Rock_or_mine

rock_or_mine.py

@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+"""Project-1
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1uJlXbRnd1mLtAv8fp2wDPVeWG1QHKbUw
+
+Importing libraries
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+"""Data collection + processing"""
+
+#loading the dataset to a panda df
+sonar_data = pd.read_csv('/content/Copy of sonar data.csv' , header=None)
+
+sonar_data.head()
+
+# number of rows and column
+sonar_data.shape
+
+sonar_data.describe()
+
+sonar_data[60].value_counts()
+
+"""M-->mine
+
+R-->rock
+"""
+
+sonar_data.groupby(60).mean()
+
+#seperate 60th column
+X=sonar_data.drop(columns=60,axis=1)##axis = 0 for rows
+Y=sonar_data[60]
+
+print(X)
+print(Y)
+
+"""Training and test data"""
+
+X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size = 0.1,stratify=Y,random_state=1)
+
+print(X.shape,X_train.shape,X_test.shape)
+
+print(X_train)
+print(Y_train)
+
+"""Model training-->Logistic reg
+
+"""
+
+model = LogisticRegression()
+
+#training the Logistic Regression model with training data
+model.fit(X_train, Y_train)
+
+"""MODEL evaluation
+
+"""
+
+#accuracy of training data
+X_train_prediction = model.predict(X_train)
+training_data_accuracy = accuracy_score(X_train_prediction, Y_train)
+
+print('Accuracy on training data : ', training_data_accuracy)
+
+#accuracy on test data
+X_test_prediction = model.predict(X_test)
+test_data_accuracy = accuracy_score(X_test_prediction, Y_test)
+
+print('Accuracy on test data : ', test_data_accuracy)
+
+input_data = (0.0307,0.0523,0.0653,0.0521,0.0611,0.0577,0.0665,0.0664,0.1460,0.2792,0.3877,0.4992,0.4981,0.4972,0.5607,0.7339,0.8230,0.9173,0.9975,0.9911,0.8240,0.6498,0.5980,0.4862,0.3150,0.1543,0.0989,0.0284,0.1008,0.2636,0.2694,0.2930,0.2925,0.3998,0.3660,0.3172,0.4609,0.4374,0.1820,0.3376,0.6202,0.4448,0.1863,0.1420,0.0589,0.0576,0.0672,0.0269,0.0245,0.0190,0.0063,0.0321,0.0189,0.0137,0.0277,0.0152,0.0052,0.0121,0.0124,0.0055)
+
+# changing the input_data to a numpy array
+input_data_as_numpy_array = np.asarray(input_data)
+
+# reshape the np array as we are predicting for one instance
+input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+prediction = model.predict(input_data_reshaped)
+print(prediction)
+
+if (prediction[0]=='R'):
+  print('The object is a Rock')
+else:
+  print('The object is a mine')