Create app.py

app.py

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+import streamlit as st
+
+st.set_page_config(page_title="Decision Tree", page_icon="✨", layout="wide")
+
+st.markdown("<h1 style='color: #003366;'>Understanding Decision Tree</h1>", unsafe_allow_html=True)
+st.image("https://cdn-uploads.huggingface.co/production/uploads/673f6e448c5214b832b3724c/zcCYCBE0x2U-nrPU5hBqc.png")
+
+# Introduction
+st.write("""
+       - A Decision Tree is a popular and powerful supervised machine learning algorithm used for both classification and regression tasks. 
+       - It models decisions and their possible consequences as a tree-like structure, similar to a flowchart.
+       - In a decision tree, data is split into smaller subsets based on certain conditions (called features), and this process continues recursively, forming a tree with nodes (where decisions are made) and leaves (which represent the final output or decision).
+""")
+st.markdown("<h2 style='color: #003366;'>How Decision Tree Works</h2>", unsafe_allow_html=True)
+
+st.subheader("Training Phase")
+st.write("""
+- Training a decision tree involves building the tree structure by splitting the dataset into subsets based on feature values, with the goal of minimizing uncertainty or impurity at each step.
+- Start at the root node with the full training dataset.
+- Choose the best feature to split the data. This is done using a criterion like:
+- Gini Impurity (used in classification)
+- Entropy / Information Gain (used in classification)
+- Mean Squared Error (MSE) (used in regression)
+- Split the dataset based on the selected feature's values.
+- Repeat the process recursively for each child node, until:
+- A stopping condition is met (like max depth or minimum samples per leaf), or The node is pure (all data points have the same label).
+""")
+
+