Email and Sms Verification

• Technology: Machine Learning ,NLP ,Gen AI ,Web Development

▪ Model Loading: Load pre-trained machine learning models stored in .pkl files. These models should be trained on labeled datasets containing examples of fake and real emails/SMS messages.

▪ Data Preprocessing: Implement data preprocessing steps to prepare input email and SMS messages for classification. Preprocessing may include tasks such as tokenization, cleaning, and vectorization to transform the raw text data into a format suitable for model input.

▪ Model Classification: Utilize the loaded models to classify input email and SMS messages as either fake or real. Apply the models to the preprocessed data and obtain the classification results.

▪ Result Interpretation: Interpret the classification results to determine whether the input email or SMS is deemed fake or real based on the model predictions. Provide clear and informative output to users indicating the verification outcome.

▪ Scalability and Efficiency: Ensure the verification process is scalable and efficient, especially when dealing with large volumes of incoming emails or SMS messages. Optimize the implementation to handle real-time verification requests with minimal latency and resource consumption.