Update pages/3_EDA and Feature Engineering.py

pages/3_EDA and Feature Engineering.py

@@ -99,479 +99,478 @@ if data is not None:
     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.
-        """)
-    
-        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.
-        """)
+    # 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")
 
-else:
-    st.warning("No dataset found in session state. Please load the dataset into `st.session_state['data']`.")
+    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.
+    """)
+    
+    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']`.")
+