Rename pages/4_EDA and Feature Engineering.py to pages/3_EDA and Feature Engineering.py

pages/3_EDA and Feature Engineering.py

@@ -0,0 +1,574 @@
+import streamlit as st 
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import StringIO
+import sys
+
+st.markdown("<h1 style='text-align:center; color:lime;'>EDA and Feature Engineering</h1>",unsafe_allow_html=True)
+
+
+# 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.
+""")
+
+# 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")
+
+if data is not None:
+    st.subheader("Dataset Preview:")
+    st.write(data)  # Display the first 5 rows
+    
+    
+    st.subheader("Info of the Dataset:")
+    # Redirect the output of df.info() to a string buffer
+    buffer = StringIO()
+    data.info(buf=buffer)
+    
+    # Display the content in Streamlit
+    st.write(buffer.getvalue())
+    
+    st.subheader("Dataset Statistics:")
+    st.write(data.describe())
+    
+    st.subheader("Dataset Shape (Rows, Columns):")
+    st.write(data.shape)
+
+    data= data[data["price"] <= 40000]  # Keep rows where price is less than or equal to 40,000
+    
+    pages = st.sidebar.selectbox(
+    ["Univariate Analysis", "Bivariate Analysis", "Multivariate Analysis"]
+)
+### univariate_analysis.
+    st.success("Dataset successfully loaded from session state!")
+    ii page='Univariate Analysis':
+        st.subheader("Univariate Analysis")
+        
+        # Rating and Review Text Distribution
+        st.subheader("Rating and Review Text Distribution")
+        fig, axs = plt.subplots(1, 2, figsize=(16, 6))
+    
+        data["rating"].value_counts().plot(kind='pie', title='Distribution of Ratings', autopct='%1.1f%%', shadow=True, startangle=45, textprops={'size': 'x-large'}, ax=axs[0])
+        axs[0].set_title("Distribution of Ratings")
+    
+        data['review_text'].value_counts().plot(kind='pie', title='Distribution of Review Text', autopct='%1.1f%%', shadow=True, startangle=45, textprops={'size': 'x-large'}, ax=axs[1])
+        axs[1].set_title("Distribution of Review Text")
+    
+        st.pyplot(fig)
+    
+        # Hotel Star Insights
+        st.write("""
+            **Insight:**
+            - Majority of hotels in this data are 3-star hotels.
+            - Frequency of 4-star and 5-star hotels are also moderately good.
+            - 1-star and 2-star hotels are lower in frequency.
+        """)
+    
+        # Price, Cashback, and Discount Distribution
+        st.subheader("Price, Cashback, and Discount Distribution")
+        fig, axs = plt.subplots(1, 3, figsize=(16, 6))
+    
+        sns.histplot(data=data, x='price', color='green', kde=True, ax=axs[0])
+        axs[0].set_title("Count based on price")
+        axs[0].set_xlabel('Price')
+        axs[0].set_ylabel('Number of People')
+    
+        sns.histplot(data=data, x='cashback', color='violet', kde=True, ax=axs[1])
+        axs[1].set_title("Count based on cashback")
+        axs[1].set_xlabel('Cashback')
+        axs[1].set_ylabel('Number of People')
+    
+        sns.histplot(data=data, x='discount', color='orange', kde=True, ax=axs[2])
+        axs[2].set_title("Count based on discount")
+        axs[2].set_xlabel('Discount')
+    
+        st.pyplot(fig)
+          # Histogram Insights
+        st.subheader("Plot-wise Analysis of Histograms")
+        st.write("""
+            **Price Distribution Insight:**
+            - The histogram is right-skewed, showing most properties are in the lower price range.
+            - A long tail indicates the presence of a few very expensive properties.
+            
+            **Cashback Distribution Insight:**
+            - The histogram is right-skewed, with the majority of properties offering lower cashback amounts.
+            - Only a small number of properties provide higher cashback.
+            
+            **Discount Distribution Insight:**
+            - The histogram is right-skewed, indicating that most properties offer lower discount percentages.
+            - A few properties stand out with higher discounts.
+            
+            **Summary:**
+            The data suggest that Agoda properties are generally affordable, with lower cashback and discount offers being common. Further statistical analysis could help uncover more detailed insights.
+        """)
+    
+        # Cancellation and State Distribution
+        st.subheader("Cancellation and State Distribution")
+        fig, axs = plt.subplots(1, 2, figsize=(16, 6))
+    
+        data["cancellation"].value_counts().plot(kind='bar', title='Distribution of Cancellation', color='red', ax=axs[0])
+        axs[0].set_title("Distribution of Cancellation")
+        axs[0].set_xlabel('Cancellation')
+        axs[0].set_ylabel('Number of Hotels')
+    
+        data["state"].value_counts().plot(kind='bar', title='Distribution of State', color='black', ax=axs[1])
+        axs[1].set_title("Distribution of State")
+        axs[1].set_xlabel('State')
+        axs[1].set_ylabel('Number of Hotels')
+    
+        st.pyplot(fig)
+        # Bar Chart Insights
+        st.subheader("Plot Wise Analysis of Bar Charts")
+        st.write("""
+            **Cancellations:**
+            - Most cancellations fall under category "1," indicating they occur within specific conditions or timeframes.
+            
+            **State Distribution:**
+            - "Maharashtra" has the highest number of hotels, followed by "Madhya Pradesh."
+            - Other states like Gujarat, Karnataka, and Kerala also have notable hotel counts.
+            - The distribution is uneven, with some states having significantly more hotels.
+            
+            **Summary:**
+            The charts highlight cancellation trends and the regional hotel distribution in India.
+        """)
+    
+        # Category and Reviews Distribution
+        st.subheader("Category and Reviews Distribution")
+        fig, axs = plt.subplots(1, 2, figsize=(16, 6))
+    
+        colors = sns.color_palette('Set2', n_colors=len(data["category"].value_counts()))
+        data["category"].value_counts().plot(kind='bar', ax=axs[0], color=colors)
+        axs[0].set_title("Distribution of Category")
+        axs[0].set_xlabel('Category')
+        axs[0].set_ylabel('Number of Hotels')
+    
+        sns.histplot(data=data, x='reviews', color='violet', kde=True, ax=axs[1])
+        axs[1].set_title("Count based on Reviews")
+        axs[1].set_xlabel('Reviews')
+        axs[1].set_ylabel('Number of Reviews')
+    
+        st.pyplot(fig)
+        # Hotel Categories and Reviews Insights
+        st.subheader("Plot Wise Analysis of Hotel Categories and Reviews")
+        st.write("""
+            **Category Distribution:**
+            - The histogram shows "Low Budget" hotels are the most common, followed by "Budget Hotels," while "Luxury Hotels" are the least common.
