pages/3Life Cycle of Machine Learning Project.py

import streamlit as st

# Page configuration
st.set_page_config(
    page_title="Life Cycle of Machine Learning Project",
    page_icon="🚀",
    layout="wide"
)

# Global CSS for consistent styling
st.markdown(""" 
    <style>
    h1 {
        text-align: center;
        color: #BB3385;
        margin-top: 20px;
        margin-bottom: 10px;
    }
    .description {
        text-align: center;
        font-size: 18px;
        margin-bottom: 40px;
        color: #333333;
    }
    .circle-container {
        display: flex;
        justify-content: center;
        align-items: center;
        margin-top: 90px;
        position: relative;
    }
    .circle {
        position: relative;
        width: 600px; /* Increased size from 400px */
        height: 600px; /* Increased size from 400px */
        border-radius: 50%;
        display: flex;
        justify-content: center;
        align-items: center;
        background: transparent;
    }
    .steps {
        position: absolute;
        width: 100%;
        height: 100%;
        display: flex;
        justify-content: center;
        align-items: center;
    }
    .step {
        position: absolute;
        width: 150px; /* Increased size from 150px */
        height: 60px; /* Increased size from 60px */
        font-size: 13px; /* Slightly larger font size */
        color: black;
        font-weight: bold; /* Bold text */
        border-radius: 30px;
        display: flex;
        justify-content: center;
        align-items: center;
        text-align: center;
        transform-origin: 50% 50%;
        box-shadow: 2px 2px 6px rgba(0, 0, 0, 0.2);
        background-color: rgba(255, 255, 255, 0.9);
        cursor: pointer;
    }
    #step1 { transform: rotate(0deg) translateX(300px) rotate(-0deg); background-color: #FFCCCB; } /* Light Red */
    #step2 { transform: rotate(36deg) translateX(300px) rotate(-36deg); background-color: #FFD700; } /* Gold */
    #step3 { transform: rotate(72deg) translateX(300px) rotate(-72deg); background-color: #90EE90; } /* Light Green */
    #step4 { transform: rotate(108deg) translateX(300px) rotate(-108deg); background-color: #ADD8E6; } /* Light Blue */
    #step5 { transform: rotate(144deg) translateX(300px) rotate(-144deg); background-color: #FFB6C1; } /* Light Pink */
    #step6 { transform: rotate(180deg) translateX(300px) rotate(-180deg); background-color: #FFA07A; } /* Light Salmon */
    #step7 { transform: rotate(216deg) translateX(300px) rotate(-216deg); background-color: #D8BFD8; } /* Thistle */
    #step8 { transform: rotate(252deg) translateX(300px) rotate(-252deg); background-color: #FFFFE0; } /* Light Yellow */
    #step9 { transform: rotate(288deg) translateX(300px) rotate(-288deg); background-color: #E0FFFF; } /* Light Cyan */
    #step10 { transform: rotate(324deg) translateX(300px) rotate(-324deg); background-color: #F5DEB3; } /* Wheat */
    </style>
""", unsafe_allow_html=True)

# Render the title
st.markdown('<h1>Life Cycle of End-to-End ML Project</h1>', unsafe_allow_html=True)

# Add the description above the circular layout
st.markdown(
    """
    <div class="description">
    - In this page, I will take you through the 10 crucial steps involved in the life cycle of a Machine Learning project.<br>
    - Each step plays a significant role in ensuring the success of the project.
    </div>
    """,
    unsafe_allow_html=True,
)

# Render the circular layout with buttons that redirect to different pages
st.markdown(
    """
    <div class="circle-container">
        <div class="circle">
            <div class="steps">
                <a href="/problem_statement" target="_self" class="step" id="step9">1. Problem Statement</a>
                <a href="/Data_Collection" target="_self" class="step" id="step10">2. Data Collection</a>
                <a href="/simple_eda" target="_self" class="step" id="step1">3. Simple EDA</a>
                <a href="/data_preprocessing" target="_self" class="step" id="step2">4. Data Preprocessing</a>
                <a href="/eda" target="_self" class="step" id="step3">5. EDA</a>
                <a href="/feature_engineering" target="_self" class="step" id="step4">6. Feature Engineering</a>
                <a href="/model_training" target="_self" class="step" id="step5">7. Model Training</a>
                <a href="/model_testing" target="_self" class="step" id="step6">8. Model Testing</a>
                <a href="/model_deployment" target="_self" class="step" id="step7">9. Model Deployment</a>
                <a href="/monitoring" target="_self" class="step" id="step8">10. Monitoring</a>
            </div>
        </div>
    </div>
    """,
    unsafe_allow_html=True
)


st.markdown("<br><br><br>", unsafe_allow_html=True)

# Additional content
st.markdown("""
**Problem Statement**:
   - Clearly define the business or research problem.
   - Identify the objectives and success criteria.

**Data Collection**:
   - Collect data from reliable sources.
   - Ensure the data is relevant and sufficient for the problem.

**Simple EDA (Exploratory Data Analysis)**:
   - Perform initial data exploration to understand basic patterns.
   - Quickly check for missing or inconsistent data.

**Data Preprocessing**:
   - Clean the data by handling missing values and correcting errors.
   - Transform data types and normalize or scale features as needed.

**EDA**:
   - Perform detailed analysis to uncover insights.
   - Visualize data relationships using charts and graphs.

**Feature Engineering**:
   - Create new features that enhance model performance.
   - Select or transform existing features to improve relevance.

**Model Training**:
   - Choose appropriate machine learning algorithms.
   - Train models and optimize hyperparameters for best performance.

**Model Testing**:
   - Evaluate the model's performance with test data.
   - Perform cross-validation and analyze performance metrics.

**Model Deployment**:
   - Deploy the model into a production environment.
   - Ensure the model can handle real-time data and user requests.

**Monitoring**:
   - Continuously monitor the model's performance over time.
   - Update and retrain the model to maintain accuracy and relevance.

""")