Rename Hotel Data Card.py to Home.py

Hotel Data Card.py → Home.py

@@ -27,3 +27,108 @@ st.markdown("""
 
 #### 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
+
+# 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>
+""", unsafe_allow_html=True)
+
+# Sidebar Navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["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.")
+
+# # Page 2: Data Analysis
+# elif page == "Page 2":
+#     st.title("Page 2: Data Analysis")
+
+#     # Access dataset from session state
+#     df = st.session_state.get("dataset")
+
+#     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))
+
+#         # Add any specific analysis or visualization here
+#     else:
+#         st.warning("No dataset found. Please upload a dataset on the Home page.")
+