Project 4. Fake News Prediction using Machine Learning with Python | Machine Learning Projects

Introduction to building a machine learning system that predicts whether news is fake or real using textual data. Mention of the data collection process involving labeled news articles with details like author and title. Overview of the challenges in preprocessing textual data compared to numerical data.

Explanation of the challenges in preprocessing textual data due to computers' understanding of numbers rather than text. Importance of converting text to meaningful numbers using various preprocessing functions for machine comprehension.

Process of splitting the dataset into training and test data for machine learning model training. Usage of a logistic regression model for binary classification (real or fake news). Overview of training and evaluating the model using the test data.

Description of evaluating the trained logistic regression model, calculating accuracy scores, and predicting news authenticity using the model. Utilization of the logistic regression model to classify news as real or fake based on textual data features.

Use of Python code to explain the math behind the logistic regression model. Mention of Google Colab for coding and accessing datasets. Overview of the features in the dataset, including IDs, authors, titles, text, and labels indicating real or fake news.

Explanation of importing necessary libraries like NumPy, Pandas, and Regular Expression for data processing. Introduction to functions like NLTK for natural language processing. Mention of TF-IDF Vectorizer and Logistic Regression model imports for building the machine learning model.

Description of the process of removing stopwords using NLTK and applying stemming to convert words to their root forms. Explanation of the stemming procedure to simplify and optimize text data for machine learning model training.

Implementation of the stemming function to process text data by reducing words to their root form using Porter Stemmer. Execution of the stemming procedure on the content column to prepare the data for further processing.

Separation of data and labels for machine learning training. Usage of TF-IDF Vectorizer to convert textual data into numerical form to feed into the model. Transformation of text data into meaningful numbers for computational understanding.

The purpose of TF-IDF is to assign a numerical value to important words based on their frequency in a text. It identifies significant words by detecting repetition and reduces the importance of common words like movie names.

Text is converted to feature vectors using the TF-IDF vectorizer function to create numerical representations that machine learning models can comprehend.

The data set is split into training and test data using the train_test_split function to enable model evaluation on unseen data.

A logistic regression model is trained using the fit function with training data to create a predictive system for classifying text data as real or fake news.

The accuracy of the trained model is evaluated on both training and test data to assess its performance in predicting text data labels.

A predictive system is developed using the trained model to classify new text data as real or fake news by making predictions based on the model's training.