import streamlit as st
import pandas as pd
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: #17a2b8;
    }
    /* Sidebar styling */
    .sidebar .sidebar-content {
        background-color: #ffffff;
        border-radius: 10px;
        padding: 15px;
    }
    .sidebar h2 {
        color: #495057;
    }
     /* Mobile specific styles */
    @media only screen and (max-width: 600px) {
        .stApp {
            background-color: #070109;
        }
        h1, h2 {
            font-size: 18px;  /* Adjust font size for mobile devices */
        }
    }
    </style>
    """, unsafe_allow_html=True)
st.title("STEPS INVOLVED IN DATA ANALYSIS")
st.markdown("""In this we undergo two scenarios. Now lets understand the both scenarios step-by-step.
""",unsafe_allow_html=True)
st.header("Scenario-1")
st.markdown("""In this scenario we will be having problem statement. According to problem statement the
analysis must be proceeded. Following steps are given below.
""",unsafe_allow_html=True)
st.subheader("Problem Statement:")
st.markdown("""In scenario-1 we will be given problem statement. By considering the
problem statement we must analyze the given problem statement perfectly. And we must take
required steps according to problem statement.
""",unsafe_allow_html=True)
st.subheader("Data Collection:")
st.markdown("""After analyzing the problem statement. We should collect the data according
to the problem statement. We should collect the data from structured data or unstructured
data or semi structured data. We should collect more data for accurate analysis. The collected
data will have some sort if impurities hence we will go for next process.
""",unsafe_allow_html=True)
st.subheader("SIMPLE EXPLORATORY DATA ANALYSIS:")
st.markdown(""" In order to check weather the collected data has any
impurities we will go for simple (EDA). Which will say weather the collected data has any
impurities or not. If the collected data doesn’t have any impurities then directly go for whole
(EDA) or else go for pre-processing
""",unsafe_allow_html=True)
st.subheader("PRE-PROCESSING:")
st.markdown(""" If the collected data has any sort of impurities then this raw data is being
cleaned. Pre-Processing involves Data Cleaning, Transforming the data etc.
""",unsafe_allow_html=True)
st.subheader("EXPLORATORY DATA ANALYSIS:")
st.markdown("""After Pre-Processing complete (EDA) is performed on data. By
once performing this (EDA) we will be getting the complete insights from the collected data.
""",unsafe_allow_html=True)
st.subheader("VISUALIZATION:")
st.markdown("""After the (EDA) we will be getting the insights these are represented by using
wide range of visualization technique’s. Or can be represented as a Dash Board.
""",unsafe_allow_html=True)
st.header("Scenario-2")
st.markdown("""In this scenario we will not be having any sort of problem statement . Hence we have our own
flexibility to create our own problem statement by analyzing the data. Following steps are
given below.
""",unsafe_allow_html=True)
st.header("Here 'DATA' is given")
st.subheader("SIMPLE EXPLORATORY DATA ANALYSIS:")
st.markdown("""In order to check weather the given data has any
impurities we will go for simple (EDA). Which will say weather the given data has any
impurities or not. If the given data doesn’t have any impurities then directly go for whole
(EDA) or else go for pre-processing.""",unsafe_allow_html=True)
st.subheader("PRE-PROCESSING:")
st.markdown(""" If the given data has any sort of impurities then this raw data is being
cleaned. Pre-Processing involves Data Cleaning, Transforming the data etc.""",unsafe_allow_html=True)
st.subheader("EXPLORATORY DATA ANALYSIS:")
st.markdown(""" After Pre-Processing complete (EDA) is performed on data. By
once performing this (EDA) we will be getting the complete insights from the given data. After
getting the clear knowledge on data. We have to create our own problem statement.""",unsafe_allow_html=True)
st.subheader("PROBLEM STATEMENT:")
st.markdown("""After creating our own problem statement we have to clearly analyze
our problem statement.""",unsafe_allow_html=True)
st.markdown("""After the above step we have to perform all the steps involved in scenario-1. And in this
scenario-2 we have our own liberty. Like we can remove some of the steps in scenario-1 if they
are not required. For example if the data is good and it is suitable for problem statement then
we can skip (simple(eda), pre-processing,(eda)) and go for visualization. If the data is not
suitable we can perform all the steps in scenario-1.""",unsafe_allow_html=True)