Create app.py

app.py

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+# Create a Streamlit app for data analysis
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans, DBSCAN
+from sklearn.metrics import silhouette_score
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import StandardScaler
+
+# Streamlit app
+st.title('Data Analysis with Streamlit')
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file is not None:
+    # Read the uploaded CSV file
+    df = pd.read_csv(uploaded_file)
+    st.write("Data loaded successfully.")
+    st.write(df.head())
+
+    # Exclude non-numeric columns for analysis
+    numeric_df = df.select_dtypes(include=[np.number])
+
+    # Standardize the data
+    scaler = StandardScaler()
+    scaled_data = scaler.fit_transform(numeric_df)
+
+    # PCA
+    pca = PCA(n_components=2)
+    pca_result = pca.fit_transform(scaled_data)
+    fig, ax = plt.subplots()
+    ax.scatter(pca_result[:, 0], pca_result[:, 1], c='blue', edgecolor='k', s=50)
+    ax.set_title('PCA Result')
+    ax.set_xlabel('Principal Component 1')
+    ax.set_ylabel('Principal Component 2')
+    st.pyplot(fig)
+
+    # KMeans Clustering
+    kmeans = KMeans(n_clusters=3, random_state=42)
+    clusters = kmeans.fit_predict(scaled_data)
+    fig, ax = plt.subplots()
+    ax.scatter(pca_result[:, 0], pca_result[:, 1], c=clusters, cmap='viridis', edgecolor='k', s=50)
+    ax.set_title('KMeans Clustering')
+    ax.set_xlabel('Principal Component 1')
+    ax.set_ylabel('Principal Component 2')
+    st.pyplot(fig)
+
+    # Silhouette Score
+    silhouette_avg = silhouette_score(scaled_data, clusters)
+    st.write('Silhouette Score:', silhouette_avg)
+
+    # Local Outlier Factor (LOF)
+    lof = LocalOutlierFactor(n_neighbors=20)
+    lof_labels = lof.fit_predict(scaled_data)
+    lof_outliers = np.sum(lof_labels == -1)
+    st.write("Number of outliers detected by LOF:", lof_outliers)
+
+    # Isolation Forest
+    isolation_forest = IsolationForest(contamination=0.1, random_state=42)
+    isolation_labels = isolation_forest.fit_predict(scaled_data)
+    isolation_outliers = np.sum(isolation_labels == -1)
+    st.write("Number of outliers detected by Isolation Forest:", isolation_outliers)
+
+    # DBSCAN
+    dbscan = DBSCAN(eps=0.5, min_samples=5)
+    dbscan_labels = dbscan.fit_predict(scaled_data)
+    silhouette_dbscan = silhouette_score(scaled_data, dbscan_labels)
+    st.write("DBSCAN Silhouette Score:", silhouette_dbscan)
+
+# To run this Streamlit app, save it as a .py file and execute it using the command: streamlit run <filename>.py
+print("Streamlit app code generated.")