Update Home.py

Home.py

@@ -1,134 +1,30 @@
-# 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("""
-# ## 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>
-
-# #### 📈 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>
-
-# #### 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
 import streamlit as st
+import pandas as pd
+import numpy as np
 
-# Define a persistent file path for the dataset
-DATA_FILE_PATH = "dataset.csv"
-
-# Page Title
 st.markdown("""
-    <h1 style="text-align:center; color:yellow;">Hotel Data Set</h1>
+    <h1 style="text-align:center; color:orange;">Hotel Data Analysis & Machine Learning</h1>
 """, unsafe_allow_html=True)
 
-# Sidebar Navigation
-
-page = ["Home", "Hotel Data", "Simple-EDA"]
-
-# Load dataset into session state if not already loaded
-if "dataset" not in st.session_state:
-    if os.path.exists(DATA_FILE_PATH):
-        st.session_state["dataset"] = pd.read_csv(DATA_FILE_PATH)
-    else:
-        st.session_state["dataset"] = None
-
-# Home Page
-if page == "Home":
-    st.title("Welcome to the Hotel Data App!")
-
-    # Add your new section for "Hotel Data Analysis & Machine Learning"
-    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.</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>
-        #### 💼 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)
-
-    # st.info("Please upload a dataset to get started.")
-elif page == "Hotel Data":
-    import pages.Hotel_Data
-elif page == "Simple-EDA":
-    import pages.Simple_EDA
-
-    # # File uploader to upload a new dataset
-    # uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
-    # if uploaded_file is not None:
-    #     # Read and save the uploaded dataset
-    #     df = pd.read_csv(uploaded_file)
-    #     df.to_csv(DATA_FILE_PATH, index=False)
-    #     st.session_state["dataset"] = df
-
-    #     st.success("Dataset uploaded and saved permanently!")
-    #     st.subheader("Uploaded Dataset Preview:")
-    #     st.write(df.head())
-
-# # Page 1: Dataset Overview
-# elif page == "Page 1":
-#     st.title("Page 1: Dataset Overview")
-
-#     # Access dataset from session state
-#     df = st.session_state.get("dataset")
-
-#     if df is not None:
-#         st.subheader("Dataset Preview:")
-#         st.write(df.head())
-
-#         st.subheader("Dataset Description:")
-#         st.write(df.describe())
-
-#         st.subheader("Dataset Shape (Rows, Columns):")
-#         st.write(df.shape)
-#     else:
-#         st.warning("No dataset found. Please upload a dataset on the Home page.")
+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.
 
-# # Page 2: Data Analysis
-# elif page == "Page 2":
-#     st.title("Page 2: Data Analysis")
+#### 🤖 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>
 
-#     # Access dataset from session state
-#     df = st.session_state.get("dataset")
+#### 📈 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>
 
-#     if df is not None:
-#         st.subheader("Basic Analysis:")
-#         st.write(f"Number of Rows: {df.shape[0]}")
-#         st.write(f"Number of Columns: {df.shape[1]}")
-#         st.write("Column Names:", list(df.columns))
+#### 💼 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>
 
-#         # Add any specific analysis or visualization here
-#     else:
-#         st.warning("No dataset found. Please upload a dataset on the Home page.")
+#### 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)