Update Home.py

Home.py

@@ -16,20 +16,22 @@ st.markdown(
 # Project description
 st.markdown(
     """
-    <div style="text-align:justify; font-size:20px; color:white;">
-        ## Project Title: 📱Consumer Electronics Sales | EDA + Model 💻:
-        ##### 📊 Data Exploration and Preprocessing:
-        - 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.
-        - Analyzing trends in **Product Categories**, **Brands**, **Prices**, **CustomerAge**, etc., to identify influential factors.
-        ##### 🤖 Predictive Modeling:
-        - **Target Variable**: Predicting key metrics like *PurchaseIntent*.
-        - **Model Selection**: Building ML models such as **KNN**, **Logistic Regression**, and **Support Vector Machine** for classification tasks.
-        - **Feature Engineering**: Extracting insights from **ProductCategory**, **ProductBrand**, and label encoding.
-        ##### 📈 Model Evaluation:
-        - Comparing model performance using metrics like **accuracy**, **F1 score**, or **Log-loss score**, depending on the task.
-        - Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.
-        ##### By integrating **machine learning** with **data analysis**, this project empowers the Electronics market to enhance customer satisfaction, optimize pricing strategies according to purchase intent, and maximize profitability.
-    </div>
+    ## Project Title: 📱Consumer Electronics Sales | EDA + Model 💻:
+    
+    ##### 📊 Data Exploration and Preprocessing:
+    - 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.
+    - Analyzing trends in **Product Categories**, **Brands**, **Prices**, **CustomerAge**, etc., to identify influential factors.
+    
+    ##### 🤖 Predictive Modeling:
+    - **Target Variable**: Predicting key metrics like *PurchaseIntent*.
+    - **Model Selection**: Building ML models such as **KNN**, **Logistic Regression**, and **Support Vector Machine** for classification tasks.
+    - **Feature Engineering**: Extracting insights from **ProductCategory**, **ProductBrand**, and label encoding.
+    
+    ##### 📈 Model Evaluation:
+    - Comparing model performance using metrics like **accuracy**, **F1 score**, or **Log-loss score**, depending on the task.
+    - Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.
+    
+    ##### By integrating **machine learning** with **data analysis**, this project empowers the Electronics market to enhance customer satisfaction, optimize pricing strategies according to purchase intent, and maximize profitability.
     """, 
     unsafe_allow_html=True
 )