Update pages/Life Cycle Of Machine Learning.py

pages/Life Cycle Of Machine Learning.py

@@ -41,4 +41,35 @@ if st.button("Data Collection"):
     - **4.Ensure Quality**
        - Data should be accurate, relevant, and complete. Remove any errors or inconsistencies.
        - Example: For customer churn prediction, make sure there are no missing customer details like age or usage data.
-      """)
+      """)
+
+if st.button("Simple EDA"):
+    st.write("""
+    **EDA (Exploratory Data Analysis) is the process of exploring your data to understand its structure, patterns, and insights before building a machine learning model.Think of it as getting to know your data better!**
+    
+    **Steps for Simple EDA:**
+    - **1.Understand the Data**
+    Look at the data to understand its structure and contents.
+        - Example: If you have a dataset of students' marks, check columns like Name, Math Marks, Science Marks, and Grade.
+    - **2.Check the Size of the Data**
+    Find out how many rows (data points) and columns (features) are in the dataset.
+        - Example: Your student dataset might have 500 rows (students) and 5 columns (attributes).
+    - **3.View the First Few Rows**
+    Look at the top 5-10 rows to get a snapshot of the data.
+        - Example: Check if the columns contain relevant information like scores and grades.
+    - **4.Summarize the Data**
+    Generate basic statistics for numerical data, such as:
+        - Mean: Average of a column (e.g., average math marks).
+        - Minimum and Maximum: Lowest and highest values (e.g., lowest and highest scores).
+        - Count: Number of non-missing values (e.g., total students who took the test).
+    - **5.Handle Missing Data**
+    Identify and deal with missing or incomplete values.
+        - Example: If some students are missing marks, decide to either fill them with an average or remove those rows.
+    - **6.Check Data Distribution**
+    Visualize how data is spread using graphs:
+        - Histograms: Show the distribution of scores (e.g., most students scored 70-80 in math).
+        - Boxplots: Highlight outliers and data spread.
+    - **7.Identify Relationships**
+    Check how different features relate to each other using scatter plots or correlation matrices.
+        - Example: Do students with high math marks also score high in science?
+    """)