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update dataset now 50000 data,model,eda add more analysis
Browse files- best_model.h5 +2 -2
- dataset_20000_rows.csv β dataset_50000_rows.csv +0 -0
- eda.py +6 -2
- t.pickle +2 -2
best_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:f619954d49dfbaf511d493e8da74073896a5f10142fb52f2bf5fb61669946cbb
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size 77868880
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dataset_20000_rows.csv β dataset_50000_rows.csv
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eda.py
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@@ -35,7 +35,7 @@ def run():
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st.write('Dataset used is amazon review dataset from [kaggle]("https://www.kaggle.com/datasets/yacharki/amazon-reviews-for-sa-binary-negative-positive-csv").')
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# show dataframe
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df2 = pd.read_csv('
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df2 = df2.drop(['Unnamed: 0'],axis=1)
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st.dataframe(df2)
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# add description of Dataset
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fig = px.pie(review,values='class_index', names='index',color_discrete_sequence=['red','blue'])
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fig.update_layout(title_text = "Type of Review")
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st.plotly_chart(fig)
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st.write('Based on the table and visualization above, it can be seen that both negative and positive reviews consist of
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elif select_eda == 'Example of Positive and Negative Review':
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# Print sample reviews
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pd.set_option('display.width', None)
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for i in range(0,20):
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st.write(sample_positive_review.iloc[i,2])
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st.write('-'*100)
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elif select_eda == 'Number of Words':
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# Count the number of words in each review
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st.write('Dataset used is amazon review dataset from [kaggle]("https://www.kaggle.com/datasets/yacharki/amazon-reviews-for-sa-binary-negative-positive-csv").')
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# show dataframe
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df2 = pd.read_csv('dataset_50000_rows.csv')
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df2 = df2.drop(['Unnamed: 0'],axis=1)
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st.dataframe(df2)
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# add description of Dataset
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fig = px.pie(review,values='class_index', names='index',color_discrete_sequence=['red','blue'])
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fig.update_layout(title_text = "Type of Review")
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st.plotly_chart(fig)
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st.write('Based on the table and visualization above, it can be seen that both negative and positive reviews consist of 25,000 reviews each.')
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elif select_eda == 'Example of Positive and Negative Review':
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# Print sample reviews
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pd.set_option('display.width', None)
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for i in range(0,20):
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st.write(sample_positive_review.iloc[i,2])
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st.write('-'*100)
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st.write('Based on the examples of various reviews above, what distinguishes between positive reviews and negative reviews :')
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st.write('1. **Positive reviews** generally express satisfaction with a product, service, or experience, highlighting the positive aspects and benefits that were experienced. They often use positive language and may include specific examples of what the reviewer liked about the product or service. They may also mention the quality, value for money, or ease of use.')
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st.write('2. **Negative reviews**, on the other hand, typically express dissatisfaction or disappointment with a product, service, or experience. They often highlight specific problems or issues that the reviewer experienced, such as poor quality, bad customer service, or difficulty using the product. Negative reviews may use negative language and may also include suggestions for how the product or service could be improved.')
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st.write('Overall, the key difference between positive and negative reviews is the attitude of the reviewer and their overall satisfaction with the product or service. Positive reviews reflect a positive experience, while negative reviews reflect a negative experience.')
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elif select_eda == 'Number of Words':
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# Count the number of words in each review
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t.pickle
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:942c8fa939686e4632a7964cc2a9e222db65c203a0eaed24032a6a6438751426
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size 2062767
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