Update Hotel Data Card.py

Hotel Data Card.py

@@ -2,33 +2,29 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 
-st.title('''
-🏨 Hotel Data Analysis & 🤖 Machine Learning: Predicting Customer Preferences and Optimizing Pricing 📊
-''')
+st.markdown(<h1 style='title-align:center,color:white'>
+🏨 Hotel Data Analysis & 🤖 Machine Learning: Predicting Customer Preferences and Optimizing Pricing 📊</h1>,unsafe-allow-html=True)
 st.markdown('''
 
-🏨 **Hotel Data Analysis and Machine Learning Project**  
+###🏨 **Hotel Data Analysis and Machine Learning Project**  
 
-📊 **Data Exploration and Preprocessing**:  
-- 🧹 Cleaning and preparing data by handling missing values, encoding categorical features like *"category"* and *"location,"* and normalizing numerical data such as *"price"* and *"rating."*  
-- 🔍 Analyzing trends in **customer reviews**, **cashback offers**, **discounts**, and **free services** to identify influential factors.  
+####📊 **Data Exploration and Preprocessing**:  
+    - 🧹 Cleaning and preparing data by handling missing values, encoding categorical features like *"category"* and *"location,"* and normalizing numerical data such as *"price"* and *"rating."*  
+    - 🔍 Analyzing trends in **customer reviews**, **cashback offers**, **discounts**, and **free services** to identify influential factors.  
 
-🤖 **Predictive Modeling**:  
-- 🎯 **Target Variable**: Predicting key metrics like *price category*, *likelihood of cancellation*, or *hotel ratings.*  
-- 🛠️ **Model Selection**: Building ML models such as **Decision Trees**, **Random Forests**, or **Gradient Boosting** for classification or regression tasks.  
-- 💡 **Feature Engineering**: Extracting insights from **review text** (via text sentiment analysis) or **free services** (binary encoding).  
+####🤖 **Predictive Modeling**:  
+    - 🎯 **Target Variable**: Predicting key metrics like *price category*, *likelihood of cancellation*, or *hotel ratings.*  
+    - 🛠️ **Model Selection**: Building ML models such as **Decision Trees**, **Random Forests**, or **Gradient Boosting** for classification or regression tasks.  
+    - 💡 **Feature Engineering**: Extracting insights from **review text** (via text sentiment analysis) or **free services** (binary encoding).  
 
-📈 **Model Evaluation**:  
-- 📊 Comparing model performance using metrics like **accuracy**, **F1 score**, or **RMSE**, depending on the task.  
-- ⚙️ Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.  
+####📈 **Model Evaluation**:  
+    - 📊 Comparing model performance using metrics like **accuracy**, **F1 score**, or **RMSE**, depending on the task.  
+    - ⚙️ Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.  
 
-💼 **Insights and Deployment**:  
-- 🔑 Unveiling actionable insights from **feature importance** to guide hotel marketing and pricing strategies.  
-- 🌐 Deploying the model in a user-friendly interface to support stakeholders in making real-time decisions.  
+####💼 **Insights and Deployment**:  
+    - 🔑 Unveiling actionable insights from **feature importance** to guide hotel marketing and pricing strategies.  
+    - 🌐 Deploying the model in a user-friendly interface to support stakeholders in making real-time decisions.  
 
-By integrating 🧠 **machine learning** with 📊 **data analysis**, this project empowers hotel businesses to enhance customer satisfaction, optimize pricing strategies, and maximize profitability.  
+####By integrating 🧠 **machine learning** with 📊 **data analysis**, this project empowers hotel businesses to enhance customer satisfaction, optimize pricing strategies, and maximize profitability.  
 
----  
-
-Let me know if this works or needs further edits! 😊
 ''')