+            
+            **Review Count Distribution:**
+            - The histogram is right-skewed, with most hotels having a low number of reviews.
+            - A few hotels have a very high number of reviews, evident from the long tail.
+            
+            **Summary:**
+            The data indicates a higher concentration of low-budget hotels and relatively low review counts for most hotels.
+        """)
+    
+        # Top 10 Amenities
+        st.subheader("Top 10 Amenities")
+        amenity_counts = data['free_services'].str.split(',').explode().str.strip().value_counts().reset_index()
+        amenity_counts.columns = ['Amenity', 'Count']
+    
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.barplot(x='Count', y='Amenity', data=amenity_counts.head(10), palette='viridis', ax=ax)
+        ax.set_title('Top 10 Amenities')
+        ax.set_xlabel('Number of Hotels Offering')
+        ax.set_ylabel('Amenity')
+    
+        st.pyplot(fig)
+        # Top Amenities Insights
+        st.subheader("Plot Wise Analysis of Top Amenities")
+        st.write("""
+            **Common Amenities:**
+            - Complimentary Parking is the most frequently offered amenity.
+            - Basic Toiletries and Hair Dryers are also widely available.
+            
+            **Less Common Amenities:**
+            - Fitness Center Access, Welcome Drinks, and Turndown Service are less common.
+            - Shoe Shine Service is the least frequently offered amenity.
+            
+            **Summary:**
+            Hotels tend to prioritize basic amenities like parking, toiletries, and hair dryers, while luxurious amenities are offered less frequently.
+        """)
+    if pages="Bivariate Analysis":
+        # Streamlit app
+        st.title("Bivariate Analysis")
+        
+        # Price vs Rating scatter plot
+        st.subheader("Price vs Rating")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.scatterplot(x='rating', y='price', data=data, color='orange')
+        ax.set_title('Price vs Rating')
+        ax.set_xlabel('Rating')
+        ax.set_ylabel('Price')
+        st.pyplot(fig)
+        
+        # Price vs Rating
+        st.subheader("Price vs Rating:")
+        st.write("""
+        - **Analysis:**
+          - Higher-priced hotels slightly tend to have better ratings, but ratings vary widely across price points.
+          - Hotels at various price points exhibit a large spread in ratings, meaning factors other than price (such as customer experience or amenities) contribute significantly to the rating.
+        """)
+        
+        # Price vs Discount scatter plot
+        st.subheader("Price vs Discount")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.scatterplot(x='discount', y='price', data=data, color='green')
+        ax.set_title('Price vs Discount')
+        ax.set_xlabel('Discount')
+        ax.set_ylabel('Price')
+        st.pyplot(fig)
+        # Price vs Discount
+        st.subheader("Price vs Discount:")
+        st.write("""
+        - **Analysis:**
+          - Some high-priced hotels still provide discounts due to promotions or special deals.
+          - This observation suggests that while premium hotels may not always need to offer discounts to attract customers, they occasionally use them as a marketing strategy or for seasonal promotions.
+        """)
+        
+        # Price vs Cashback scatter plot
+        st.subheader("Price vs Cashback")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.scatterplot(x='cashback', y='price', data=data, color='blue')
+        ax.set_title('Price vs Cashback')
+        ax.set_xlabel('Cashback')
+        ax.set_ylabel('Price')
+        st.pyplot(fig)
+        
+        # Price vs Cashback
+        st.subheader("Price vs Cashback:")
+        st.write("""
+        - **Analysis:**
+          - Exceptions exist due to promotional campaigns.
+          - While higher-priced hotels generally offer fewer cashback incentives, some may offer cashback due to specific promotional campaigns aimed at increasing sales volume or attracting customers in a competitive market.
+        """)
+        
+        # Price vs Category bar plot
+        st.subheader("Price vs Category")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.barplot(x='category', y='price', data=data, palette='Set2')
+        ax.set_title('Price vs Category')
+        ax.set_xlabel('Category')
+        ax.set_ylabel('Price')
+        st.pyplot(fig)
+        # Price vs Category
+        st.subheader("Price vs Category:")
+        st.write("""
+        - **Analysis:**
+          - "Luxury" hotels have the highest prices, followed by "Premium" and "Free & Easy."
+          - "Low Budget" and "Budget" hotels occupy the lower price range, showing that the category directly influences pricing strategy.
+          - Categories like "Luxury" and "Premium" aim to target a specific market willing to pay more for superior quality and services, while "Budget" and "Low Budget" cater to a price-sensitive segment.
+        """)
+            # Summary of Scatter Plot Analysis
+        st.subheader("Summary of Scatter Plot Analysis:")
+        st.write("""
+        - **Price vs Rating:** Higher-priced hotels generally offer better ratings, but the ratings vary widely across price points, indicating that factors such as service quality and amenities matter significantly.
+        - **Price vs Discount:** High-priced hotels may still offer discounts due to seasonal promotions or special offers.
+        - **Price vs Cashback:** Although high-priced hotels generally offer fewer cashback incentives, there are exceptions driven by promotional campaigns.
+        - **Price vs Category:** "Luxury" hotels are the most expensive, followed by "Premium" and "Free & Easy" categories. On the other hand, "Low Budget" and "Budget" hotels have lower prices.
+        - **Overall Insight:** The scatter plots reveal trends where higher-priced hotels tend to offer better ratings but fewer discounts and cashback incentives, while lower-priced categories tend to provide more promotional benefits such as discounts and cashback.
+        """)
+        
+        # Rating vs Category bar plot
+        st.subheader("Rating vs Category")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.barplot(x='category', y='rating', data=data, palette='Set1')
+        ax.set_title('Rating vs Category')
+        ax.set_xlabel('Category')
+        ax.set_ylabel('Rating')
+        st.pyplot(fig)
+        # Rating vs Category
+        st.subheader("Rating vs Category:")
+        st.write("""
+        - **Analysis:**
+          - "Luxury" hotels lead in average ratings, followed by "Premium" hotels.
+          - "Budget" and "Low Budget" categories show lower average ratings, indicating that these hotels may focus on price rather than offering premium services or experiences.
+          - The disparity in ratings shows that customers tend to have higher expectations for luxury and premium accommodations, which are reflected in the ratings.
+        """)
+            
+        # Discount vs Category box plot
+        st.subheader("Discount vs Category")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.boxplot(x='category', y='discount', data=data, palette='Set2')
+        ax.set_title('Discount vs Category')
+        ax.set_xlabel('Category')
+        ax.set_ylabel('Discount')
+        st.pyplot(fig)
+        # Discount vs Category
+        st.subheader("Discount vs Category:")
+        st.write("""
+        - **Analysis:**
+          - "Low Budget" hotels offer the highest discounts, while "Luxury" hotels provide the least discounts.
