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("""
""", unsafe_allow_html=True)
# Render the title
st.markdown('Life Cycle of End-to-End ML Project
', unsafe_allow_html=True)
# Add the description above the circular layout
st.markdown(
"""
- In this page, I will take you through the 10 crucial steps involved in the life cycle of a Machine Learning project.
- Each step plays a significant role in ensuring the success of the project.
""",
unsafe_allow_html=True,
)
# Render the circular layout with buttons that redirect to different pages
st.markdown(
"""
""",
unsafe_allow_html=True
)
st.markdown("
", 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.
""")