Dataset dapat diunduh di link berikut ini:
https://drive.google.com/file/d/1pQxtljlNVh0DHYg-Ye7dtpDTlFceHVfa/view
Importing the Depedencies
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_scoreData Collection and Data Processing
#loading the dataset to a pandas Dataframe
sonar_data = pd.read_csv('sonar_data.csv', header=None)
sonar_data.head()# number of rows and columns
sonar_data.shape# describe --> statistical measures of the data
sonar_data.describe()sonar_data[60].value_counts()M –> Mine
R –> Rock
sonar_data.groupby(60).mean()# separating data and Labels
X = sonar_data.drop(columns=60, axis=1)
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 Regression
model = LogisticRegression()
# training the Logistic Regression model with training data
model.fit(X_train, Y_train)Model Evaluation
# accuracy on 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)Making a Predictive System
input_data = (0.0056,0.0267,0.0221,0.0561,0.0936,0.1146,0.0706,0.0996,0.1673,0.1859,0.2481,0.2712,0.2934,0.2637,0.1880,0.1405,0.2028,0.2613,0.2778,0.3346,0.3830,0.4003,0.5114,0.6860,0.7490,0.7843,0.9021,1.0000,0.8888,0.6511,0.6083,0.4463,0.2948,0.1729,0.1488,0.0801,0.1770,0.1382,0.2404,0.2046,0.1970,0.2778,0.1377,0.0685,0.0664,0.1665,0.1807,0.1245,0.0516,0.0044,0.0185,0.0072,0.0055,0.0074,0.0068,0.0084,0.0037,0.0024,0.0034,0.0007)
# 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')
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Seorang pemuda luar biasa yang mempunyai hobi menulis, membaca, dan bermusik. Tertarik dengan bidang ilmu komputer untuk memecahkan beberapa persoalan. Co-Founder Triglav ID dan Co-Founder METLIGO. Sejak tahun 2018 bekerja di BMKG di bagian Pusat Database.
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