UmairFaheem042/spam-classifier

SPAM SMS-MAIL CLASSIFIER

Dataset: spam.csv from Kaggle

Algorithm: Naive Bayes Classifier

Screenshots:

Flow of the Model

Importing Libraries

1. The program begins by importing necessary libraries like numpy, pandas, and scikit-learn modules.
2. Pandas is used for data manipulation, while scikit-learn is used for machine learning.

Loading Data

1. The program loads a dataset spam.csv using pandas.read_csv(). The data likely consists of text messages and labels spam or ham.

Data Preprocessing

1. Columns that are not necessary (like unnamed columns) are dropped from the dataset.
2. The columns are renamed: one column is renamed to target (indicating spam or ham), and other column is renamed to text.
3. The target labels are encoded using LabelEncoder where the classes spam and ham are transformed into binary values 0 and 1.

Handling Missing or Duplicate Data

1. The program checks for missing values using isnull().sum() and counts the number of duplicates using df.duplicated().sum().
2. Duplicates are dropped, keeping only the first occurrence.

Text Preprocessing

1. Text data is preprocessed by converting it into lowercase, tokenizing, removing special characters, and stopwords.
2. The text data is then transformed into vectors using TF-IDF (Term Frequency-Inverse Document Frequency) via TfidfVectorizer. This converts the text into a numerical format suitable for machine learning models.

Splitting Data

1. The dataset is split into training and testing sets using train_test_split(). A portion(80%) of the data is used for training, and the remaining 20% is used for testing the model.

Model Training

1. Three Naive Bayes classifiers are instantiated: GaussianNB, MultinomialNB & BernoulliNB

2. Each of these classifiers is trained on the training data using fit().

Model Evaluation

1. After training, the program evaluates each classifier on the test data using: Accuracy Score(Percentage of correct predictions) Confusion Matrix(A matrix summarizing true positives, false positives, true negatives, and false negatives.) Precision Score(Measures the precision of the classifier for spam prediction.)

2. These metrics help compare the performance of the classifiers.

Saving the Model

1. After the evaluation, the program saves the trained model MultinomialNB and the vectorizer TfidfVectorizer using the pickle library.
2. These saved models can be loaded later for real-time spam classification.

Why This Approach Was Used?

1. Naive Bayes Classifiers: Naive Bayes is ideal for text classification problems due to its simplicity, effectiveness, and speed, especially when dealing with large datasets like text.

2. TF-IDF Vectorization: Converting text data into numerical form using TF-IDF is a common approach in Natural Language Processing (NLP). It helps to capture the importance of words in relation to their frequency in the text.

3. Model Comparison: By training multiple Naive Bayes models, the program ensures the best-performing model is chosen based on accuracy and precision metrics.

This structure allows for effective detection of spam in text data with high accuracy and precision, particularly using the MultinomialNB classifier which works well for discrete features like word counts.