Wine Reviews Classification

Wine Reviews Classification/Dataset/README.md

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+# Wine Reviews Classification using DL 
+
+## PROJECT TITLE
+
+Wine Reviews Classification using Deep Learning 
+
+## GOAL
+
+To classify the quality of wine based on reviews. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/datasets/zynicide/wine-reviews 
+
+## EDA
+Shape of Dataset:(129971, 14)
+![Dataset](../Images/Input_Dataset.png)
+![EDA](../Images/EDA3.png)
+
+## DESCRIPTION
+
+This project aims to identify the quality points of wine based upon its reviews.
+
+## 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: "sequential"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ embedding (Embedding)       (None, 89, 200)           12794200  
+
+ global_average_pooling1d (  (None, 200)               0         
+ GlobalAveragePooling1D)                                         
+
+ dense (Dense)               (None, 100)               20100     
+
+ dense_1 (Dense)             (None, 50)                5050      
+
+ dense_2 (Dense)             (None, 21)                1071      
+
+=================================================================
+Total params: 12820421 (48.91 MB)
+Trainable params: 12820421 (48.91 MB)
+Non-trainable params: 0 (0.00 Byte)
+
+Model-2: "sequential_1"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ embedding_1 (Embedding)     (None, 89, 100)           6397100   
+
+ simple_rnn (SimpleRNN)      (None, 30)                3930      
+
+ dense_3 (Dense)             (None, 21)                651       
+
+=================================================================
+Total params: 6401681 (24.42 MB)
+Trainable params: 6401681 (24.42 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:
+
+- Loss 
+- Accuracy
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+For Model-1:
+Accuracy:31%
+loss: 3.1
+
+For Model-2:
+Accuracy:9%
+loss:8.05
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The model-1 performed better than model 2.