this Naive Bayes classifier implementation is written in a very simple and structured way! I’ve always been interested in understanding AI and ML concepts practically, and this code gave me a solid idea of how such models work in real-world applications. If you’re looking to explore classification and probabilistic learning concepts more deeply through models like this, it’s definitely worth it to learn more.

import sys import math from collections import defaultdict

def train_naive_bayes(X_train, y_train): # Count the number of instances for each class (+1, -1) class_count = defaultdict(int) feature_count = defaultdict(lambda: defaultdict(int))

for i in range(len(X_train)):
    label = y_train[i]
    class_count[label] += 1
    for j, val in enumerate(X_train[i]):
        feature_count[label][j, val] += 1

return class_count, feature_count

def predict_naive_bayes(X_test, class_count, feature_count): predictions = [] total_docs = sum(class_count.values())

for x in X_test:
    probs = {}

    for label in class_count:
        prob_label = math.log(class_count[label] / total_docs)
        prob_features = 0

        for j, val in enumerate(x):
            feature_prob = (feature_count[label].get((j, val), 0) + 1) / (class_count[label] + len(x))
            prob_features += math.log(feature_prob)

        probs[label] = prob_label + prob_features

    # Assign the class with the highest probability
    predicted_label = max(probs, key=probs.get)
    predictions.append(predicted_label)

return predictions

if name == 'main': input_data = sys.stdin.read().splitlines()

N, M = map(int, input_data[0].split())

X_train = []
y_train = []

# Read the training data
for i in range(1, N + 1):
    parts = input_data[i].split()
    label = 1 if parts[1] == '+1' else -1
    features = [0.0] * M
    for feat in parts[2:]:
        idx, val = feat.split(':')
        features[int(idx) - 1] = float(val)
    X_train.append(features)
    y_train.append(label)

# Read query data
q = int(input_data[N + 1])
query_ids = []
X_test = []

for i in range(N + 2, N + 2 + q):
    parts = input_data[i].split()
    query_ids.append(parts[0])
    features = [0.0] * M
    for feat in parts[1:]:
        idx, val = feat.split(':')
        features[int(idx) - 1] = float(val)
    X_test.append(features)

# Train the Naive Bayes model
class_count, feature_count = train_naive_bayes(X_train, y_train)

# Predict the results
predictions = predict_naive_bayes(X_test, class_count, feature_count)

# Print the output
for i in range(q):
    print(f"{query_ids[i]} {'+1' if predictions[i] == 1 else '-1'}")

Is there are method through which I can embed this on my black friday website?

this test is so aestethic ..done alhamdulillah

even after passing both test cases it is showing "try again"? Can anyone suggest what to do