copy_of_sonar_data.py

# -*- coding: utf-8 -*-
"""Copy of sonar_data.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1NkMCOtdyScShZCGgmQVw-opxtzABgQ_d
"""

#importing the libraries

import pandas as pd 
import numpy as np

from google.colab import drive
drive.mount('/content/drive')

path = '/content/drive/MyDrive/sonar ml project/Copy of sonar data.csv'

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

sonar_data = pd.read_csv(path , header=None)

sonar_data.head()

# number of rowa and columns
sonar_data.shape

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 amd Testing 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)

#training the Logistic Regression model with training data
model.fit(X_train, Y_train)

"""Model Evaluation"""

#accuracy on trainig data 
X_test_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_test_prediction, Y_train)

print('Accuracy on traing the data :' , training_data_accuracy)

"""Making a Predictive System """

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')