Is KNN Algorithm is the Key to Harry Potter’s Success in Destroying Horcruxes AND Defeating Lord Voldemort?

Once upon a time, in the magical realm of Hogwarts, a young wizard named Harry Potter embarked on a daring quest to defeat the dark wizard Lord Voldemort. Little did Harry know that his journey would take him on a path intertwined with the mysteries of machine learning and the power of data science.

As Harry delved deeper into the lore of the wizarding world, he discovered the existence of Horcruxes — objects containing fragments of Voldemort’s soul, making him immortal. Determined to rid the world of Voldemort’s evil once and for all, Harry knew he had to locate and destroy these dark artifacts.

But how could Harry, armed with his wand and courage, uncover the hidden Horcruxes scattered across the wizarding world? That’s when Hermione, ever the clever witch, suggested employing a magical tool from the Muggle world — the K-nearest neighbors (KNN) algorithm.

With Hermione’s guidance, Harry set out to apply the principles of KNN to his quest. The KNN algorithm works by classifying data points based on their similarity to neighboring points. In Harry’s case, each Horcrux could be represented as a data point, with features such as its location, magical properties, and historical significance.

Harry began by collecting data on known Horcruxes from ancient tomes, wizarding archives, and the memories of those who had encountered them. He compiled a dataset containing information about each Horcrux, including its attributes and the circumstances surrounding its creation.

With his dataset in hand, Harry used the KNN algorithm to analyze the similarities between known Horcruxes and potential locations where others might be hidden. By examining the characteristics of each Horcrux and comparing them to those of other magical objects, Harry could identify patterns and infer the presence of additional Horcruxes.

As Harry traversed the wizarding world, he encountered various challenges and obstacles, each requiring him to adapt his approach and refine his model. With each new discovery, Harry gained insights into Voldemort’s twisted mind and the dark magic that bound his soul to the mortal world.

Through perseverance and the power of machine learning, Harry gradually uncovered the hidden Horcruxes, one by one. From the locket of Salazar Slytherin to the diadem of Rowena Ravenclaw, each Horcrux brought him closer to his ultimate goal — the destruction of Voldemort’s soul and the defeat of darkness.

Finally, armed with the knowledge gained from his journey and the support of his friends, Harry faced Voldemort in a final showdown. With the aid of his wisdom, bravery, and the lessons learned from his foray into machine learning, Harry emerged victorious, vanquishing the dark lord and restoring peace to the wizarding world once more.

And so, the tale of Harry Potter and the Quest for Horcruxes came to an end, a testament to the power of magic, friendship, and the endless possibilities that arise when the realms of fantasy and technology intersect.

Lets Take A Dive Into Their Conversation:-

🧙‍♂️🧙‍♀️ Harry and Hermione’s Conversations

Harry: Hermione, I’ve been thinking. We need a way to find those pesky Horcruxes of Voldemort. But how do we locate them amidst all the magic and mystery?

Hermione: Well, Harry, have you heard of something called machine learning? It’s a fascinating branch of magic that involves teaching computers to learn from data and make predictions.

Harry: Machine learning? That sounds intriguing. But how can it help us in our quest?

Hermione: Let me show you, Harry. We can use a powerful spell known as the K-nearest neighbors (KNN) algorithm. It’s like casting a charm to find similarities between objects based on their features.

📊 Exploring the Data

Hermione: First, we gather information about known Horcruxes from ancient scrolls and wizarding archives. Here, let’s load our dataset and take a look at its contents.

import pandas as pd

dataset = pd.read_csv(“Social_Network_Ads.csv”)
dataset.info()
dataset.columns
dataset.head()

Hermione: This code snippet loads our data and displays its structure. Each row represents a magical object, and the columns describe its characteristics.

🔍 Analyzing the Data

Harry: I see, Hermione. But how do we make sense of all this information?

Hermione: Fear not, Harry. We can visualize the data using a magical tool called seaborn. It allows us to see patterns and relationships between the features of our objects.

import seaborn as sns
sns.set()
sns.scatterplot(x=”Age”,y=”EstimatedSalary”,data=dataset,hue=”Purchased”)

Hermione: Look, Harry! This scatter plot reveals how the age and estimated salary of witches and wizards relate to their likelihood of purchasing a Horcrux.

🧠 Training the Model

Harry: Fascinating, Hermione! But how do we use this data to find the Horcruxes?

Hermione: Ah, that’s where the KNN algorithm comes in. We split our data into training and testing sets, then train the model to classify magical objects based on their similarities to others.

X= dataset[[“Age”,”EstimatedSalary”]]
y= dataset[“Purchased”]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
from sklearn.neighbors import KNeighborsClassifier
model= KNeighborsClassifier(n_neighbors=5)
model.fit(X_train,y_train)

Hermione: By teaching the model to recognize patterns in the data, we empower it to predict whether an object is a Horcrux or not.

🔮 Making Predictions

Harry: I’m starting to see, Hermione! But how accurate are these predictions?

Hermione: Let’s put our model to the test. We’ll use it to predict whether certain magical objects are Horcruxes and compare its predictions to the actual outcomes.

model.predict([[20,20000]])
y_pred= model.predict(X_test)

Hermione: With each prediction, our model gains insights into the hidden truths of the wizarding world.

📊 Evaluating Performance

Harry: This is incredible, Hermione! But how do we know if our model is reliable?

Hermione: We assess its accuracy using magical metrics like confusion matrices and accuracy scores.

from sklearn.metrics import confusion_matrix
confusion_matrix(y_test,y_pred)
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_pred)

Hermione: These metrics help us gauge the effectiveness of our model in distinguishing between Horcruxes and ordinary objects.

📉 Optimizing the Model

Harry: Hermione, this is truly remarkable! But how do we ensure our model is as powerful as possible?

Hermione: We experiment with different configurations, tweaking parameters like the number of neighbors, to find the optimal settings.

error_rate=[]
for i in range(1,50):
model= KNeighborsClassifier(n_neighbors=i)
model.fit(X_train,y_train)
y_pred= model.predict(X_test)
error_rate.append(accuracy_score(y_test,y_pred))

Hermione: By fine-tuning our model, we unlock its full potential and enhance its ability to uncover hidden truths.

📈 Visualizing Performance

Harry: Hermione, I’m beginning to grasp the power of machine learning! But how do we interpret these results?

Hermione: We visualize the error rates across different configurations to understand how each parameter affects the model’s performance.

Hermione: This plot helps us see the relationship between the number of neighbors and the accuracy of our predictions.

🌟 Conclusion

Harry: Hermione, you’ve opened my eyes to a whole new world of magic! With the KNN algorithm, we can uncover the secrets of the Horcruxes and defeat Voldemort once and for all.

Hermione: Indeed, Harry! Through the power of machine learning, we can harness the magic of data to triumph over darkness and bring light to the wizarding world.

And so, Harry and Hermione embarked on their journey armed with the wisdom of machine learning, ready to face whatever challenges lay ahead in their quest for the Horcruxes.