The document describes a project where a machine learning model was developed to detect fake news using logistic regression and random forest classifiers, achieving an accuracy of 98%. The dataset, sourced from Kaggle, contains 23,481 observations with five attributes, and feature engineering was applied to enhance model performance. A prediction function was created to classify news text as either fake or not.

1. Submitted By:
Harpreet Singh
BTech CSE A3
12201070

9. PROPERTIES

12. FIGHT BACK

13. USE OF MACHINE LEARNING
• In this project, we have built a machine learning
model to detect the fake news using machine learning
algorithm.
• Our model can be used by news readers.
• Logistic Regression and Random forest classifier
algorithms are used to create a model with a great
accuracy score.

14. DATASET
• Dataset comes from kaggle.com.
• Link of the dataset –
“https://www.kaggle.com/datasets/bhavikjikadara/fake-news-detection”
• There are 23481 number of observations in our dataset.
• There are a total of 5 columns/attributes in our dataset.
• The all 5 columns are title, text, subject, date and class.
• We have used a total of 5 features to train our machine learning model.

15. FEATURE ENGINEERING
• Feature engineering is the process of using domain
knowledge to extract features from raw data via data
mining techniques. These features can be used to
improve the performance of machine learning algorithms.
Feature engineering can be considered as applied
machine learning itself.
• Dimensionality reduction techniques are used in our
dataset to reduce those rows who are not very much
important to decide the house price.

16. MODEL CREATION
• The process of modeling means training a machine
learning algorithm to predict the labels from the features.
• We have used Logistic Regression and Random Forest
Classifier algorithms for training and testing of the model.
• The accuracy rate of our models is 98% which is Excellent.

17. RESULT/OUTPUT
• We created a function to predict the house price.
• Our function be like “ Prediction(news/text) ”.
• When we pass the news text into our function, it will predict
that news is fake or not.

18. Message

19. THANK YOU