+          - This suggests that budget-friendly hotels use discounts as a key strategy to attract price-sensitive customers, whereas luxury hotels focus on providing premium experiences without relying on price reductions.
+          - The discount strategy varies by category, with lower-priced categories incentivizing customers with discounts to stay competitive.
+        """)
+        # Cashback vs Category violin plot
+        st.subheader("Cashback vs Category")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.violinplot(x='category', y='cashback', data=data, palette='Set3')
+        ax.set_title('Cashback vs Category')
+        ax.set_xlabel('Category')
+        ax.set_ylabel('Cashback')
+        st.pyplot(fig)
+        # Cashback vs Category
+        st.subheader("Cashback vs Category:")
+        st.write("""
+        - **Analysis:**
+          - Higher cashback offers are more common in "Low Budget" hotels, as these hotels rely on cashback incentives to attract customers looking for value deals.
+          - Luxury hotels rarely provide cashback, as their target market is less likely to be motivated by such offers.
+          - The trend highlights the different strategies employed by each category: budget options often provide financial incentives like cashback to drive bookings, while luxury options focus on premium services.
+        """)
+        
+        # Reviews vs Category count plot
+        st.subheader("Reviews vs Category")
+        fig, ax = plt.subplots(figsize=(7, 5))
+        sns.countplot(x='category', data=data, palette='Set1')
+        ax.set_title('Reviews vs Category (Count)')
+        ax.set_xlabel('Category')
+        ax.set_ylabel('Count of Reviews')
+        st.pyplot(fig)
+        # Reviews vs Category
+        st.subheader("Reviews vs Category:")
+        st.write("""
+        - **Analysis:**
+          - "Luxury" hotels attract the most reviews, indicating that higher-quality accommodations often receive more feedback from customers.
+          - "Budget" and "Low Budget" hotels tend to have fewer reviews, which may be due to their more straightforward offerings and smaller customer base.
+          - This trend suggests that customers who opt for luxury hotels are more likely to share their experiences, whereas budget options may attract fewer repeat customers or have less word-of-mouth influence.
+        """)
+        # Summary of Bar and Box Plot Analysis
+        st.subheader("Summary of Bar and Box Plot Analysis:")
+        st.write("""
+        - **Rating vs Category:** "Luxury" and "Premium" hotels have higher ratings on average, while "Budget" and "Low Budget" hotels show lower ratings.
+        - **Discount vs Category:** Budget hotels, especially "Low Budget," offer higher discounts, while luxury hotels offer fewer discounts, relying more on their value proposition.
+        - **Cashback vs Category:** "Low Budget" hotels offer higher cashback incentives, while luxury hotels rarely provide cashback, highlighting the pricing strategies in different categories.
+        - **Reviews vs Category:** "Luxury" hotels attract the most reviews, while "Budget" and "Low Budget" hotels attract fewer.
+        - **Overall Insight:** Bar and box plots reveal that higher-rated and more reviewed hotels tend to offer fewer discounts or cashback, focusing on customer experience, while budget categories focus on providing customer incentives to compete in the market.
+        """)
+        # Regional price analysis by state
+        st.subheader("Price by State")
+        fig, ax = plt.subplots(figsize=(16, 6))
+        sns.barplot(data=data, x='state', y='price', ax=ax, color='green')
+        ax.set_title('Price by State')
+        ax.tick_params(axis='x', rotation=90)
+        sns.set_palette('magma')
+        plt.tight_layout()
+        st.pyplot(fig)
+       # Hotel Prices Across Indian States
+        st.subheader("Hotel Prices Across Indian States:")
+        st.write("""
+        - **Analysis:**
+          - Hotel prices vary significantly across Indian states, reflecting regional differences in demand and supply.
+          - States with popular tourist destinations, such as Goa and Rajasthan, tend to show higher hotel prices, as they attract more visitors and have a higher demand for accommodations.
+          - Conversely, states with less tourism or lower demand may exhibit more affordable pricing, catering to the local population or budget travelers.
+          - Price differences also reflect factors such as local economic conditions, infrastructure, and tourism policies in each state.
+        """)
+        
+        # Regional category count by state
+        st.subheader("Category by State")
+        fig, ax = plt.subplots(figsize=(16, 6))
+        sns.countplot(data=data, x='state', hue='category', ax=ax, palette='Set1')
+        ax.set_title('Category by State')
+        ax.tick_params(axis='x', rotation=90)
+        plt.tight_layout()
+        st.pyplot(fig)
+       # Hotel Categories by State
+        st.subheader("Hotel Categories by State:")
+        st.write("""
+        - **Analysis:**
+          - States with a higher concentration of "Low Budget" and "Budget" hotels cater primarily to cost-conscious travelers, offering affordable accommodations for a wide range of customers.
+          - States with more "Luxury" hotels are likely to be major tourist hubs, such as Delhi, Mumbai, and Kerala, or regions that cater to premium audiences, offering high-end services for affluent customers.
+          - These states may also focus on attracting international tourists or business travelers who prefer premium amenities and luxury experiences.
+        """)
+        
+           # Summary of Regional Price and Category Trends
+        st.subheader("Summary of Regional Price and Category Trends:")
+        st.write("""
+        - **Hotel Prices Across Indian States:** Prices vary significantly depending on the state, with tourist-heavy regions showing higher prices due to greater demand.
+        - **Hotel Categories by State:** States with more budget hotels focus on catering to price-sensitive travelers, while states with luxury hotels cater to premium audiences, often in tourist hotspots.
+        - **Overall Insight:** Regional trends indicate diverse pricing and category distributions, influenced by tourism, regional economics, and state-specific factors that shape hotel offerings across the country.
+        """)
+    if pages="Multivariate Analysis":
+        st.title("Multivariate Analysis of Hotel Data")
+        
+        # Create a subset of the data for the analysis
+        subset_data = data[['category', 'price', 'reviews', 'discount', 'cashback', 'rating']]
+        
+        # Section 1: Price vs. Reviews by Category
+        st.header("Price vs. Reviews by Category")
+        fig1 = sns.catplot(data=data, x='reviews', y='price', hue='category', kind='strip', palette='Set2', height=6, aspect=1.5)
+        fig1.set_axis_labels("Reviews", "Price")
+        fig1.fig.suptitle('Price vs Reviews by Category', fontsize=16)
+        st.pyplot(fig1)
+        # Analysis Text for Price vs. Reviews by Category
+        st.write("""
+        - **Price Variation within Categories:**
+          - Wide price ranges exist within each category, with "Low Budget" hotels featuring both low- and high-priced options. 
