Added Files in the repo

.gitignore

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+.venv/

SPY500MW.csv

@@ -0,0 +1,253 @@
+Date,Open,High,Low,Close
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app.py

@@ -0,0 +1,189 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans, DBSCAN
+from sklearn.metrics import silhouette_score
+from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
+from sklearn.mixture import GaussianMixture
+from datasets import load_dataset
+import os
+
+# Load dataset
+@st.cache_data
+def load_data():
+    dataset = load_dataset("Ci-Dave/SPY500MW_StockMarket")  
+    df = pd.DataFrame(dataset["train"])
+    return df
+
+# Preprocessing function
+def preprocess_data(df):
+    numeric_cols = df.select_dtypes(include=[np.number]).columns  # Select only numeric columns
+    df_numeric = df[numeric_cols].dropna()  # Drop missing values
+    scaler = StandardScaler()
+    df_scaled = scaler.fit_transform(df_numeric)
+    return df_numeric, df_scaled, scaler
+
+# Load dataset
+df = load_data()
+df_numeric, df_scaled, scaler = preprocess_data(df)
+
+# Load trained models with error handling
+def load_model(filename):
+    if os.path.exists(filename):
+        return joblib.load(filename)
+    else:
+        st.error(f"Model file {filename} not found. Make sure you uploaded the trained model.")
+        return None
+
+kmeans = load_model("kmeans_stock_model.pkl")
+hierarchical = load_model("hierarchical_stock_model.pkl")
+dbscan = load_model("dbscan_stock_model.pkl")
+gmm = load_model("gmm_stock_model.pkl")
+
+# Sidebar navigation
+st.sidebar.title("Stock Market Clustering")
+page = st.sidebar.radio("Choose a model:", ["Home", "Dataset", "K-Means", "Hierarchical", "DBSCAN", "GMM"])
+
+if page == "Home":
+    st.title("Stock Market Clustering Analysis")
+    st.write("""
+    This application analyzes stock market data using clustering techniques. 
+    Explore different clustering models (K-Means, Hierarchical, DBSCAN, and GMM) to identify patterns in stock prices.
+    
+    **Dataset:**
+    The dataset includes stock market price data with key attributes such as Open, High, Low, and Close values.
+    
+    **Clustering Models:**
+    - **K-Means:** Groups stocks into clusters based on price similarities.
+    - **Hierarchical Clustering:** Forms a tree-like structure to identify stock relationships.
+    - **DBSCAN:** Detects core clusters and outliers in stock prices.
+    - **Gaussian Mixture Model (GMM):** Uses probabilistic clustering for stock analysis.
+    
+    **Instructions:**
+    - Select a clustering model from the sidebar.
+    - View the clustering results, including cluster assignments, distributions, and visualizations.
+    """)
+  
+elif page == "Dataset":
+    st.title("Stock Market Dataset")
+
+    st.write("### Dataset Preview:")
+    st.dataframe(df[["Open", "High", "Low", "Close"]].head(), use_container_width=True)  # Show only key features
+    
+    st.write("### Full Dataset:")
+    st.dataframe(df, use_container_width=True)
+
+    st.write("### Dataset Summary:")
+    st.write(df_numeric.describe())
+
+    st.write("### Correlation Heatmap:")
+    fig, ax = plt.subplots()
+    sns.heatmap(df_numeric.corr(), annot=True, cmap="coolwarm", ax=ax)
+    st.pyplot(fig)
+
+elif page == "K-Means":
+    st.title("K-Means Clustering")
+    if kmeans:
+        clusters = kmeans.predict(df_scaled)
+        df_numeric["KMeans Cluster"] = clusters
+
+        # Display Cluster Assignments
+        st.write("Cluster Assignments:")
+        st.dataframe(df_numeric[["KMeans Cluster"]].head())
+
+        # Display Cluster Distribution
+        cluster_counts = df_numeric["KMeans Cluster"].value_counts().reset_index()
