hf_knn_project.py

from datasets import load_dataset
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

print("1. Downloading dataset from Hugging Face...")
# Fetching a clean tabular dataset directly from Hugging Face Hub
dataset = load_dataset("scikit-learn/iris", split="train")
df = pd.DataFrame(dataset)

# Features (Measurements) and Target (Species)
X = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]
y = df['target']

print("2. Splitting and scaling data...")
# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features for accurate KNN distance calculations
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("3. Training the KNN Model...")
# Train KNN with 3 neighbors
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train_scaled, y_train)

# Check model accuracy
y_pred = knn.predict(X_test_scaled)
print(f"Model Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%")

# Test a brand new sample prediction
new_sample = [[5.1, 3.5, 1.4, 0.2]]
new_sample_scaled = scaler.transform(new_sample)
prediction = knn.predict(new_sample_scaled)
print(f"Prediction for new sample [Class]: {prediction[0]}")

print("4. Generating Graph...")
# Simple 2D plot using first two features (Sepal Length vs Sepal Width)
plt.figure(figsize=(8, 6))
plt.scatter(df['sepal length (cm)'], df['sepal width (cm)'], c=df['target'], cmap='viridis', s=80, edgecolors='k')
plt.title("Hugging Face Dataset - KNN Classification (Iris Sepal Dimensions)")
plt.xlabel("Sepal Length (cm)")
plt.ylabel("Sepal Width (cm)")
plt.grid(True)
plt.show()