+          - This shows that pricing within categories isn't always uniform and may depend on other factors like location, amenities, and hotel size.
+          
+        - **Price and Reviews Relationship:**
+          - There's a slight tendency for hotels with more reviews to have higher prices, possibly due to the influence of popularity, better marketing efforts, or higher quality services.
+        
+        - **Summary:**
+          - The stripplot reveals a weak positive correlation between price and reviews, indicating that well-reviewed hotels tend to have higher prices.
+        """)
+        
+        # Section 2: Price vs. Discount by Category
+        st.header("Price vs. Discount by Category")
+        fig2 = sns.catplot(data=data, x='discount', y='price', hue='category', kind='bar', palette='Set2', height=6, aspect=1.5)
+        fig2.set_axis_labels("Discount", "Price")
+        fig2.fig.suptitle('Price vs Discount by Category', fontsize=16)
+        st.pyplot(fig2) 
+        # Analysis Text for Price vs. Discount by Category
+        st.write("""
+        - **Price and Discount Relationship:**
+          - The stripplot clearly shows that as hotel prices increase, discounts tend to decrease. This confirms a negative correlation between price and discount.
+        
+        - **Category-Specific Trends:**
+          - "Low Budget" and "Budget" hotels offer much higher discounts compared to "Premium" and "Luxury" hotels, which typically offer fewer or smaller discounts.
+          - This trend highlights that budget-conscious categories use higher discounts to attract customers, whereas premium and luxury categories rely on factors other than discounts (e.g., quality, exclusivity) to appeal to their clientele.
+        
+        - **Summary:**
+          - The plot confirms that lower-priced hotels use higher discounts to attract customers, while premium and luxury hotels maintain lower discount rates, aligning with typical market behavior.
+        """)
+    
+        
+        # Section 3: Price vs Cashback and Rating by Category (Stripplot)
+        st.header("Price vs Cashback and Rating by Category")
+        fig3, axes2 = plt.subplots(1, 2, figsize=(16, 6))
+        
+        sns.stripplot(data=data, x='cashback', y='price', hue='category', ax=axes2[0], palette='Set2', jitter=True, dodge=True)
+        axes2[0].set_title('Price vs Cashback by Category')
+        
+        sns.stripplot(data=data, x='rating', y='price', hue='category', ax=axes2[1], palette='Set2', jitter=True, dodge=True)
+        axes2[1].set_title('Price vs Rating by Category')
+        
+        st.pyplot(fig3)
+        
+       
+        # Analysis Text for Price vs Cashback by Category
+        st.write("""
+        - **Price vs Cashback by Category:**
+          - The plot shows that cashback incentives tend to decrease as hotel prices increase. However, variations exist due to promotional offers that may affect cashback amounts.
+          - Lower-priced categories, such as "Budget" and "Low Budget," offer higher cashback incentives to attract price-sensitive customers.
+        
+        - **Summary:**
+          - The stripplot reveals a clear trend where lower-priced hotels use cashback as an incentive to boost bookings, whereas higher-priced hotels focus on other value propositions (e.g., premium services).
+        """)
+        # Analysis Text for Price vs Rating by Category
+        st.write("""
+        - **Price vs Rating by Category:**
+          - Higher-priced categories like "Premium" and "Luxury" tend to have better ratings, indicating that customers perceive these hotels as providing superior value.
+          - Interestingly, some lower-priced hotels achieve high ratings, suggesting that other factors such as service quality and customer experience may contribute to higher satisfaction despite the lower price point.
+        
+        - **Summary:**
+          - The stripplot shows that while higher-priced hotels emphasize quality to achieve better ratings, lower-priced hotels still manage to deliver satisfactory experiences for customers through factors like service quality.
+        """)
+        # Section 4: Correlation Heatmap
+        st.header("Correlation Matrix Heatmap")
+        numeric_columns = ['price', 'reviews', 'discount', 'cashback', 'rating']
+        
+        # Compute the correlation matrix
+        correlation_matrix = data[numeric_columns].corr()
+        
+        # Create a heatmap to visualize the correlation matrix
+        plt.figure(figsize=(10, 8))
+        sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, vmin=-1, vmax=1)
+        
+        # Set title for the plot
+        plt.title('Correlation Matrix Heatmap')
+        
+        # Display the plot
+        st.pyplot(plt)
+        
+        # Analysis Text for Correlation Heatmap
+        st.write("""
+        - **Price vs Discount/Cashback:**
+          - The heatmap shows a strong negative correlation between price and both discount and cashback. Higher-priced hotels tend to offer fewer discounts and cashback incentives, suggesting that premium offerings rely on value rather than incentives.
+          
+        - **Price vs Rating:**
+          - A weak positive correlation is observed between price and rating. Higher-priced hotels generally have slightly better ratings, although this relationship is not very strong.
+        
+        - **Reviews vs Rating:**
+          - A moderate positive correlation exists between reviews and ratings. Hotels that attract more reviews tend to have better ratings, likely due to a larger customer base providing feedback.
+          
+        - **Reviews vs Price:**
+          - A weak positive correlation between reviews and price suggests that well-reviewed hotels are often priced higher, likely due to their popularity and perceived value.
+        
+        - **Summary:**
+          - The heatmap provides insights into the relationships between key variables in the dataset. Higher-priced hotels tend to offer fewer discounts and cashback offers but generally have better ratings and more reviews, indicating that customers are willing to pay a premium for a better experience.
+        """)
+        
+        st.header("Overall Summary")
+        st.write("""
+            - Most properties are affordable, with lower prices, cashback, and discounts dominating the dataset.
+            - Regional distribution shows states like Maharashtra and Madhya Pradesh having more hotels.
+            - The data reflects a market focused on affordability and basic amenities, with regional and category-specific variations.
+            - Cancellations and reviews provide further insights into customer behavior, while skewed distributions highlight potential outliers and trends in pricing and service offerings.
+        """)
+        
+        st.header("Why Right-Skewed Trends Are Normal, Not Outliers")
+        st.write("""
+            - Right-skewed distributions for price, cashback, discounts, cancellations, reviews, and amenities are normal trends in the market.
+            - These trends represent the expected distribution of data where higher values are less frequent but are not considered outliers.
+            - The variations in cancellation patterns and review counts reflect typical customer behavior and industry dynamics.
+        """)
+        st.write("""
+            Since no outliers were detected, we can proceed with model training and selection.
+            With clean data, we can now focus on choosing the best algorithm, tuning hyperparameters, and evaluating model performance.
