train the model
+y_pred1 = lr.predict(x_test)         # Make predictions
+print("Logistic Regression Accuracy:", accuracy_score(y_test, y_pred1))  # Calculate accuracy
+
+# K-Nearest Neighbors model
+from sklearn.neighbors import KNeighborsClassifier
+knn = KNeighborsClassifier(n_neighbors=3)
+knn.fit(x_train, y_train)            # Train the model
+y_pred2 = knn.predict(x_test)        # Make predictions
+print("KNN Accuracy:", accuracy_score(y_test, y_pred2))  # Calculate accuracy
+
+# Random Forest model
+from sklearn.ensemble import RandomForestClassifier
+rf = RandomForestClassifier()
+rf.fit(x_train, y_train)              # Train the model
+y_pred3 = rf.predict(x_test)          # Make predictions
+print("Random Forest Accuracy:", accuracy_score(y_test, y_pred3))  # Calculate accuracy
+
+# Stochastic Gradient Descent model
+from sklearn.linear_model import SGDClassifier
+sgd = SGDClassifier()
+
+# Train using partial_fit
+for i in range(len(x_train)):  # Corrected from 'ramge' to 'range'
+    sgd.partial_fit(x_train[i:i+1], y_train[i:i+1], classes=['R', 'M'])
+
+# Calculate and print the score on the test set
+score = sgd.score(x_test, y_test)
+print("SGD Classifier Accuracy:", score)