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| import streamlit as st | |
| import pandas as pd | |
| import os | |
| import re | |
| import preprocessor as p | |
| import joblib | |
| import base64 | |
| project_description = """ | |
| # Hotel Data Analysis Project | |
| ## Overview | |
| I have completed a hotel data analysis project using an instant web scraper. | |
| This project involved scraping hotel data and hotel reviews separately, cleaning the data, | |
| concatenating it, and performing sentiment analysis on the DataFrame. | |
| Additionally, I clustered the hotel reviews, applied sentiment analysis, and passed | |
| those clusters to an LLM (Language Model) to extract strengths and weaknesses of hotels. | |
| ## Steps | |
| ### 1. Scraping Hotel Data | |
| - Utilized an instant web scraper to collect hotel data. | |
| - Scraped hotel data separately from hotel reviews. | |
| ### 2. Data Collection | |
| - Collected hotel data and hotel reviews data separately for each hotel. | |
| ### 3. Data Cleaning | |
| - Cleaned the collected data to remove any inconsistencies or errors. | |
| - Applied preprocessing techniques to prepare the data for analysis. | |
| ### 4. Data Concatenation | |
| - Concatenated the cleaned hotel data and hotel reviews data to create a unified dataset for analysis. | |
| ### 5. Sentiment Analysis | |
| - Performed sentiment analysis on the concatenated DataFrame. | |
| - Utilized the results to understand the overall sentiment of hotel reviews. | |
| ### 6. Clustering Hotel Reviews | |
| - Clustered the hotel reviews based on their content to identify patterns and similarities. | |
| ### 7. Extracting Strengths and Weaknesses | |
| - Passed the clustered reviews to an LLM (Language Model) to extract strengths and weaknesses of hotels. | |
| - Used the extracted information to gain insights into customer perceptions. | |
| ## Conclusion | |
| This project demonstrates the use of web scraping, data cleaning, sentiment analysis, and clustering techniques to analyze hotel data. | |
| The extracted strengths and weaknesses provide valuable insights for hotel management to improve customer satisfaction and service quality. | |
| """ | |
| def create_download_link(df, filename): | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() | |
| href = f'<a href="data:file/csv;base64,{b64}" download="{filename}.csv">Download CSV file</a>' | |
| return href | |
| # Path to the directory containing CSV files | |
| directory_path = r'hotel reviews' | |
| # Get a list of CSV files in the directory | |
| csv_files = [file for file in os.listdir(directory_path) if file.endswith('.csv')] | |
| # Function to concatenate selected columns | |
| def concatenate_columns(df, selected_columns): | |
| concatenated_data = df[selected_columns[0]].tolist() + df[selected_columns[1]].tolist() | |
| return pd.DataFrame({'ConcatenatedData': concatenated_data}) | |
| # Function to display selected dataset | |
| def display_selected_dataset(selected_dataset): | |
| dataset_path = os.path.join(directory_path, selected_dataset) | |
| selected_df = pd.read_csv(dataset_path) | |
| st.subheader(f'Dataset: {selected_dataset}') | |
| st.write(selected_df) | |
| def clean_tweets(series): | |
| REPLACE_NO_SPACE = re.compile("[.;:!\'?,\"()\[\]]") | |
| REPLACE_WITH_SPACE = re.compile("(<br\s*/><br\s*/>)|(\-)|(\/)") | |
| tempArr = [] | |
| for line in series: | |
| # Check if the value is NaN | |
| if pd.isnull(line): | |
| tempArr.append("") | |
| continue | |
| # Send to tweet_processor | |
| tmpL = p.clean(line) | |
| # Remove punctuation | |
| tmpL = REPLACE_NO_SPACE.sub("", tmpL.lower()) | |
| # Replace specific characters with spaces | |
| tmpL = REPLACE_WITH_SPACE.sub(" ", tmpL) | |
| # Remove extra spaces | |
| tmpL = " ".join(tmpL.split()) | |
| tempArr.append(tmpL) | |
| return tempArr | |
| # Streamlit app | |
| def main(): | |
| # Create a menu bar | |
| menu = st.sidebar.selectbox( | |
| 'Navigation', | |
| ['Home', 'collected hotel data', 'Display Hotel Data', 'Display hotel reviews Datasets'] | |