+        """)
+
+
+else:
+    st.warning("No dataset found in session state. Please load the dataset into `st.session_state['data']`.")
+    

pages/4_EDA and Feature Engineering.py

@@ -1,572 +0,0 @@
-import streamlit as st 
-import numpy as np 
-import pandas as pd 
-import matplotlib.pyplot as plt
-import seaborn as sns
-from io import StringIO
-import sys
-
-st.markdown("<h1 style='text-align:center; color:lime;'>EDA and Feature Engineering</h1>",unsafe_allow_html=True)
-
-
-# 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.
-""")
-
-# 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")
-
-if data is not None:
-    st.subheader("Dataset Preview:")
-    st.write(data)  # Display the first 5 rows
-    
-    
-    st.subheader("Info of the Dataset:")
-    # Redirect the output of df.info() to a string buffer
-    buffer = StringIO()
-    data.info(buf=buffer)
-    
-    # Display the content in Streamlit
-    st.write(buffer.getvalue())
-    
-    st.subheader("Dataset Statistics:")
-    st.write(data.describe())
-    
-    st.subheader("Dataset Shape (Rows, Columns):")
-    st.write(data.shape)
-
-    data= data[data["price"] <= 40000]  # Keep rows where price is less than or equal to 40,000
-    
-    
-### univariate_analysis.
-    st.success("Dataset successfully loaded from session state!")
-    
-    st.subheader("Univariate Analysis")
-    
-    # Rating and Review Text Distribution
-    st.subheader("Rating and Review Text Distribution")
-    fig, axs = plt.subplots(1, 2, figsize=(16, 6))
-
-    data["rating"].value_counts().plot(kind='pie', title='Distribution of Ratings', autopct='%1.1f%%', shadow=True, startangle=45, textprops={'size': 'x-large'}, ax=axs[0])
-    axs[0].set_title("Distribution of Ratings")
-
-    data['review_text'].value_counts().plot(kind='pie', title='Distribution of Review Text', autopct='%1.1f%%', shadow=True, startangle=45, textprops={'size': 'x-large'}, ax=axs[1])
-    axs[1].set_title("Distribution of Review Text")
-
-    st.pyplot(fig)
-
-    # Hotel Star Insights
-    st.write("""
-        **Insight:**
-        - Majority of hotels in this data are 3-star hotels.
-        - Frequency of 4-star and 5-star hotels are also moderately good.
-        - 1-star and 2-star hotels are lower in frequency.
-    """)
-
-    # Price, Cashback, and Discount Distribution
-    st.subheader("Price, Cashback, and Discount Distribution")
-    fig, axs = plt.subplots(1, 3, figsize=(16, 6))
-
-    sns.histplot(data=data, x='price', color='green', kde=True, ax=axs[0])
-    axs[0].set_title("Count based on price")
-    axs[0].set_xlabel('Price')
-    axs[0].set_ylabel('Number of People')
-
-    sns.histplot(data=data, x='cashback', color='violet', kde=True, ax=axs[1])
-    axs[1].set_title("Count based on cashback")
-    axs[1].set_xlabel('Cashback')
-    axs[1].set_ylabel('Number of People')
-
-    sns.histplot(data=data, x='discount', color='orange', kde=True, ax=axs[2])
-    axs[2].set_title("Count based on discount")
-    axs[2].set_xlabel('Discount')
-
-    st.pyplot(fig)
-      # Histogram Insights
-    st.subheader("Plot-wise Analysis of Histograms")
-    st.write("""
-        **Price Distribution Insight:**
-        - The histogram is right-skewed, showing most properties are in the lower price range.
-        - A long tail indicates the presence of a few very expensive properties.
-        
-        **Cashback Distribution Insight:**
-        - The histogram is right-skewed, with the majority of properties offering lower cashback amounts.
-        - Only a small number of properties provide higher cashback.
-        
-        **Discount Distribution Insight:**
-        - The histogram is right-skewed, indicating that most properties offer lower discount percentages.
-        - A few properties stand out with higher discounts.
-        
-        **Summary:**
-        The data suggest that Agoda properties are generally affordable, with lower cashback and discount offers being common. Further statistical analysis could help uncover more detailed insights.
-    """)
-
-    # Cancellation and State Distribution
-    st.subheader("Cancellation and State Distribution")
-    fig, axs = plt.subplots(1, 2, figsize=(16, 6))
-
-    data["cancellation"].value_counts().plot(kind='bar', title='Distribution of Cancellation', color='red', ax=axs[0])
-    axs[0].set_title("Distribution of Cancellation")
-    axs[0].set_xlabel('Cancellation')
-    axs[0].set_ylabel('Number of Hotels')
-
-    data["state"].value_counts().plot(kind='bar', title='Distribution of State', color='black', ax=axs[1])
-    axs[1].set_title("Distribution of State")
-    axs[1].set_xlabel('State')
-    axs[1].set_ylabel('Number of Hotels')
-
-    st.pyplot(fig)
-    # Bar Chart Insights
-    st.subheader("Plot Wise Analysis of Bar Charts")
-    st.write("""
-        **Cancellations:**
-        - Most cancellations fall under category "1," indicating they occur within specific conditions or timeframes.
-        
-        **State Distribution:**
-        - "Maharashtra" has the highest number of hotels, followed by "Madhya Pradesh."
-        - Other states like Gujarat, Karnataka, and Kerala also have notable hotel counts.
-        - The distribution is uneven, with some states having significantly more hotels.
-        
-        **Summary:**
-        The charts highlight cancellation trends and the regional hotel distribution in India.
-    """)
-
-    # Category and Reviews Distribution
-    st.subheader("Category and Reviews Distribution")
-    fig, axs = plt.subplots(1, 2, figsize=(16, 6))
-
-    colors = sns.color_palette('Set2', n_colors=len(data["category"].value_counts()))
-    data["category"].value_counts().plot(kind='bar', ax=axs[0], color=colors)
-    axs[0].set_title("Distribution of Category")
-    axs[0].set_xlabel('Category')
-    axs[0].set_ylabel('Number of Hotels')
-
-    sns.histplot(data=data, x='reviews', color='violet', kde=True, ax=axs[1])
-    axs[1].set_title("Count based on Reviews")
-    axs[1].set_xlabel('Reviews')
-    axs[1].set_ylabel('Number of Reviews')
-
-    st.pyplot(fig)
-    # Hotel Categories and Reviews Insights
-    st.subheader("Plot Wise Analysis of Hotel Categories and Reviews")
-    st.write("""
-        **Category Distribution:**
-        - The histogram shows "Low Budget" hotels are the most common, followed by "Budget Hotels," while "Luxury Hotels" are the least common.
