issue resolved

Machine_Learning/SONAR_Rock_ Mine_Prediction/sonar.py

@@ -1,37 +1,69 @@
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
-data=pd.read_csv('sonar_data.csv')
-data.shape()
+
+# 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()
-data.isnull().sum()
-data.describe()
-data.columns
+
+# 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])
-data,groupby(60).mean()
-x=data.drop(60,axis=1)
-y=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)
+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
-from sklearn.metrics import accuracy_score
-lr= LogisticRegression()
-lr.fit(x_train,y_train)
-y_pred1=lr.predict(x_test)
-accuracy_score(y_test,y_pred1)
-knn=KNeighborsClassifier(n_neighbors=3)
-knn.fit(x_train,y_train)
-y_pred2=knn.predict(x_test)
-accuracy_score(y_test,y_pred2)
-rf=RandomForestClassifier()
-rf.fit(x_train,y_train)
-y_pred3=rf.predict(x_test)
-accuracy_score(y_test,y_pred3)
+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()
-for i in ramge(len(x_train)):
-  sgd.partial_fit(x_train[i:i+1],y_train[i:i+1],classes=['R','M'])
-score=sgd.score(x_test,y_test)
-print(score)
+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)