Made all the asked changes for Fake News

Fake-News-Classification/Dataset/README.md

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+# Fake News Classification using DL 
+
+## PROJECT TITLE
+
+Fake News Detection using Deep Learning 
+
+## GOAL
+
+To identify whether the given news is fake or not. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/competitions/fake-news/data?select=train.csv 
+
+
+## DESCRIPTION
+
+This project aims to identify whether the given news is fake or not by extracting meaning and semantics of the given news.
+
+## WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above. 
+2. Data preprocessing: Preprocessed the news by combining title and text to create a new feature and did some augementation like tokeinizing and vectorising before passing them to model training
+3. Model selection: Self Designed model having a Embedding Layer followed by Global Pooling Layer and then 2 Dense layers and then output layer.Second model had a Embedding layer followed by a RNN layer and a Dense output layer.
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+## MODELS SUMMARY
+
+Model-1: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ embedding (Embedding)       (None, 12140, 182)        30222010  
+
+ global_average_pooling1d (  (None, 182)               0         
+ GlobalAveragePooling1D)                                         
+
+ dense (Dense)               (None, 96)                17568     
+
+ dense_1 (Dense)             (None, 24)                2328      
+
+ dense_2 (Dense)             (None, 1)                 25        
+
+=================================================================
+Total params: 30241931 (115.36 MB)
+Trainable params: 30241931 (115.36 MB)
+Non-trainable params: 0 (0.00 Byte)
+
+Model-2: "sequential_3"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ embedding_3 (Embedding)     (None, 12140, 100)        16605500  
+
+ simple_rnn (SimpleRNN)      (None, 10)                1110      
+
+ dense_5 (Dense)             (None, 1)                 11        
+
+=================================================================
+Total params: 16606621 (63.35 MB)
+Trainable params: 16606621 (63.35 MB)
+Non-trainable params: 0 (0.00 Byte)
+
+## LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- nltk
+- pandas
+- matplotlib
+- tensorflow
+- keras
+- sklearn
+
+## EVALUATION METRICS
+
+The evaluation metrics I used to assess the models:
+
+- Accuracy 
+- Loss
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+For Model-1:
+Accuracy:96.11%
+loss: 0.1350
+
+For Model-2:
+Accuracy:85.03%
+loss: 0.1439
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The model-1 showed high validation accuracy of 96.11% and loss of 0.1350.Thus the model-1 worked fairly well identifying 2874 fake articles from a total of 3044.The first model performed better.The second model had good training accuracy but less test accuracy hinting towards overfitting.Maybe the key reason being in fake news it is important to capture overall sentiment better than individual word sentiment.