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52e54aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import streamlit as st
import streamlit.components.v1 as components
import os # Import the os module
import pandas as pd
import zipfile
from sidebar_logo import add_sidebar_logo, load_css
# Set page configuration
st.set_page_config(
layout="wide"
)
add_sidebar_logo()
load_css()
st.markdown("""
<div style="
font-size: 1.9rem;
font-weight: 800;
background: linear-gradient(135deg, #a78bfa, #818cf8, #f472b6);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-family: 'Poppins', sans-serif;
">
📓 4. Support Vector Machine (SVM)
</div>
""", unsafe_allow_html=True)
# Define the file path with regular spaces
path_to_html = "Support_Vector_Machine_(SVM).html"
# Check if the HTML file exists
if not os.path.exists(path_to_html):
st.error("HTML file not found!")
else:
# Read HTML content
with open(path_to_html, 'r', encoding='utf-8') as f:
html_data = f.read()
# Show HTML content
st.header(":violet[Support Vector Machine (SVM)]")
st.markdown("""Hi guys. Today we are going to learn about 'Support Vector Machine' algorithm. SVM is a special kind of an algorithm which can be used to solve both classification & regression problems.
Today however, we are going to see how it goes ahead and does classification on a bank loan data. The bank loan zip dataset contains both risk train & risk test data. The data here is
split already into train & test data, so we are not going to perform 'Train-test split' in today's session. The limitation of KNN is the fact that it works only on linearly separable data.
However, if we want to work on non-linearly separable data, by this I mean data which is distributed/scattered in such a way that it cannot be separated using a straight line then we make
use of SVM. SVM operates well on sporadic/concentric data as it can properly separate data by using a hyperplane. Similar to KNN, we have hyperparameters here as well (namely kernel, C & gamma)
which can help us tune the model to achieve even better results. For now, I would request you to please download the 'risk_analytics' zip datatset that contains both the training and testing data
on which we are going to perform SVM. Let's get started.""")
def create_zip():
with zipfile.ZipFile('risk_analytics.zip', 'w') as zipf:
for root, dirs, files in os.walk('risk_analytics'):
for file in files:
zipf.write(os.path.join(root, file), file)
with open("risk_analytics.zip", "rb") as f:
data = f.read()
return data
# Create a download button for the zip file
button_label_zip = ":violet[Download risk analytics dataset Zip]"
button_download_zip = st.download_button(label=button_label_zip, data=create_zip(), file_name='risk_analytics.zip', mime='application/zip')
st.write("---")
st.components.v1.html(html_data, width=1000, height=12300)
def download_notebook():
with open("Support_Vector_Machine_(SVM).ipynb", "rb") as f:
data = f.read()
return data
# Create a download button for the notebook
st.write("----")
st.write("To download 'Support Vector Machine (SVM)' Jupyter notebook click on the button below.")
button_label = ":violet[Download Jupyter Notebook]"
button_download = st.download_button(label=button_label, data=download_notebook(), file_name="Support_Vector_Machine_(SVM).ipynb", mime='application/x-ipynb+json')
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