Advanced Python Project – Detecting Fake News with Python

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Advanced Python Project – Detecting Fake News

Do you trust all the news you hear from social media? All news are not real, right? So how will you detect the fake news? The answer is Python. By practicing this advanced python project of detecting fake news, you will easily make a difference between real and fake news. Before moving ahead in this advanced Python project, get aware of the terms related to it like fake news, tfidfvectorizer, PassiveAggressive Classifier.

Also, I like to add that DataFlair has published a series of Python Projects where you will get interesting and open-source advanced python projects. Do check, and then share your experience through comments. Here is the list of top Python projects:

  1. Fake News Detection Python Project
  2. Parkinson’s Disease Detection Python Project
  3. Color Detection Python Project
  4. Speech Emotion Recognition Python Project 
  5. Breast Cancer Classification Python Project
  6. Age and Gender Detection Python Project 
  7. Handwritten Digit Recognition Python Project
  8. Chatbot Python Project
  9. Driver Drowsiness Detection Python Project
  10. Traffic Signs Recognition Python Project
  11. Image Caption Generator Python Project

What is Fake News?

A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end up being viralized by algorithms, and users may end up in a filter bubble.

What is a TfidfVectorizer?

TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.

IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.

The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.

What is a PassiveAggressiveClassifier?

Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector.

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Detecting Fake News with Python – Objective

To build a model to accurately classify a piece of news as REAL or FAKE.

Detecting Fake News with Python – About the Python Project

This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.


The Dataset

The dataset we’ll use for this python project- we’ll call it news.csv. This dataset has a shape of 7796×4. The first column identifies the news, the second and third are the title and text, and the fourth column has labels denoting whether the news is REAL or FAKE. The dataset takes up 29.2MB of space and you can download it here.

Advanced Python Project – Prerequisites

You’ll need to install the following libraries with pip:

pip install numpy pandas sklearn

You’ll need to install Jupyter Lab to run your code. Get to your command prompt and run the following command:

C:\Users\DataFlair>jupyter lab

You’ll see a new browser window open up; create a new console and use it to run your code. To run multiple lines of code at once, press Shift+Enter.

Steps for detecting fake news with Python

Follow the below steps for detecting fake news and complete your first advanced Python Project –

  1. Make necessary imports:
import numpy as np
import pandas as pd
import itertools
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.metrics import accuracy_score, confusion_matrix


importing data sets in python open source projects

2. Now, let’s read the data into a DataFrame, and get the shape of the data and the first 5 records.

#Read the data

#Get shape and head

Output Screenshot:

interesting python projects - read data frame


Check frequently asked Python Interview Questions with Answers

3. And get the labels from the DataFrame.

#DataFlair - Get the labels

Output Screenshot:

Python projects examples - get labels

4. Split the dataset into training and testing sets.

#DataFlair - Split the dataset
x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)


Python data science projects - split data sets

5. Let’s initialize a TfidfVectorizer with stop words from the English language and a maximum document frequency of 0.7 (terms with a higher document frequency will be discarded). Stop words are the most common words in a language that are to be filtered out before processing the natural language data. And a TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features.

Now, fit and transform the vectorizer on the train set, and transform the vectorizer on the test set.

#DataFlair - Initialize a TfidfVectorizer
tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)

#DataFlair - Fit and transform train set, transform test set


python data science projects

6. Next, we’ll initialize a PassiveAggressiveClassifier. This is. We’ll fit this on tfidf_train and y_train.

Then, we’ll predict on the test set from the TfidfVectorizer and calculate the accuracy with accuracy_score() from sklearn.metrics.

#DataFlair - Initialize a PassiveAggressiveClassifier

#DataFlair - Predict on the test set and calculate accuracy
print(f'Accuracy: {round(score*100,2)}%')

Output Screenshot:

python machine learning projects

7. We got an accuracy of 92.82% with this model. Finally, let’s print out a confusion matrix to gain insight into the number of false and true negatives and positives.

#DataFlair - Build confusion matrix
confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])

Output Screenshot:

python projects - confusion matrix

So with this model, we have 589 true positives, 587 true negatives, 42 false positives, and 49 false negatives.


Today, we learned to detect fake news with Python. We took a political dataset, implemented a TfidfVectorizer, initialized a PassiveAggressiveClassifier, and fit our model. We ended up obtaining an accuracy of 92.82% in magnitude.

Hope you enjoyed our first advanced level Python Project. Keep visiting DataFlair for more interesting Python data science and machine learning projects.

