Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -48,57 +48,13 @@ st.markdown(
 # Title of the Streamlit app
 st.title("Exploratory Data Analysis (EDA) on Agoda Hotel Dataset")
 
-# Introduction and Aim
-st.header("Aim of the EDA")
-st.write("""
-    The main objective of this EDA is to analyze Agoda's hotel dataset to identify key factors influencing hotel pricing strategies and customer booking preferences. 
-    The analysis will focus on uncovering patterns, trends, and relationships in hotel ratings, pricing structures, discounts, and free services.
-    By leveraging these insights, Agoda can optimize its pricing strategy, predict booking preferences, and enhance revenue generation while maintaining customer satisfaction.
+st.markdown("""
+This page provides advanced Exploratory Data Analysis (EDA) and Feature Engineering using the dataset loaded in memory.
+---
 """)
 
-# Description of the Data
-st.header("Description of the Data")
-st.write("""
-    **Overall Summary:** We are analyzing the Agoda dataset by performing EDA and Statistical Tests on the data that has already been cleaned through data wrangling to address any messiness or missing information.
-    
-    **Table - Agoda_df:** The cleaned dataset consists of over 3,500 hotel listings, which will be used as test subjects for the hotel pricing period.
-    **Dataset Details:**
-    The dataset contains information about 3,219 hotel room listings with 12 features, each detailing aspects of the listing. Below is the description of each column:
-    | Column Name     | Description                                                               |
-    |-----------------|---------------------------------------------------------------------------|
-    | hotel_name      | Name of the hotel.                                                        |
-    | rating          | Average customer rating of the hotel (float, range 1-5).                   |
-    | location        | Address or locality of the hotel.                                          |
-    | review_text     | Customer feedback or comments about the hotel.                             |
-    | reviews         | Total number of customer reviews for the hotel.                            |
-    | cashback        | Cashback amount offered for the booking.                                  |
-    | discount        | Discount percentage applied to the room price.                             |
-    | free_services   | Free services provided (e.g., breakfast, Wi-Fi).                          |
-    | cancellation    | Cancellation policy for the booking (e.g., free, non-refundable).         |
-    | price           | Price of the room after discounts and cashback (float).                    |
-    | state           | The state where the hotel is located.                                      |
-    | category        | Target variable representing the room type or category (e.g., budget, luxury). |
-""")
-
-# Table-wise EDA & Necessary Tests
-st.header("Table-wise EDA and Necessary Statistical Tests")
-st.write("""
-    **Agoda_df:** Cleaned dataset with hotel details and key features like ratings, price, reviews, cashback, discounts, and free services.
-    The EDA will involve the following steps:
-    - **Summary Statistics:** Analyze the central tendency, spread, and shape of the distribution of each feature.
-    - **Data Distribution:** Visualize the distribution of key features like price, ratings, reviews, cashback, etc.
-    - **Correlation Analysis:** Analyze relationships between numeric features like price, ratings, reviews, cashback, etc.
-    - **Categorical Data Analysis:** Explore categorical variables like hotel category, cancellation policy, state, and location using frequency tables and visualizations.
-    - **Missing Value Analysis:** Ensure no missing values remain, and check the need for imputations.
-    - **Outlier Detection:** Identify any outliers that may skew the analysis or predictions.
-    - **Statistical Tests:** Apply appropriate statistical tests to identify significant differences or relationships (e.g., t-tests for comparing means, chi-squared for categorical variables).
-""")
-
-# Placeholder for further detailed code or visualizations
-st.write("Further steps will include generating visualizations and statistical tests to explore relationships between features in more detail.")
-
 # Access dataset from session state
-data= st.session_state.get("dataset")
+df= st.session_state.get("dataset")
 
 if data is not None:
     df = st.session_state['df']