diff --git a/code/artificial_intelligence/src/naive_bayes/naive_bayes_algorithm.cpp b/code/artificial_intelligence/src/naive_bayes/naive_bayes_algorithm.cpp
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+// ### Naive Bayes Classifier Implementation
+
+// This PR adds an implementation of the Naive Bayes classifier with the following features:
+// - Training with Laplace smoothing.
+// - Prediction based on log-probabilities.
+// - Data input from CSV or manual user input.
+// - Model parameters and predictions are displayed.
+
+// ### Time Complexity:
+// - **fit()**: O(n * m) - where `n` is the number of training samples, and `m` is the number of features.
+// - **predict()**: O(t * m * k) - where `t` is the number of test samples, `m` is the number of features, and `k` is the number of classes.
+// - **load_data_from_csv()**: O(n * m) - where `n` is the number of data samples, and `m` is the number of features.
+// - **interactive_input()**: O(n * m) - where `n` is the number of samples, and `m` is the number of features.
+
+// ### Space Complexity:
+// - **fit()**: O(k * m) - where `k` is the number of classes, and `m` is the number of features.
+// - **predict()**: O(t) - where `t` is the number of test samples.
+// - **Data Storage**: O(n * m) for training data, O(t * m) for test data.
+#include <iostream>
+#include <vector>
+#include <set>
+#include <map>
+#include <cmath>
+#include <algorithm>
+#include <numeric>
+#include <sstream>
+#include <fstream>
+#include <string>
+
+using namespace std;
+
+class NaiveBayes {
+private:
+    double laplace_smoothing;
+    vector<int> classes;
+    map<int, int> class_indices;
+    vector<double> class_priors;
+    map<int, map<int, int>> feature_counts; 
+    map<int, int> class_counts;  
+    map<int, int> feature_sums;  
+    int num_features;
+
+    vector<double> split_string(const string& s, char delimiter) {
+        vector<double> tokens;
+        string token;
+        istringstream tokenStream(s);
+        while (getline(tokenStream, token, delimiter)) {
+            tokens.push_back(stod(token));
+        }
+        return tokens;
+    }
+
+public:
+    NaiveBayes(double smoothing = 1.0) : laplace_smoothing(smoothing), num_features(0) {}
+
+    void fit(const vector<vector<double>>& X, const vector<int>& y) {
+        // Get unique sorted classes
+        set<int> unique_classes(y.begin(), y.end());
+        classes.assign(unique_classes.begin(), unique_classes.end());
+        sort(classes.begin(), classes.end());
+
+
+        for (size_t i = 0; i < classes.size(); ++i) {
+            class_indices[classes[i]] = i;
+        }
+
+        class_counts.clear();
+        feature_counts.clear();
+        feature_sums.clear();
+
+        vector<int> counts(classes.size(), 0);
+        for (int label : y) {
+            counts[class_indices[label]]++;
+        }
+
+        class_priors.resize(classes.size());
+        for (size_t i = 0; i < classes.size(); ++i) {
+            class_priors[i] = static_cast<double>(counts[i]) / y.size();
+        }
+
+        num_features = X[0].size();  // Number of features in the data
+        for (size_t i = 0; i < y.size(); ++i) {
+            int label = y[i];
+            class_counts[label]++;
+
+            for (size_t j = 0; j < num_features; ++j) {
+                int feature_value = static_cast<int>(X[i][j]);  // Assuming features are discrete
+                feature_counts[label][feature_value]++;
+                feature_sums[label]++;
+            }
+        }
+    }
+
+    vector<int> predict(const vector<vector<double>>& X) {
+        vector<int> predictions;
+        for (const auto& sample : X) {
+            vector<double> log_probs;
+            for (int cls : classes) {
+                int idx = class_indices[cls];
+                
+                double log_prob = log(class_priors[idx]);
+                
+                for (size_t j = 0; j < sample.size(); ++j) {
+                    int feature_value = static_cast<int>(sample[j]);
+                    
+                    int feature_count = feature_counts[cls][feature_value];
+                    int total_feature_count = feature_sums[cls];
+                    
+                    // Apply Laplace smoothing
+                    double feature_prob = (feature_count + laplace_smoothing) / (total_feature_count + laplace_smoothing * num_features);
+                    log_prob += log(feature_prob);
+                }
+
+                log_probs.push_back(log_prob);
+            }
+
+            auto max_it = max_element(log_probs.begin(), log_probs.end());
+            int pred_class = classes[distance(log_probs.begin(), max_it)];
