Update Home.py

Home.py

@@ -5,7 +5,7 @@ import numpy as np
 
 
 st.markdown("""
-    <h1 style="text-align:center; color:orange;">Electronics Sales Analysis & Machine Learning Model</h1>
+    <h1 style="text-align:center; color:red;">Electronics Sales Analysis & Machine Learning Model</h1>
 """, unsafe_allow_html=True)
 st.markdown(
     """
@@ -14,21 +14,21 @@ st.markdown(
     unsafe_allow_html=True
 )
 st.markdown("""
-## Predicting Customer Preferences and Optimizing Pricing:
+## Project Title: 📱Consumer Electronics Sales | EDA + Model 💻:
 ##### 📊 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 style="font-size:20px;">Preparing data by encoding categorical features like "ProductCategory" and "ProductBrand" and scaling numerical data such as "price" and "rating", as the dataset has minimal outliers or missing values.</span>
+- <span style="font-size:20px;">Analyzing trends in **Product Categories**, **Brands**, **Prices**, **CustomerAge** etc., 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>
+- <span style="font-size:20px;">**Target Variable**: Predicting key metrics like *PurchaseIntent*</span>
+- <span style="font-size:20px;">**Model Selection**: Building ML models such as **KNN**, "Logistic Regression" and "Support Vector Machine"** for classification task</span>
+- <span style="font-size:20px;">**Feature Engineering**: Extracting insights from **ProductCategory**, **ProductBrand** and label 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;">Comparing model performance using metrics like **accuracy**, **F1 score**, or **Log-loss score**, 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.
+##### By integrating **machine learning** with **data analysis**, this project empowers Electronics market to enhance customer satisfaction, optimize pricing strategies according to purchase intent, and maximize profitability.
 """, unsafe_allow_html=True)
 
 # # Display an image from a file
@@ -63,7 +63,7 @@ st.markdown(
         }}
         /* Styling the content to ensure text visibility */
         .stMarkdown {{
-            color: black;  /* White text to ensure visibility */
+            color: white;  /* White text to ensure visibility */
             font-size: 30px;  /* Adjust font size for better readability */
         }}
     </style>