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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# App Title
st.title("Unsupervised Learning: K-Means Clustering")
# Sidebar Section: Tab for downloading a sample dataset
st.sidebar.subheader("Sample Dataset")
st.sidebar.write("Download a sample dataset to test the app. This sample contains two numerical features for demonstration purposes.")
sample_data = {
"Feature1": [1.0, 1.5, 3.0, 5.0, 3.5, 4.5, 3.5],
"Feature2": [1.0, 2.0, 4.0, 7.0, 5.0, 5.0, 4.5],
}
sample_df = pd.DataFrame(sample_data)
sample_csv = sample_df.to_csv(index=False)
st.sidebar.download_button(
label="Download Sample CSV",
data=sample_csv,
file_name="sample_data.csv",
mime="text/csv"
)
# Main Section: Upload dataset
st.header("Step 1: Upload Your Dataset")
st.write("Upload a CSV file containing your data. Ensure it includes numerical features for clustering.")
uploaded_file = st.file_uploader("Upload your dataset (CSV format)", type="csv")
if uploaded_file:
data = pd.read_csv(uploaded_file)
st.write("Preview of the uploaded data:")
st.dataframe(data)
# Step 2: Select features for clustering
st.subheader("Step 2: Feature Selection")
st.write("Select the numerical features you want to use for clustering.")
selected_features = st.multiselect(
"Select features for clustering:", data.columns.tolist()
)
if selected_features:
X = data[selected_features]
# Step 3: Configure clustering parameters
st.subheader("Step 3: Clustering Configuration")
st.write("Choose the number of clusters you want to create using the slider below.")
n_clusters = st.slider("Select the number of clusters:", min_value=2, max_value=10, value=3)
# Apply K-Means Clustering
model = KMeans(n_clusters=n_clusters, random_state=42)
cluster_labels = model.fit_predict(X)
# Step 4: Add cluster labels to the dataset
data['Cluster'] = cluster_labels
st.write("Clustered Data:")
st.dataframe(data)
# Step 5: Visualize the clusters
st.subheader("Step 5: Cluster Visualization")
st.write("Visualize the clustering results. Select at least two features for plotting.")
if len(selected_features) >= 2:
fig, ax = plt.subplots()
scatter = ax.scatter(
X[selected_features[0]],
X[selected_features[1]],
c=cluster_labels,
cmap="viridis",
s=50
)
ax.set_xlabel(selected_features[0])
ax.set_ylabel(selected_features[1])
ax.set_title("K-Means Clustering")
legend = ax.legend(*scatter.legend_elements(), title="Clusters")
ax.add_artist(legend)
st.pyplot(fig)
else:
st.warning("Select at least 2 features for visualization.")
# Step 6: Download the clustered data
st.subheader("Step 6: Download Clustered Data")
st.write("Download the dataset with the cluster labels added.")
csv = data.to_csv(index=False)
st.download_button(
label="Download CSV",
data=csv,
file_name="clustered_data.csv",
mime="text/csv"
)
else:
st.warning("Please select features for clustering.")
else:
st.info("Awaiting file upload. Use the sample dataset in the sidebar if you don’t have a file.")
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