This repository focuses on applying machine learning models to classify complaint types using natural language processing (NLP) techniques. We aim to predict whether a complaint is related to computer issues or non-computer issues using various ML models.
We are working with two important .csv files that contain:
- Computer complaints
- Non-computer complaints
All complaints are in Spanish, which adds an interesting dimension to the text processing and classification.
As shown above, we are dealing with a moderately sized dataset.
Our goal is to build a machine learning model that can accurately classify complaints into two categories:
- Computer-related complaints
- Non-computer-related complaints
Since machine learning models only work with numerical data, we need to transform our text data (complaints in Spanish) into a numerical format. To achieve this, we will use the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm.
TF-IDF is a popular algorithm in NLP used to convert text into a format that can be processed by machine learning models. It assigns weights to words based on their importance in a document and across a collection of documents.
- Term Frequency (TF): Measures how frequently a term appears in a document.
- Inverse Document Frequency (IDF): Measures how important a term is across the entire collection.
In Scikit-learn, this transformation is done using TfidfVectorizer.
from sklearn.feature_extraction.text import TfidfVectorizer
legal_tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS))
legal_tfidf.fit(df[df['tipo'] == 'denuncia-Legal']['Denuncias'])
legal_vocab = legal_tfidf.vocabulary_
This snippet transforms the complaint texts into numerical vectors and builds a vocabulary from the legal complaint data.
After applying TF-IDF, we define the features and labels:
legal_features = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS), vocabulary=legal_vocab).fit_transform(df[df['tipo'] == 'denuncia-Legal']['Denuncias'])
legal_labels = labels[df['tipo'] == 'denuncia-Legal']
The legal_features are extracted using the defined vocabulary, and legal_labels contain the corresponding labels for the complaints.
We experiment with several machine learning models to find the best one for our classification task:
The RandomForestClassifier is an ensemble learning method based on decision trees. It creates multiple decision trees and combines their predictions to make the final classification.
n_estimators: Number of decision trees.max_depth: Limits the depth of each tree.random_state: Seed for reproducibility.
LinearSVC (Linear Support Vector Classifier) is a linear model for classification:
- Uses a linear kernel by default.
- Finds the best hyperplane that separates different classes.
- Supports regularization to prevent overfitting.
MultinomialNB (Multinomial Naive Bayes) is based on Bayes' theorem:
- Suitable for text classification tasks.
- Models the likelihood of each feature's occurrence given the class.
LogisticRegression is a linear model for binary and multi-class classification:
- Estimates the probability of belonging to a certain class using a logistic function.
- Supports regularization to control model complexity.
Applying our data to these models, we find that the LinearSVC performs the best.
So, the LinearSVC model achieves 100% effectiveness, confirming its superior performance for our dataset.