-        
-        **Review Count Distribution:**
-        - The histogram is right-skewed, with most hotels having a low number of reviews.
-        - A few hotels have a very high number of reviews, evident from the long tail.
-        
-        **Summary:**
-        The data indicates a higher concentration of low-budget hotels and relatively low review counts for most hotels.
-    """)
-
-    # Top 10 Amenities
-    st.subheader("Top 10 Amenities")
-    amenity_counts = data['free_services'].str.split(',').explode().str.strip().value_counts().reset_index()
-    amenity_counts.columns = ['Amenity', 'Count']
-
-    fig, ax = plt.subplots(figsize=(10, 6))
-    sns.barplot(x='Count', y='Amenity', data=amenity_counts.head(10), palette='viridis', ax=ax)
-    ax.set_title('Top 10 Amenities')
-    ax.set_xlabel('Number of Hotels Offering')
-    ax.set_ylabel('Amenity')
-
-    st.pyplot(fig)
-    # Top Amenities Insights
-    st.subheader("Plot Wise Analysis of Top Amenities")
-    st.write("""
-        **Common Amenities:**
-        - Complimentary Parking is the most frequently offered amenity.
-        - Basic Toiletries and Hair Dryers are also widely available.
-        
-        **Less Common Amenities:**
-        - Fitness Center Access, Welcome Drinks, and Turndown Service are less common.
-        - Shoe Shine Service is the least frequently offered amenity.
-        
-        **Summary:**
-        Hotels tend to prioritize basic amenities like parking, toiletries, and hair dryers, while luxurious amenities are offered less frequently.
-    """)
- 
-    # Streamlit app
-    st.title("Bivariate Analysis")
-    
-    # Price vs Rating scatter plot
-    st.subheader("Price vs Rating")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.scatterplot(x='rating', y='price', data=data, color='orange')
-    ax.set_title('Price vs Rating')
-    ax.set_xlabel('Rating')
-    ax.set_ylabel('Price')
-    st.pyplot(fig)
-    
-    # Price vs Rating
-    st.subheader("Price vs Rating:")
-    st.write("""
-    - **Analysis:**
-      - Higher-priced hotels slightly tend to have better ratings, but ratings vary widely across price points.
-      - Hotels at various price points exhibit a large spread in ratings, meaning factors other than price (such as customer experience or amenities) contribute significantly to the rating.
-    """)
-    
-    # Price vs Discount scatter plot
-    st.subheader("Price vs Discount")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.scatterplot(x='discount', y='price', data=data, color='green')
-    ax.set_title('Price vs Discount')
-    ax.set_xlabel('Discount')
-    ax.set_ylabel('Price')
-    st.pyplot(fig)
-    # Price vs Discount
-    st.subheader("Price vs Discount:")
-    st.write("""
-    - **Analysis:**
-      - Some high-priced hotels still provide discounts due to promotions or special deals.
-      - This observation suggests that while premium hotels may not always need to offer discounts to attract customers, they occasionally use them as a marketing strategy or for seasonal promotions.
-    """)
-    
-    # Price vs Cashback scatter plot
-    st.subheader("Price vs Cashback")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.scatterplot(x='cashback', y='price', data=data, color='blue')
-    ax.set_title('Price vs Cashback')
-    ax.set_xlabel('Cashback')
-    ax.set_ylabel('Price')
-    st.pyplot(fig)
-    
-    # Price vs Cashback
-    st.subheader("Price vs Cashback:")
-    st.write("""
-    - **Analysis:**
-      - Exceptions exist due to promotional campaigns.
-      - While higher-priced hotels generally offer fewer cashback incentives, some may offer cashback due to specific promotional campaigns aimed at increasing sales volume or attracting customers in a competitive market.
-    """)
-    
-    # Price vs Category bar plot
-    st.subheader("Price vs Category")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.barplot(x='category', y='price', data=data, palette='Set2')
-    ax.set_title('Price vs Category')
-    ax.set_xlabel('Category')
-    ax.set_ylabel('Price')
-    st.pyplot(fig)
-    # Price vs Category
-    st.subheader("Price vs Category:")
-    st.write("""
-    - **Analysis:**
-      - "Luxury" hotels have the highest prices, followed by "Premium" and "Free & Easy."
-      - "Low Budget" and "Budget" hotels occupy the lower price range, showing that the category directly influences pricing strategy.
-      - Categories like "Luxury" and "Premium" aim to target a specific market willing to pay more for superior quality and services, while "Budget" and "Low Budget" cater to a price-sensitive segment.
-    """)
-        # Summary of Scatter Plot Analysis
-    st.subheader("Summary of Scatter Plot Analysis:")
-    st.write("""
-    - **Price vs Rating:** Higher-priced hotels generally offer better ratings, but the ratings vary widely across price points, indicating that factors such as service quality and amenities matter significantly.
-    - **Price vs Discount:** High-priced hotels may still offer discounts due to seasonal promotions or special offers.
-    - **Price vs Cashback:** Although high-priced hotels generally offer fewer cashback incentives, there are exceptions driven by promotional campaigns.
-    - **Price vs Category:** "Luxury" hotels are the most expensive, followed by "Premium" and "Free & Easy" categories. On the other hand, "Low Budget" and "Budget" hotels have lower prices.
-    - **Overall Insight:** The scatter plots reveal trends where higher-priced hotels tend to offer better ratings but fewer discounts and cashback incentives, while lower-priced categories tend to provide more promotional benefits such as discounts and cashback.
-    """)
-    
-    # Rating vs Category bar plot
-    st.subheader("Rating vs Category")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.barplot(x='category', y='rating', data=data, palette='Set1')
-    ax.set_title('Rating vs Category')
-    ax.set_xlabel('Category')
-    ax.set_ylabel('Rating')
-    st.pyplot(fig)
-    # Rating vs Category
-    st.subheader("Rating vs Category:")
-    st.write("""
-    - **Analysis:**
-      - "Luxury" hotels lead in average ratings, followed by "Premium" hotels.
-      - "Budget" and "Low Budget" categories show lower average ratings, indicating that these hotels may focus on price rather than offering premium services or experiences.
-      - The disparity in ratings shows that customers tend to have higher expectations for luxury and premium accommodations, which are reflected in the ratings.
-    """)
-        
-    # Discount vs Category box plot
-    st.subheader("Discount vs Category")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.boxplot(x='category', y='discount', data=data, palette='Set2')
-    ax.set_title('Discount vs Category')
-    ax.set_xlabel('Category')
-    ax.set_ylabel('Discount')
-    st.pyplot(fig)
-    # Discount vs Category
-    st.subheader("Discount vs Category:")
-    st.write("""
-    - **Analysis:**
-      - "Low Budget" hotels offer the highest discounts, while "Luxury" hotels provide the least discounts.
