K-Nearest Neighbors (KNN)
সম্পূর্ণ বাংলা লেকচার নোট

# প্রয়োজনীয় লাইব্রেরি Import করি
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Dataset তৈরি করি
data = {
    'study_hours':    [1,2,2,3,4,4,5,5,6,6,7,7,8,8,9,3,5,7,2,8],
    'attendance':     [40,50,45,55,60,70,65,75,80,85,90,88,95,92,98,48,72,88,42,96],
    'previous_marks': [30,40,35,45,50,55,60,58,65,70,75,72,80,78,88,38,62,79,33,84],
    'pass_fail':      [0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,0,1,1,0,1]
}

df = pd.DataFrame(data)
print(df.head(10))

# X = Input Features, y = Target
X = df[['study_hours', 'attendance', 'previous_marks']]
y = df['pass_fail']

print("Feature আকার:", X.shape)
print("Target আকার:", y.shape)

# Scaling করার আগে ডেটার পরিসর দেখি
print("Scaling-এর আগে:")
print(X.describe().loc[['min','max']])

# StandardScaler দিয়ে সব Feature-কে একই পরিসরে আনি
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)
print("\nScaling-এর পরে (প্রথম ৫ সারি):")
print(X_scaled_df.head())

# Scaled ডেটা দিয়ে Train-Test ভাগ করি
X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y,
    test_size=0.2,
    random_state=42
)

print("Training ডেটা:", X_train.shape)
print("Testing ডেটা:", X_test.shape)

# KNN মডেল তৈরি ও Training
knn_model = KNeighborsClassifier(n_neighbors=5)
knn_model.fit(X_train, y_train)

print("✅ KNN মডেল Training সম্পন্ন! (K=5)")

y_pred = knn_model.predict(X_test)
print("Predicted Class:", y_pred)
print("Actual Class:   ", y_test.values)

accuracy = accuracy_score(y_test, y_pred)
print(f"✅ Model Accuracy: {accuracy:.2%}")

print("\n📊 Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))

print("\n📋 Classification Report:")
print(classification_report(y_test, y_pred, target_names=['Fail','Pass']))

# বিভিন্ন K দিয়ে Accuracy তুলনা করি
k_values = [1, 3, 5, 7, 9]

for k in k_values:
    model_k = KNeighborsClassifier(n_neighbors=k)
    model_k.fit(X_train, y_train)
    pred_k = model_k.predict(X_test)
    acc_k = accuracy_score(y_test, pred_k)
    print(f"K={k}: Accuracy = {acc_k:.2%}")