home.py

import streamlit as st
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
import random
import requests
st.markdown("""
    <style>
    /* Set a soft background color */
    body {
        background-color: #eef2f7;
    }
    /* Style for main title */
    h1 {
        color: #00FFFF;
        font-family: 'Roboto', sans-serif;
        font-weight: 700;
        text-align: center;
        margin-bottom: 25px;
    }
    /* Style for headers */
    h2 {
        color: #FFFACD;
        font-family: 'Roboto', sans-serif;
        font-weight: 600;
        margin-top: 30px;
    }
    
    /* Style for subheaders */
     h3 {
        color: #ba95b0;
        font-family: 'Roboto', sans-serif;
        font-weight: 500;
        margin-top: 20px;
    }
    .custom-subheader {
        color: #00FFFF;
        font-family: 'Roboto', sans-serif;
        font-weight: 600;
        margin-bottom: 15px;
    }
    /* Paragraph styling */
    p {
        font-family: 'Georgia', serif;
        line-height: 1.8;
        color: #FFFFFF; /* Darker text color for better visibility */
        margin-bottom: 20px;
    }
    /* List styling with checkmark bullets */
    .icon-bullet {
        list-style-type: none;
        padding-left: 20px;
    }
    .icon-bullet li {
        font-family: 'Georgia', serif;
        font-size: 1.1em;
        margin-bottom: 10px;
        color: #FFFFF0; /* Darker text color for better visibility */
    }
    .icon-bullet li::before {
        content: "✔️";
        padding-right: 10px;
        color: #b3b3ff;
    }
    /* Sidebar styling */
    .sidebar .sidebar-content {
        background-color: #ffffff;
        border-radius: 10px;
        padding: 15px;
    }
    .sidebar h2 {
        color: #495057;
    }
    </style>
    """, unsafe_allow_html=True)
st.title("Zero to Hero: Mastering Machine Learning from the Ground Up 🚀🤖📚")
from streamlit_lottie import st_lottie

def load_lottieurl(url: str):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

lottie_data_analysis = load_lottieurl("https://lottie.host/6e182649-61a6-4683-8680-5493855ac08a/G0pStmcS8T.json")

st_lottie(lottie_data_analysis, height=150, key="data")
st.subheader("About This App")
st.markdown("""
Welcome to the Machine Learning Roadmap App! 🚀

This app gives you a step-by-step guide to building and using machine learning models. It takes you through all the important stages of an ML project, starting from understanding the problem to keeping track of the model once it's being used.
The roadmap includes the following key steps:
<ul class="icon-bullet">
    <li>Problem Statement: Define the problem you're trying to solve and establish clear objectives.📝</li>
    <li>Collect the Data: Gather the necessary data to train your model. 📊</li>
    <li>Simple EDA: Perform a quick exploration of your data to get an initial understanding. 🔍</li>
    <li>Data Processing: Clean and preprocess your data to ensure it's ready for modeling. 🧹</li>
    <li>Original EDA: Dive deeper into the data through comprehensive exploratory data analysis. 📈</li>
    <li>Feature Engineering: Create meaningful features that will help your model perform better. 🛠️</li>
    <li>Testing the Model: Evaluate your model's performance using various metrics. 🧪</li>
    <li>Deployment: Deploy your trained model into a production environment.🚀</li>
    <li>Monitoring: Continuously monitor the model's performance in the real world to ensure it remains effective.👀</li>
</ul>
The app provides interactive visualizations, practical examples, and step-by-step instructions to guide you through the entire process.
""",unsafe_allow_html=True)
st.image("/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64c972774515835c4dadd754%2FQCg0WmIF90q6pHFfk6Ese.png%26quot%3B%3C%2Fspan%3E%2Cwidth%3D%3Cspan class="hljs-number">700)
st.subheader("👋 About the Author")
st.markdown("""Hi! I'm D Thoran Raj 😊, a passionate Machine Learning enthusiast 🤖 who loves learning and sharing knowledge about machine learning. I’ve spent a lot of time exploring the whole ML process – from understanding problems 🧐 to deploying models in real-world situations 🌍.
""",unsafe_allow_html=True)