-      - This suggests that budget-friendly hotels use discounts as a key strategy to attract price-sensitive customers, whereas luxury hotels focus on providing premium experiences without relying on price reductions.
-      - The discount strategy varies by category, with lower-priced categories incentivizing customers with discounts to stay competitive.
-    """)
-    # Cashback vs Category violin plot
-    st.subheader("Cashback vs Category")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.violinplot(x='category', y='cashback', data=data, palette='Set3')
-    ax.set_title('Cashback vs Category')
-    ax.set_xlabel('Category')
-    ax.set_ylabel('Cashback')
-    st.pyplot(fig)
-    # Cashback vs Category
-    st.subheader("Cashback vs Category:")
-    st.write("""
-    - **Analysis:**
-      - Higher cashback offers are more common in "Low Budget" hotels, as these hotels rely on cashback incentives to attract customers looking for value deals.
-      - Luxury hotels rarely provide cashback, as their target market is less likely to be motivated by such offers.
-      - The trend highlights the different strategies employed by each category: budget options often provide financial incentives like cashback to drive bookings, while luxury options focus on premium services.
-    """)
-    
-    # Reviews vs Category count plot
-    st.subheader("Reviews vs Category")
-    fig, ax = plt.subplots(figsize=(7, 5))
-    sns.countplot(x='category', data=data, palette='Set1')
-    ax.set_title('Reviews vs Category (Count)')
-    ax.set_xlabel('Category')
-    ax.set_ylabel('Count of Reviews')
-    st.pyplot(fig)
-    # Reviews vs Category
-    st.subheader("Reviews vs Category:")
-    st.write("""
-    - **Analysis:**
-      - "Luxury" hotels attract the most reviews, indicating that higher-quality accommodations often receive more feedback from customers.
-      - "Budget" and "Low Budget" hotels tend to have fewer reviews, which may be due to their more straightforward offerings and smaller customer base.
-      - This trend suggests that customers who opt for luxury hotels are more likely to share their experiences, whereas budget options may attract fewer repeat customers or have less word-of-mouth influence.
-    """)
-    # Summary of Bar and Box Plot Analysis
-    st.subheader("Summary of Bar and Box Plot Analysis:")
-    st.write("""
-    - **Rating vs Category:** "Luxury" and "Premium" hotels have higher ratings on average, while "Budget" and "Low Budget" hotels show lower ratings.
-    - **Discount vs Category:** Budget hotels, especially "Low Budget," offer higher discounts, while luxury hotels offer fewer discounts, relying more on their value proposition.
-    - **Cashback vs Category:** "Low Budget" hotels offer higher cashback incentives, while luxury hotels rarely provide cashback, highlighting the pricing strategies in different categories.
-    - **Reviews vs Category:** "Luxury" hotels attract the most reviews, while "Budget" and "Low Budget" hotels attract fewer.
-    - **Overall Insight:** Bar and box plots reveal that higher-rated and more reviewed hotels tend to offer fewer discounts or cashback, focusing on customer experience, while budget categories focus on providing customer incentives to compete in the market.
-    """)
-    # Regional price analysis by state
-    st.subheader("Price by State")
-    fig, ax = plt.subplots(figsize=(16, 6))
-    sns.barplot(data=data, x='state', y='price', ax=ax, color='green')
-    ax.set_title('Price by State')
-    ax.tick_params(axis='x', rotation=90)
-    sns.set_palette('magma')
-    plt.tight_layout()
-    st.pyplot(fig)
-   # Hotel Prices Across Indian States
-    st.subheader("Hotel Prices Across Indian States:")
-    st.write("""
-    - **Analysis:**
-      - Hotel prices vary significantly across Indian states, reflecting regional differences in demand and supply.
-      - States with popular tourist destinations, such as Goa and Rajasthan, tend to show higher hotel prices, as they attract more visitors and have a higher demand for accommodations.
-      - Conversely, states with less tourism or lower demand may exhibit more affordable pricing, catering to the local population or budget travelers.
-      - Price differences also reflect factors such as local economic conditions, infrastructure, and tourism policies in each state.
-    """)
-    
-    # Regional category count by state
-    st.subheader("Category by State")
-    fig, ax = plt.subplots(figsize=(16, 6))
-    sns.countplot(data=data, x='state', hue='category', ax=ax, palette='Set1')
-    ax.set_title('Category by State')
-    ax.tick_params(axis='x', rotation=90)
-    plt.tight_layout()
-    st.pyplot(fig)
-   # Hotel Categories by State
-    st.subheader("Hotel Categories by State:")
-    st.write("""
-    - **Analysis:**
-      - States with a higher concentration of "Low Budget" and "Budget" hotels cater primarily to cost-conscious travelers, offering affordable accommodations for a wide range of customers.
-      - States with more "Luxury" hotels are likely to be major tourist hubs, such as Delhi, Mumbai, and Kerala, or regions that cater to premium audiences, offering high-end services for affluent customers.
-      - These states may also focus on attracting international tourists or business travelers who prefer premium amenities and luxury experiences.
-    """)
-    
-       # Summary of Regional Price and Category Trends
-    st.subheader("Summary of Regional Price and Category Trends:")
-    st.write("""
-    - **Hotel Prices Across Indian States:** Prices vary significantly depending on the state, with tourist-heavy regions showing higher prices due to greater demand.
-    - **Hotel Categories by State:** States with more budget hotels focus on catering to price-sensitive travelers, while states with luxury hotels cater to premium audiences, often in tourist hotspots.
-    - **Overall Insight:** Regional trends indicate diverse pricing and category distributions, influenced by tourism, regional economics, and state-specific factors that shape hotel offerings across the country.
-    """)
-    
-    st.title("Multivariate Analysis of Hotel Data")
-    
-    # Create a subset of the data for the analysis
-    subset_data = data[['category', 'price', 'reviews', 'discount', 'cashback', 'rating']]
-    
-    # Section 1: Price vs. Reviews by Category
-    st.header("Price vs. Reviews by Category")
-    fig1 = sns.catplot(data=data, x='reviews', y='price', hue='category', kind='strip', palette='Set2', height=6, aspect=1.5)
-    fig1.set_axis_labels("Reviews", "Price")
-    fig1.fig.suptitle('Price vs Reviews by Category', fontsize=16)
-    st.pyplot(fig1)
-    # Analysis Text for Price vs. Reviews by Category
-    st.write("""
-    - **Price Variation within Categories:**
-      - Wide price ranges exist within each category, with "Low Budget" hotels featuring both low- and high-priced options. 