| ) | |
| if menu == 'Home': | |
| st.markdown(project_description) | |
| elif menu == 'collected hotel data': | |
| # Display DataFrame | |
| df = pd.read_csv('chennai hotes.csv') | |
| df1 = pd.read_csv('stream.csv') | |
| st.subheader('Collected chennai hotes Data') | |
| st.write(df) | |
| st.subheader('preprocess applyed data') | |
| st.write(df1) | |
| elif menu == 'Display Hotel Data': | |
| # Display hotel data | |
| df = pd.read_csv('stream.csv') | |
| css = """ | |
| <style> | |
| .hotel-container { | |
| border: 1px solid #ddd; | |
| border-radius: 5px; | |
| padding: 10px; | |
| margin-bottom: 20px; | |
| } | |
| .hotel-image { | |
| max-width: 100%; | |
| border-radius: 5px; | |
| margin-bottom: 10px; | |
| } | |
| .hotel-details { | |
| font-size: 16px; | |
| } | |
| </style> | |
| """ | |
| st.markdown(css, unsafe_allow_html=True) | |
| for index, row in df.iterrows(): | |
| st.markdown(f""" | |
| <div class="hotel-container"> | |
| <img class="hotel-image" src="{row['hotel image']}"> | |
| <div class="hotel-details"> | |
| <h2>{row['Hotel Name']}</h2> | |
| <p><strong>Rating:</strong> {row['rating']}</p> | |
| <p><strong>Location:</strong> {row['location']} ({row['nearest places']})</p> | |
| <p><strong>Website:</strong> <a href="{row['hotel website']}">Website link</a></p> | |
| <p><strong>Number of Reviews:</strong> {row['number of reviewss 2']}</p> | |
| <p><strong>Room Type:</strong> {row['room type']}</p> | |
| <p><strong>Price:</strong> {row['price']}</p> | |
| <p><strong>Strengths:</strong> {row['Strengths']}</p> | |
| <p><strong>Weaknesses:</strong> {row['Weaknesses']}</p> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| elif menu == 'Display hotel reviews Datasets': | |
| selected_dataset = st.selectbox('Select Dataset', csv_files) | |
| if selected_dataset: | |
| display_selected_dataset(selected_dataset) | |
| elif menu == 'CSV Column Concatenation and Sentiment Analysis': | |
| st.title('CSV Column Concatenation and Sentiment Analysis') | |
| new_names = { | |
| 'a3332d346a': 'Reviewer Name', | |
| 'afac1f68d9': 'Reviewer Country', | |
| 'abf093bdfe': 'Room Type', | |
| 'abf093bdfe 2': 'Length of Stay', | |
| 'abf093bdfe 3': 'Review Date', | |
| 'abf093bdfe 4': 'Traveler Type', | |
| 'abf093bdfe 5': 'Second Review Date', | |
| 'f6431b446c': 'Overall Rating', | |
| 'a53cbfa6de': 'Positive Comments', | |
| 'a53cbfa6de 2': 'Negative Comments', | |
| 'a3332d346a 2': 'Hotel Response', | |
| 'a53cbfa6de 3': 'Hotel Response1' | |
| } | |
| # File upload | |
| uploaded_file = st.file_uploader('Upload CSV file', type=['csv']) | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file) | |
| df.rename(columns=new_names, inplace=True) | |
| # Show original DataFrame | |
| st.subheader('Original DataFrame:') | |
| st.write(df) | |
| # Select columns | |
| selected_columns = st.multiselect('Select columns to concatenate', df.columns) | |
| if st.button('Concatenate columns'): | |
| if len(selected_columns) == 2: | |
| # Concatenate columns | |
| new_df = concatenate_columns(df, selected_columns) | |
| # Remove null values | |
| new_df = new_df.dropna() | |
| # Drop duplicates | |
| new_df = new_df.drop_duplicates() | |
| # Reset the index | |
| new_df = new_df.reset_index(drop=True) | |
| # Clean tweets | |
| new_df['CleanedData'] = clean_tweets(new_df['ConcatenatedData']) | |
| # Load the saved model | |
| loaded_model = joblib.load('sentiment_analysis_model.pkl') | |
| # Apply sentiment analysis | |
| new_df['Sentiment'] = loaded_model.predict(new_df['CleanedData']) | |
| # Display concatenated, cleaned, and sentiment analyzed DataFrame | |
| st.subheader('Concatenated, Cleaned, and Sentiment Analyzed DataFrame:') | |
| st.write(new_df) | |
| # Create download link | |
| st.markdown(create_download_link(new_df, 'concatenated_sentiment_analyzed_data'), unsafe_allow_html=True) | |
| else: | |
| st.warning('Please select exactly two columns to concatenate.') | |
| # Run the app | |
| if __name__ == '__main__': | |
| main() | |