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Update pages/Life_cycle_of_ML.py
Browse files- pages/Life_cycle_of_ML.py +120 -121
pages/Life_cycle_of_ML.py
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import streamlit as st
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""
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st.markdown(html_content, unsafe_allow_html=True)
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import streamlit as st
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# Function to display lifecycle descriptions
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def display_lifecycle_stage(stage_name, description):
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st.subheader(stage_name)
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st.write(description)
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# Page Title
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st.title("Machine Learning Life Cycle")
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# Markdown Diagram
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st.markdown(
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"""
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```
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+-----------------------+
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| Problem Statement |
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+-----------------------+
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v
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+-----------------------+
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| Data Collection |
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+-----------------------+
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v
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+-----------------------+
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| Simple EDA |
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+-----------------------+
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v
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+-----------------------+
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| Data Preprocessing |
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+-----------------------+
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v
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+-----------------------+
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| EDA |
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+-----------------------+
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v
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+-----------------------+
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| Feature Engineering |
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+-----------------------+
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v
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+-----------------------+
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| Training |
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+-----------------------+
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v
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+-----------------------+
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| Testing |
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+-----------------------+
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v
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+-----------------------+
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| Deployment |
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+-----------------------+
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v
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+-----------------------+
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| Monitoring |
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+-----------------------+
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```
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"""
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)
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# Buttons for each stage
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col1, col2 = st.columns(2)
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with col1:
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if st.button("Problem Statement"):
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display_lifecycle_stage(
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"Problem Statement",
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"Defining the problem and setting objectives for the machine learning project.",
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)
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if st.button("Simple EDA"):
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display_lifecycle_stage(
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"Simple EDA",
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"Performing initial exploratory data analysis to understand data distribution and trends.",
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)
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if st.button("EDA"):
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display_lifecycle_stage(
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"EDA",
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"Detailed exploratory data analysis for deeper insights into data patterns.",
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)
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if st.button("Training"):
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display_lifecycle_stage(
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"Training",
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"Fitting the model using the training dataset to learn patterns and relationships.",
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)
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if st.button("Deployment"):
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display_lifecycle_stage(
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"Deployment",
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"Deploying the trained model to production for real-world use.",
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)
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with col2:
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if st.button("Data Collection"):
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display_lifecycle_stage(
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"Data Collection",
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"Gathering the data required for the machine learning project.",
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)
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if st.button("Data Preprocessing"):
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display_lifecycle_stage(
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"Data Preprocessing",
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"Cleaning and transforming the data to prepare it for analysis.",
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)
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if st.button("Feature Engineering"):
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display_lifecycle_stage(
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"Feature Engineering",
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"Creating new features or modifying existing ones to improve model performance.",
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)
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if st.button("Testing"):
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display_lifecycle_stage(
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"Testing",
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"Evaluating the model's performance using a separate testing dataset.",
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)
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if st.button("Monitoring"):
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display_lifecycle_stage(
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"Monitoring",
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"Monitoring the deployed model's performance and maintaining its accuracy.",
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)
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