pages/Life_cycle_of_ML.py

## Shapes Representing the ML Life Cycle

<svg width="600" height="300">
  <!-- Problem Definition (Rectangle) -->
  <rect x="50" y="50" width="150" height="50" fill="#FFB6C1" stroke="#000" />
  <text x="75" y="80" fill="#000" font-size="14">Problem Definition</text>
  
  <!-- Data Collection (Circle) -->
  <circle cx="300" cy="75" r="40" fill="#ADD8E6" stroke="#000" />
  <text x="270" y="80" fill="#000" font-size="14">Data Collection</text>
  
  <!-- Data Preprocessing (Ellipse) -->
  <ellipse cx="500" cy="75" rx="80" ry="40" fill="#90EE90" stroke="#000" />
  <text x="445" y="80" fill="#000" font-size="14">Data Preprocessing</text>
  
  <!-- Arrows between Problem Definition and Data Collection -->
  <line x1="200" y1="75" x2="260" y2="75" stroke="#000" marker-end="url(#arrow)" />
  
  <!-- Model Building (Rectangle) -->
  <rect x="50" y="200" width="150" height="50" fill="#FFD700" stroke="#000" />
  <text x="75" y="230" fill="#000" font-size="14">Model Building</text>
  
  <!-- Evaluation (Circle) -->
  <circle cx="300" cy="225" r="40" fill="#FF7F50" stroke="#000" />
  <text x="275" y="230" fill="#000" font-size="14">Evaluation</text>
  
  <!-- Deployment (Rectangle) -->
  <rect x="450" y="200" width="150" height="50" fill="#9370DB" stroke="#000" />
  <text x="475" y="230" fill="#000" font-size="14">Deployment</text>
  
  <!-- Arrows between Model Building, Evaluation, and Deployment -->
  <line x1="125" y1="250" x2="260" y2="225" stroke="#000" marker-end="url(#arrow)" />
  <line x1="340" y1="225" x2="450" y2="225" stroke="#000" marker-end="url(#arrow)" />
  
  <!-- Define arrow marker -->
  <defs>
    <marker id="arrow" viewBox="0 0 10 10" refX="5" refY="5" markerWidth="4" markerHeight="4" orient="auto">
      <polygon points="0,0 10,5 0,10" fill="#000" />
    </marker>
  </defs>
</svg>
"""

# Render HTML content in Streamlit
st.markdown(html_content, unsafe_allow_html=True)


# Render the SVG
st.markdown(html_content, unsafe_allow_html=True)

# Interactive buttons for stages
st.subheader("Stages in ML Life Cycle")
if st.button("Problem Statement"):
    st.write("Understanding the problem and setting objectives for the ML model.")
if st.button("Data Collection"):
    st.write("Gathering relevant data for model training.")
if st.button("Simple EDA"):
    st.write("Initial analysis to understand the dataset's basic properties.")
if st.button("Data Preprocessing"):
    st.write("Cleaning the data to ensure it's in a usable format.")
if st.button("EDA"):
    st.write("Deeper analysis to gain insights and find patterns in the data.")
if st.button("Feature Engineering"):
    st.write("Creating new features or modifying existing ones to improve model performance.")
if st.button("Training"):
    st.write("Training machine learning models using the processed data.")
if st.button("Testing"):
    st.write("Evaluating the trained model using a test set to assess its performance.")
if st.button("Deploying"):
    st.write("Deploying the model to a production environment.")
if st.button("Monitoring"):
    st.write("Continuously monitoring the model's performance in the production environment.")