-      - This shows that pricing within categories isn't always uniform and may depend on other factors like location, amenities, and hotel size.
-      
-    - **Price and Reviews Relationship:**
-      - There's a slight tendency for hotels with more reviews to have higher prices, possibly due to the influence of popularity, better marketing efforts, or higher quality services.
-    
-    - **Summary:**
-      - The stripplot reveals a weak positive correlation between price and reviews, indicating that well-reviewed hotels tend to have higher prices.
-    """)
-    
-    # Section 2: Price vs. Discount by Category
-    st.header("Price vs. Discount by Category")
-    fig2 = sns.catplot(data=data, x='discount', y='price', hue='category', kind='bar', palette='Set2', height=6, aspect=1.5)
-    fig2.set_axis_labels("Discount", "Price")
-    fig2.fig.suptitle('Price vs Discount by Category', fontsize=16)
-    st.pyplot(fig2) 
-    # Analysis Text for Price vs. Discount by Category
-    st.write("""
-    - **Price and Discount Relationship:**
-      - The stripplot clearly shows that as hotel prices increase, discounts tend to decrease. This confirms a negative correlation between price and discount.
-    
-    - **Category-Specific Trends:**
-      - "Low Budget" and "Budget" hotels offer much higher discounts compared to "Premium" and "Luxury" hotels, which typically offer fewer or smaller discounts.
-      - This trend highlights that budget-conscious categories use higher discounts to attract customers, whereas premium and luxury categories rely on factors other than discounts (e.g., quality, exclusivity) to appeal to their clientele.
-    
-    - **Summary:**
-      - The plot confirms that lower-priced hotels use higher discounts to attract customers, while premium and luxury hotels maintain lower discount rates, aligning with typical market behavior.
-    """)
-
-    
-    # Section 3: Price vs Cashback and Rating by Category (Stripplot)
-    st.header("Price vs Cashback and Rating by Category")
-    fig3, axes2 = plt.subplots(1, 2, figsize=(16, 6))
-    
-    sns.stripplot(data=data, x='cashback', y='price', hue='category', ax=axes2[0], palette='Set2', jitter=True, dodge=True)
-    axes2[0].set_title('Price vs Cashback by Category')
-    
-    sns.stripplot(data=data, x='rating', y='price', hue='category', ax=axes2[1], palette='Set2', jitter=True, dodge=True)
-    axes2[1].set_title('Price vs Rating by Category')
-    
-    st.pyplot(fig3)
-    
-   
-    # Analysis Text for Price vs Cashback by Category
-    st.write("""
-    - **Price vs Cashback by Category:**
-      - The plot shows that cashback incentives tend to decrease as hotel prices increase. However, variations exist due to promotional offers that may affect cashback amounts.
-      - Lower-priced categories, such as "Budget" and "Low Budget," offer higher cashback incentives to attract price-sensitive customers.
-    
-    - **Summary:**
-      - The stripplot reveals a clear trend where lower-priced hotels use cashback as an incentive to boost bookings, whereas higher-priced hotels focus on other value propositions (e.g., premium services).
-    """)
-    # Analysis Text for Price vs Rating by Category
-    st.write("""
-    - **Price vs Rating by Category:**
-      - Higher-priced categories like "Premium" and "Luxury" tend to have better ratings, indicating that customers perceive these hotels as providing superior value.
-      - Interestingly, some lower-priced hotels achieve high ratings, suggesting that other factors such as service quality and customer experience may contribute to higher satisfaction despite the lower price point.
-    
-    - **Summary:**
-      - The stripplot shows that while higher-priced hotels emphasize quality to achieve better ratings, lower-priced hotels still manage to deliver satisfactory experiences for customers through factors like service quality.
-    """)
-    # Section 4: Correlation Heatmap
-    st.header("Correlation Matrix Heatmap")
-    numeric_columns = ['price', 'reviews', 'discount', 'cashback', 'rating']
-    
-    # Compute the correlation matrix
-    correlation_matrix = data[numeric_columns].corr()
-    
-    # Create a heatmap to visualize the correlation matrix
-    plt.figure(figsize=(10, 8))
-    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, vmin=-1, vmax=1)
-    
-    # Set title for the plot
-    plt.title('Correlation Matrix Heatmap')
-    
-    # Display the plot
-    st.pyplot(plt)
-    
-    # Analysis Text for Correlation Heatmap
-    st.write("""
-    - **Price vs Discount/Cashback:**
-      - The heatmap shows a strong negative correlation between price and both discount and cashback. Higher-priced hotels tend to offer fewer discounts and cashback incentives, suggesting that premium offerings rely on value rather than incentives.
-      
-    - **Price vs Rating:**
-      - A weak positive correlation is observed between price and rating. Higher-priced hotels generally have slightly better ratings, although this relationship is not very strong.
-    
-    - **Reviews vs Rating:**
-      - A moderate positive correlation exists between reviews and ratings. Hotels that attract more reviews tend to have better ratings, likely due to a larger customer base providing feedback.
-      
-    - **Reviews vs Price:**
-      - A weak positive correlation between reviews and price suggests that well-reviewed hotels are often priced higher, likely due to their popularity and perceived value.
-    
-    - **Summary:**
-      - The heatmap provides insights into the relationships between key variables in the dataset. Higher-priced hotels tend to offer fewer discounts and cashback offers but generally have better ratings and more reviews, indicating that customers are willing to pay a premium for a better experience.
-    """)
-    
-    st.header("Overall Summary")
-    st.write("""
-        - Most properties are affordable, with lower prices, cashback, and discounts dominating the dataset.
-        - Regional distribution shows states like Maharashtra and Madhya Pradesh having more hotels.
-        - The data reflects a market focused on affordability and basic amenities, with regional and category-specific variations.
-        - Cancellations and reviews provide further insights into customer behavior, while skewed distributions highlight potential outliers and trends in pricing and service offerings.
-    """)
-    
-    st.header("Why Right-Skewed Trends Are Normal, Not Outliers")
-    st.write("""
-        - Right-skewed distributions for price, cashback, discounts, cancellations, reviews, and amenities are normal trends in the market.
-        - These trends represent the expected distribution of data where higher values are less frequent but are not considered outliers.
-        - The variations in cancellation patterns and review counts reflect typical customer behavior and industry dynamics.
-    """)
-    st.write("""
-        Since no outliers were detected, we can proceed with model training and selection.
-        With clean data, we can now focus on choosing the best algorithm, tuning hyperparameters, and evaluating model performance.
-    """)
-
-
-else:
-    st.warning("No dataset found in session state. Please load the dataset into `st.session_state['data']`.")
-