Update pages/3_Life cycle of ML.py

pages/3_Life cycle of ML.py

@@ -3,15 +3,15 @@ import streamlit as st
 # Function for displaying lifecycle steps with details
 def display_lifecycle_step(step_name):
     steps = {
-        "Problem Statement": "The first step is to define the problem clearly. It involves identifying the objective of the machine learning model, whether it's classification, regression, or another type of problem.",
-        "Data Collection": "Data is collected from various sources like APIs, databases, or web scraping. It's crucial to have enough data for the model to learn effectively.",
-        "Simple EDA": "At this stage, we do a simple Exploratory Data Analysis (EDA) to understand the data better, identify patterns, and detect anomalies or missing values.",
-        "Data Pre-Processing": "This step includes cleaning the data: handling missing values, encoding categorical variables, normalizing numerical features, and splitting the data into training and testing sets.",
-        "EDA": "A deeper EDA helps to identify relationships between features, correlation, and insights that guide feature engineering and model selection.",
-        "Feature Engineering": "In this step, you create new features or modify existing features to improve the model's performance. Techniques like scaling, encoding, or creating polynomial features are common.",
-        "Model Training": "You select an appropriate algorithm and train the model using the training dataset. Common algorithms include decision trees, linear regression, or neural networks.",
-        "Testing": "After training the model, you test it using unseen data to assess its performance using evaluation metrics like accuracy, precision, recall, or mean squared error.",
-        "Deployment and Maintenance": "Once the model is deployed, it’s integrated into an application or system, and continuous monitoring is required to ensure it performs well and is updated as needed."
+        "Problem Statement": "Objective of the project.",
+        "Data Collection": "Data is collected from various sources like APIs, databases, or web scraping atlast we've to go with manual collection.",
+        "Simple EDA": "Describing the quality of the data.",
+        "Data Pre-Processing": "It is a technique by which we can convert raw data into pre-procesed data --->1.Clean the data  2.Transform the data.",
+        "EDA": "Transforming insights into a clean dataset and providing proper visualizations.",
+        "Feature Engineering": "Creating and analyzing features and labels.",
+        "Model Training": "Training the machine about relationships between features and labels.",
+        "Testing": "Testing how efficiently the machine learned.",
+        "Deployment and Maintenance": "Deploying the machine to the client and ensuring maintenance for accurate results."
     }
     st.write(f"### {step_name}")
     st.write(steps[step_name])
@@ -26,20 +26,16 @@ steps_list = [
     "Model Training", "Testing", "Deployment and Maintenance"
 ]
 
-# Displaying the lifecycle steps as buttons
 selected_step = st.sidebar.radio(
     "Choose a step in the ML lifecycle:",
     steps_list,
-    index=0  # Default selected step
+    index=0  
 )
 
-# Main content area displaying the corresponding step details
 st.title("Machine Learning Lifecycle")
 
-# Show corresponding step details when a button is selected
 display_lifecycle_step(selected_step)
 
-# Add custom styling to make it look like a circular structure
 st.markdown("""
     <style>
         .stApp {
@@ -66,7 +62,6 @@ st.markdown("""
 
 import webbrowser
 
-# Function to display detailed content for "Data Collection" page
 def data_collection_page():
     st.write("### What is Data?")
     st.write("""
@@ -81,13 +76,11 @@ def data_collection_page():
     3. **Semi-Structured Data**: Data that has some organizational properties but isn't fully structured (e.g., JSON, XML).
     """)
 
-    # Button to select Structured Data
     selected_data_type = st.radio("Choose Data Type", ["Structured Data", "Unstructured Data", "Semi-Structured Data"])
 
     if selected_data_type == "Structured Data":
         display_structured_data_info()
 
-# Function to display structured data information and formats
 def display_structured_data_info():
     st.write("### Structured Data")
     st.write("Structured data is data that is highly organized and stored in a fixed format, like tables, rows, and columns.")
@@ -181,15 +174,12 @@ def open_code_example(data_format):
     if link:
         webbrowser.open_new_tab(link)
 
-# Main Streamlit app
 def main():
     st.title("Machine Learning Life Cycle")
     st.sidebar.title("ML Life Cycle Navigation")
     
-    # Button to go to "Data Collection" page
     if st.sidebar.button("Data Collection"):
         data_collection_page()
 
-# Run the main function to start the app
 if __name__ == "__main__":
     main()