Rock vs mine prediction.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


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sonar_data = pd.read_csv(r"C:\Users\shiva\Desktop\Copy of sonar data.csv", header= None)


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sonar_data.head()


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#number of columns and rows
sonar_data.shape


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sonar_data.describe()


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sonar_data[60].value_counts()


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sonar_data.groupby(60).mean()


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#seperating data and labels
X = sonar_data.drop(columns = 60 ,axis =1)
Y = sonar_data[60]


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print(X)
print(Y)


# Training and Test data 

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X_train , X_test  , Y_train , Y_test = train_test_split(X,Y, test_size = 0.1 , stratify = Y , random_state=1)


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print(X.shape , X_test.shape , X_train.shape)
print(X_train)
print(Y_train)


# MODEL TRAINING
# 

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model = LogisticRegression()


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#training the logistic regression model with training data
model.fit(X_train, Y_train)


# model evaluation

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#accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_prediction = accuracy_score(X_train_prediction, Y_train)


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print("Accuracy on training data : ", training_data_prediction)


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#accuracy on test DATA
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)


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print("Accuracy on test data is - " , test_data_accuracy)


# MAKING A PREDICTIVE SYSTEM

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input_data = (0.0164,0.0173,0.0347,0.0070,0.0187,0.0671,0.1056,0.0697,0.0962,0.0251,0.0801,0.1056,0.1266,0.0890,0.0198,0.1133,0.2826,0.3234,0.3238,0.4333,0.6068,0.7652,0.9203,0.9719,0.9207,0.7545,0.8289,0.8907,0.7309,0.6896,0.5829,0.4935,0.3101,0.0306,0.0244,0.1108,0.1594,0.1371,0.0696,0.0452,0.0620,0.1421,0.1597,0.1384,0.0372,0.0688,0.0867,0.0513,0.0092,0.0198,0.0118,0.0090,0.0223,0.0179,0.0084,0.0068,0.0032,0.0035,0.0056,0.0040)

#changing input_data to a numpy array 
input_data_as_numpy_array = np.asarray(input_data)

#reshape the np array as we're predictiing for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

prediction = model.predict(input_data_reshaped)
print(prediction)

if (prediction == 'R'):
    print("The object is a rock")
else:    
    print("The object is a mine")


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