Home.py

import streamlit as st
import pandas as pd
import numpy as np



st.markdown("""
    <h1 style="text-align:center; color:red;">Electronics Sales Analysis & Machine Learning Model</h1>
""", unsafe_allow_html=True)
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    """
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    """, 
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st.markdown("""
## Project Title: 📱Consumer Electronics Sales | EDA + Model 💻:
##### 📊 Data Exploration and Preprocessing:
- <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 *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 **Log-loss score**, depending on the task.</span>
- <span style="font-size:20px;">Employing techniques like **hyperparameter tuning** and **cross-validation** for optimization.</span>
##### 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
st.subheader("Hotel Data Analysis Model Creation Flow")
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