+            predictions.push_back(pred_class);
+        }
+        return predictions;
+    }
+
+    void load_data_from_csv(const string& filename, vector<vector<double>>& X, vector<int>& y, bool has_header = false) {
+        ifstream file(filename);
+        if (!file.is_open()) {
+            cerr << "Error opening file: " << filename << endl;
+            return;
+        }
+
+        string line;
+        if (has_header) {
+            getline(file, line); // Skip header
+        }
+
+        while (getline(file, line)) {
+            vector<double> values = split_string(line, ',');
+            if (values.size() < 2) {
+                cerr << "Invalid data format in line: " << line << endl;
+                continue;
+            }
+
+            // Last value is the class label
+            y.push_back(static_cast<int>(values.back()));
+            values.pop_back();
+            X.push_back(values);
+        }
+    }
+
+    void interactive_input(vector<vector<double>>& X, vector<int>& y) {
+        cout << "Enter number of samples: ";
+        int num_samples;
+        cin >> num_samples;
+
+        cout << "Enter number of features: ";
+        int num_features;
+        cin >> num_features;
+
+        cout << "Enter data (features followed by class label for each sample):" << endl;
+        for (int i = 0; i < num_samples; ++i) {
+            cout << "Sample " << i + 1 << ": ";
+            vector<double> sample;
+            for (int j = 0; j < num_features; ++j) {
+                double value;
+                cin >> value;
+                sample.push_back(value);
+            }
+            int label;
+            cin >> label;
+            X.push_back(sample);
+            y.push_back(label);
+        }
+    }
+
+    void print_model() {
+        cout << "\nTrained Model Parameters:\n";
+        cout << "Classes: ";
+        for (int cls : classes) {
+            cout << cls << " ";
+        }
+        cout << "\n\n";
+
+        for (int cls : classes) {
+            cout << "Class " << cls << ":\n";
+            cout << "  Prior probability: " << class_priors[class_indices[cls]] << "\n";
+            cout << "  Feature counts: ";
+            for (const auto& feature_count : feature_counts[cls]) {
+                cout << feature_count.first << ": " << feature_count.second << " ";
+            }
+            cout << "\n\n";
+        }
+    }
+};
+
+int main() {
+    NaiveBayes nb;
+    vector<vector<double>> X_train;
+    vector<int> y_train;
+    vector<vector<double>> X_test;
+
+    cout << "NAIVE BAYES CLASSIFIER\n";
+    cout << "======================\n\n";
+
+    int choice;
+    cout << "Choose input method:\n";
+    cout << "1. Load from CSV file\n";
+    cout << "2. Enter data manually\n";
+    cout << "Enter choice (1 or 2): ";
+    cin >> choice;
+
+    if (choice == 1) {
+        string filename;
+        cout << "Enter training data filename: ";
+        cin >> filename;
+        nb.load_data_from_csv(filename, X_train, y_train);
+    } else {
+        nb.interactive_input(X_train, y_train);
+    }
+
+    // Train the model
+    nb.fit(X_train, y_train);
+    nb.print_model();
+
+    // Prediction phase
+    cout << "\nPREDICTION PHASE\n";
+    cout << "Enter test data input method:\n";
+    cout << "1. Load from CSV file\n";
+    cout << "2. Enter data manually\n";
+    cout << "Enter choice (1 or 2): ";
+    cin >> choice;
+
+    if (choice == 1) {
+        string filename;
+        cout << "Enter test data filename: ";
+        cin >> filename;
+        vector<int> dummy_labels;
+        nb.load_data_from_csv(filename, X_test, dummy_labels);
+    } else {
+        cout << "Enter number of test samples: ";
+        int num_samples;
+        cin >> num_samples;
+
+        if (num_samples > 0 && X_train.size() > 0) {
+            cout << "Enter " << X_train[0].size() << " features for each sample:\n";
+            for (int i = 0; i < num_samples; ++i) {
+                cout << "Test sample " << i + 1 << ": ";
+                vector<double> sample;
+                for (size_t j = 0; j < X_train[0].size(); ++j) {
+                    double value;
+                    cin >> value;
+                    sample.push_back(value);
+                }
+                X_test.push_back(sample);
+            }
+        }
+    }
+
+    if (!X_test.empty()) {
+        vector<int> predictions = nb.predict(X_test);
+        cout << "\nPredictions:\n";
+        for (size_t i = 0; i < predictions.size(); ++i) {
+            cout << "Test sample " << i + 1 << ": " << predictions[i] << endl;
+        }
+    } else {
+        cout << "No test data provided.\n";
+    }
+
+    return 0;
+}