Update Home.py

Home.py

@@ -1,32 +1,32 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
+# import streamlit as st
+# import pandas as pd
+# import numpy as np
 
-st.markdown("""
-    <h1 style="text-align:center; color:orange;">Hotel Data Analysis & Machine Learning</h1>
-""", unsafe_allow_html=True)
+# st.markdown("""
+#     <h1 style="text-align:center; color:orange;">Hotel Data Analysis & Machine Learning</h1>
+# """, unsafe_allow_html=True)
 
-st.markdown("""
-## Predicting Customer Preferences and Optimizing Pricing:
-#### 📊 Data Exploration and Preprocessing:
-- <span style="font-size:20px;">Cleaning and preparing data by handling missing values, encoding categorical features like *"category"* and *"location,"* and normalizing numerical data such as *"price"* and *"rating."*</span>
-- <span style="font-size:20px;">Analyzing trends in **customer reviews**, **cashback offers**, **discounts**, and **free services** to identify influential factors.
+# st.markdown("""
+# ## Predicting Customer Preferences and Optimizing Pricing:
+# #### 📊 Data Exploration and Preprocessing:
+# - <span style="font-size:20px;">Cleaning and preparing data by handling missing values, encoding categorical features like *"category"* and *"location,"* and normalizing numerical data such as *"price"* and *"rating."*</span>
+# - <span style="font-size:20px;">Analyzing trends in **customer reviews**, **cashback offers**, **discounts**, and **free services** to identify influential factors.
 
-#### 🤖 Predictive Modeling:
-- <span style="font-size:20px;">**Target Variable**: Predicting key metrics like *price category*, *likelihood of cancellation*, or *hotel ratings.*</span>
-- <span style="font-size:20px;">**Model Selection**: Building ML models such as **Decision Trees**, **Random Forests**, or **Gradient Boosting** for classification or regression tasks.</span>
-- <span style="font-size:20px;">**Feature Engineering**: Extracting insights from **review text** (via text sentiment analysis) or **free services** (binary encoding).</span>
+# #### 🤖 Predictive Modeling:
+# - <span style="font-size:20px;">**Target Variable**: Predicting key metrics like *price category*, *likelihood of cancellation*, or *hotel ratings.*</span>
+# - <span style="font-size:20px;">**Model Selection**: Building ML models such as **Decision Trees**, **Random Forests**, or **Gradient Boosting** for classification or regression tasks.</span>
+# - <span style="font-size:20px;">**Feature Engineering**: Extracting insights from **review text** (via text sentiment analysis) or **free services** (binary encoding).</span>
 
-#### 📈 Model Evaluation:
-- <span style="font-size:20px;">Comparing model performance using metrics like **accuracy**, **F1 score**, or **RMSE**, depending on the task.</span>
-- <span style="font-size:20px;">Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.</span>
+# #### 📈 Model Evaluation:
+# - <span style="font-size:20px;">Comparing model performance using metrics like **accuracy**, **F1 score**, or **RMSE**, depending on the task.</span>
+# - <span style="font-size:20px;">Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.</span>
 
-#### 💼 Insights and Deployment:
-- <span style="font-size:20px;">Unveiling actionable insights from **feature importance** to guide hotel marketing and pricing strategies.</span>
-- <span style="font-size:20px;">Deploying the model in a user-friendly interface to support stakeholders in making real-time decisions.</span>
+# #### 💼 Insights and Deployment:
+# - <span style="font-size:20px;">Unveiling actionable insights from **feature importance** to guide hotel marketing and pricing strategies.</span>
+# - <span style="font-size:20px;">Deploying the model in a user-friendly interface to support stakeholders in making real-time decisions.</span>
 
-#### By integrating **machine learning** with **data analysis**, this project empowers hotel businesses to enhance customer satisfaction, optimize pricing strategies, and maximize profitability.
-""", unsafe_allow_html=True)
+# #### By integrating **machine learning** with **data analysis**, this project empowers hotel businesses to enhance customer satisfaction, optimize pricing strategies, and maximize profitability.
+# """, unsafe_allow_html=True)
 
 import os
 import pandas as pd
@@ -82,7 +82,7 @@ if page == "Home":
 elif page == "Hotel Data":
     import pages.Hotel_Data
 elif page == "Simple-EDA":
-    import pages.Simple-EDA
+    import pages.Simple_EDA
 
     # # File uploader to upload a new dataset
     # uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])