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68 Responses

  1. Bijal says:

    From where I get the project of “Age and Gender detection in python”?

  2. prabhat kumar says:

    not abke to download datasets of fake news detecting projects.

  3. abhi says:

    using which model is it made?

  4. Prabhat Kumar says:

    How can i access Drive file? While opening it showing error in loading file. While i was trying to practice detecting fake news with python project.

  5. Al says:

    When I try to run your code, I get a memory error:

    —> 35, y_train)

    Please can you explain what’s going on?

  6. Maanik Garg says:

    The Label Column is already segregating the news as REAL or FAKE then why are we making this model of detecting the news as real or fake

    • DataFlair Team says:

      Suppose you want to automate the task of identifying news as Real or Fake. For this purpose, how will you create the model?
      What we are doing here is that feeding the model with a dataset.

      It contains the news and their labels. The model will look for patterns using the algorithm and adjust the weights on the model. Now our model can predict news as Real or Fake.

      Yes, we already knew that, but what about new data? We can use the model to predict new news that can classify them. This is what machine learning is about.

      Note: The model’s accuracy in real-world depends on the dataset. We need a large amount of labeled data to make good models.

  7. dogukan says:

    I can’t run the code. will the output look like? Would you add it here. Is there also a github link?

  8. Jayanthan says:

    Thats why it is called as supervising learning it means the labels are already known .The aim of this algorithm is to train the data and to predict the model.

  9. abhigyan kumar says:

    I liked this Website so much.

  10. sabiya says:

    thanks it helped me alot

  11. Alfred joy says:

    how can we give new data ..which is a current news and show its output like is fake or it can we give new news article and say that it is fake or not!!

  12. gaurav says:

    thanks a lot .

  13. Neronemesis says:

    Hi , how can we adapt this code to detect online fake job ? I need help for it please.

  14. sanjana says:

    from where i should get source code for fake news detection

    • DataFlair Team says:

      Hello Sanjana,

      The full source code is available in this blog. Just follow along and go through each of the steps individually.

  15. hassan says:

    I used your model to predict latest news and it shows fake.

    • DataFlair Team says:

      Hey Hassan,

      The model will usually work good on data that is similar to the dataset. As the dataset used for this model is small. For real world application we need to train our model on very huge datasets.

  16. Bryan McCormack says:

    I understand the need to create a model to test new bits of data, but I’m having trouble actually using the model to test a new piece of news. How would I test a headline using the model to actually make a real-world prediction?

  17. ajaykrishnan s says:

    iam looking for a project such as a fake news detection that does and detect automatically and views a popup like output .ie it take news from online using a web crawler and then it takes news from some trusted sources and then compare them all to produce whether the news is fake or true .can u help me out?

  18. Daniel Onehi says:

    Thanks! This is insightful but how can I finish it and use it on current news? Simply put How can I deploy it

  19. Titi says:


    The algorithm stopped working when I got to the train/split step.

    It returns this error

    NameError Traceback (most recent call last)
    —-> 1 x_train,x_test,y_train,y_test=train_test_split(df[‘text’], labels, test_size=0.2, random_state=7)

    NameError: name ‘labels’ is not defined

    What does it mean for ‘L’ not to be defined?

    I’d appreciate prompt assists.

  20. Titi says:

    ‘labels’ is not defined I mean

  21. Kanwal says:

    how to classify that news is fake or real writing like this:
    PAC.predict([‘ Obama is running for president in 2016’])

  22. shruti Sharma says:

    How can I check if this working correct or not ? ps : It was very helpful.

  23. RAJKUMAR GAALI says:

    i think fake news detection is not good idea

  24. Vidhya J Menon says:

    Is there any paper regarding this topic??

  25. Srilahari says:

    How can I check for a new sentence whether it is fake or real news

  26. prachi says:

    I want to ask that why you have taken only text column as feature in train_test_split ?????we also could take title along with this.
    Please explain i am not getting this point.

  27. prachi says:

    Could you please provide any relevant information regarding concept of passive aggressive classifier algorithm??

  28. puneet singh says:

    i am getting keyerror : text
    how to get rid of this though i have done everything as explained.
    reply needed urgently!!!!!!

  29. puneet singh says:

    on splitting data sets into training and testing
    i am getting a keyerror : test when using df[‘text’]
    reply needed asap

  30. puneet singh says:

    while using dataframe like df[‘text’]
    i am getting keyerror : text
    althoug doing everything as explained

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