Machine Learning- Classifying Authenticity of Banknotes

 Introduction:

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning provides a computer with data, rather than explicit instructions. Using these data, the computer learns to recognize patterns and becomes able to execute tasks on its own.

Today we are going to train the machine to predict that it is an actual and fake banknote based on some previous value. We have a dataset. The four left columns are data that we can use to predict whether a note is genuine or counterfeit,  which is external data provided by a human, coded as 0 and 1. Now we can train our model on this data set and see if we can predict whether new banknotes are genuine or not. Today we are using. Today I use perceptron learning, K-nearest neighbor, SVM, and GaussianNB to check which model performs well.

This is an Artificial Intelligence course conducted in City Universit by Nuruzzaman Faruqui. This is the best course in Bangladesh. Because Sir explained all the necessary things to us well. The problem we are dealing with in the lab. First, sir gives us a very good idea of the problem by using many examples. After that sir explains about the solutions like how we can solve the problem, how they work. For that, we understand everything very easily.

Problem:

The problem is :

We have a dataset. Now we are going to train the machine to predict that it is an actual and fake banknote based on some previous value.

 Pseudocode :

# pip install scikit-learn
import csv
import random

from sklearn import svm
from sklearn.linear_model import Perceptron
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier

model = Perceptron()
# model = svm.SVC()
# model = KNeighborsClassifier(n_neighbors=1)
# model = GaussianNB()

# Read data in from file
with open("banknotes.csv") as f:
    reader = csv.reader(f)
    next(reader)

    data = []
    for row in reader:
        data.append({
            "evidence": [float(cell) for cell in row[:4]],
            "label": "Authentic" if row[4] == "0" else "Counterfeit"
        })

# Separate data into training and testing groups
holdout = int(0.40 * len(data))
random.shuffle(data)
testing = data[:holdout]
training = data[holdout:]

# Train model on training set
X_training = [row["evidence"] for row in training]
y_training = [row["label"] for row in training]
model.fit(X_training, y_training)

# Make predictions on the testing set
X_testing = [row["evidence"] for row in testing]
y_testing = [row["label"] for row in testing]
predictions = model.predict(X_testing)

# Compute how well we performed
correct = 0
incorrect = 0
total = 0
for actual, predicted in zip(y_testing, predictions):
    total += 1
    if actual == predicted:
        correct += 1
    else:
        incorrect += 1

# Print results
print(f"Results for model {type(model).__name__}")
print(f"Correct: {correct}")
print(f"Incorrect: {incorrect}")
print(f"Accuracy: {100 * correct / total:.2f}%")

Result : 

If everything is fine then we will get this type of result. 

We can see that KneighborClassifier perfom well its accuracy is 100%. But GaussianNB has very poor accuracy it's 83.03%

Conclusion : 

First, we introduced the problem. After that, we explain the problem and how we can solve the problem. then we explain the algorithm after that we write the code and give comments that's why you can easily understand the code. I hope anyone can easily understand the code. we discuss the result. If anyone follows this article then he/she can easily understand everything, They can also do this very easily. 

we can say this is the only reflection of the way that Sir has taught us. So this is the best AI course in Bangladesh. 

 

You are free to copy the code from here or some other concluding remarks