"Sentiment Analysis" rev_01

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    - Project: "Sentiment Analysis"
    - Source Code NOT compiled for: Arduino Pro Mini 3.3V
    - Source Code created on: 2025-01-12 10:17:31

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/****** SYSTEM REQUIREMENTS *****/
/****** SYSTEM REQUIREMENT 1 *****/
    /* iam doing sentement anylsysis right then this code */
    /* executed successuffly with accuracy 97 so here i */
    /* also understand x is for my data and y represent */
    /* vector value 0 and1 but these step what are their */
    /* use along with this can you please tell their each */
/****** END SYSTEM REQUIREMENTS *****/

/* START CODE */

/****** DEFINITION OF LIBRARIES *****/
// Include necessary libraries for sentiment analysis
// Note: The following libraries are placeholders as the actual implementations may not exist for Arduino
#include "CountVectorizer.h" // For feature extraction
#include "MultinomialNB.h"   // For the Naive Bayes model
#include "accuracy_score.h"  // For evaluating model accuracy
#include "classification_report.h" // For generating classification reports
#include "train_test_split.h" // For splitting the dataset

/****** FUNCTION PROTOTYPES *****/
void setup(void);
void loop(void);

// Instantiate objects for the libraries
CountVectorizer vectorizer; // Object for feature extraction
MultinomialNB model;         // Object for the Naive Bayes model

void setup(void)
{
    // put your setup code here, to run once:

    // Step 1: Feature Extraction using TF-IDF
    // This step transforms the text data into a numerical format that can be used for modeling.
    // X = vectorizer.fit_transform(df['Data']) // Placeholder for feature extraction

    // Step 2: Label Encoding
    // This step converts categorical labels ('ham' and 'spam') into numerical values (0 and 1).
    // y = df['Review'].map({'ham': 0, 'spam': 1}) // Placeholder for label encoding

    // Step 3: Splitting the Data
    // This step divides the dataset into training and testing sets to evaluate the model's performance.
    // X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) // Placeholder for data splitting

    // Step 4: Model Selection and Training
    // This step selects the Naive Bayes model and trains it using the training data.
    // model.fit(X_train, y_train) // Placeholder for model training

    // Step 5: Making Predictions
    // This step uses the trained model to make predictions on the test data.
    // y_pred = model.predict(X_test) // Placeholder for making predictions

    // Step 6: Evaluating the Model
    // This step evaluates the model's performance by calculating accuracy and generating a classification report.
    // print("Accuracy:", accuracy_score(y_test, y_pred)) // Placeholder for accuracy evaluation
    // print(classification_report(y_test, y_pred)) // Placeholder for classification report
}

void loop(void)
{
    // put your main code here, to run repeatedly:

}

/* END CODE */