Create Machine learning Life cycle.py

pages/Machine learning Life cycle.py

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+import streamlit as st
+
+# Title
+st.title("Machine Learning Lifecycle")
+
+# Buttons as the flowchart
+steps = {
+    "Problem Statement": """
+        - Define the objective and understand the business requirements.
+        - Identify the type of problem (e.g., classification, regression, etc.).
+        - Frame the problem statement to align with the project goals.
+    """,
+    "Data Collection": """
+        - Check if data is available on websites like Kaggle or through APIs.
+        - Explore options like web scraping or manually creating data.
+        - Ensure the data is sufficient to train the model.
+    """,
+    "Exploratory Data Analysis (EDA)": """
+        - Assess data quality and identify missing values or anomalies.
+        - Use descriptive statistics (e.g., `df.info()`) to summarize data.
+        - Handle missing values, identify data types, and explore relationships.
+    """,
+    "Data Preprocessing": """
+        - Clean the data: Handle missing values, remove duplicates, and fix outliers.
+        - Normalize or standardize data for consistency.
+        - Transform raw data into a cleaned and usable format.
+    """,
+    "Feature Engineering": """
+        - Create or select relevant features to improve model performance.
+        - Extract meaningful information and engineer new features if required.
+    """,
+    "Model Training": """
+        - Train the model using appropriate algorithms (e.g., y = f(x)).
+        - Use optimization techniques to adjust performance during training.
+        - Regularly evaluate to avoid overfitting or underfitting.
+    """,
+    "Model Evaluation": """
+        - Test the model on validation or test datasets.
+        - Evaluate using metrics like accuracy, precision, recall, F1-score, etc.
+        - Check if the model meets the problem statement's requirements.
+    """,
+    "Model Deployment": """
+        - Deploy the model into a production environment or integrate it with a web app.
+        - Provide users access to predictions by exposing the model through APIs.
+    """,
+    "Monitoring and Updating": """
+        - Continuously monitor model performance and compare accuracy over time.
+        - Update the model to handle new data or changing patterns.
+        - Retrain and replace models as necessary to ensure consistent performance.
+    """
+}
+
+# Display buttons and corresponding descriptions
+for step, description in steps.items():
+    if st.button(step):
+        st.subheader(step)
+        st.write(description)