+        cluster_counts.columns = ["Cluster", "Count"]
+        st.write("Cluster Distribution:")
+        st.dataframe(cluster_counts)
+
+        # Compute and Display Silhouette Score
+        silhouette = silhouette_score(df_scaled, clusters)
+        st.write(f"Silhouette Score: {silhouette:.4f}")
+
+        # Visualization
+        fig, ax = plt.subplots()
+        sns.scatterplot(x=df_numeric.iloc[:, 0], y=df_numeric.iloc[:, 1], hue=clusters, palette="viridis", ax=ax)
+        st.pyplot(fig)
+
+elif page == "Hierarchical":
+    st.title("Hierarchical Clustering")
+
+    # Perform Hierarchical Clustering
+    linked = linkage(df_scaled, method='ward')  # Create linkage matrix
+    clusters = fcluster(linked, 4, criterion='maxclust')  # Assign cluster labels
+    df_numeric["Hierarchical Cluster"] = clusters  # Add to dataframe
+
+    # Display Cluster Assignments
+    st.write("Cluster Assignments:")
+    st.dataframe(df_numeric[["Hierarchical Cluster"]].head())
+
+    # Display Cluster Distribution
+    cluster_counts = df_numeric["Hierarchical Cluster"].value_counts().reset_index()
+    cluster_counts.columns = ["Cluster", "Count"]
+    st.write("Cluster Distribution:")
+    st.dataframe(cluster_counts)
+
+    # Dendrogram Visualization
+    fig, ax = plt.subplots(figsize=(10, 5))
+    dendrogram(linked, truncate_mode='level', p=5, ax=ax)  # Plot dendrogram
+    st.pyplot(fig)
+
+    # Scatter Plot of First Two Features
+    fig, ax = plt.subplots()
+    sns.scatterplot(x=df_numeric.iloc[:, 0], y=df_numeric.iloc[:, 1], hue=clusters, palette="viridis", ax=ax)
+    st.pyplot(fig)
+
+elif page == "DBSCAN":
+    st.title("DBSCAN Clustering")
+    if dbscan:
+        clusters = dbscan.fit_predict(df_scaled)
+        df_numeric["DBSCAN Cluster"] = clusters
+
+        # Display Cluster Assignments
+        st.write("Cluster Assignments:")
+        st.dataframe(df_numeric[["DBSCAN Cluster"]].head())
+
+        # Display Cluster Distribution (Handle outliers)
+        cluster_counts = df_numeric["DBSCAN Cluster"].value_counts().reset_index()
+        cluster_counts.columns = ["Cluster", "Count"]
+        st.write("Cluster Distribution:")
+        st.dataframe(cluster_counts)
+
+        # Visualization: Different color for outliers
+        fig, ax = plt.subplots()
+        palette = {i: sns.color_palette("viridis", as_cmap=True)(i / 4) for i in set(clusters) if i != -1}
+        palette[-1] = "red"  # Mark outliers in red
+        sns.scatterplot(x=df_numeric.iloc[:, 0], y=df_numeric.iloc[:, 1], hue=clusters, palette=palette, ax=ax)
+        st.pyplot(fig)
+
+elif page == "GMM":
+    st.title("Gaussian Mixture Model (GMM)")
+    if gmm:
+        expected_features = gmm.means_.shape[1]  # Get the number of features used during training
+        df_gmm = df_scaled[:, :expected_features]  # Keep only the needed columns
+        clusters = gmm.predict(df_gmm)
+        df_numeric["GMM Cluster"] = clusters
+
+        # Display Cluster Assignments
+        st.write("Cluster Assignments:")
+        st.dataframe(df_numeric[["GMM Cluster"]].head())
+
+        # Display Cluster Distribution
+        cluster_counts = df_numeric["GMM Cluster"].value_counts().reset_index()
+        cluster_counts.columns = ["Cluster", "Count"]
+        st.write("Cluster Distribution:")
+        st.dataframe(cluster_counts)
+
+        # Compute and Display Silhouette Score
+        silhouette = silhouette_score(df_gmm, clusters)
+        st.write(f"Silhouette Score: {silhouette:.4f}")
+
+        # Visualization
+        fig, ax = plt.subplots()
+        sns.scatterplot(x=df_numeric.iloc[:, 0], y=df_numeric.iloc[:, 1], hue=clusters, palette="viridis", ax=ax)
+        st.pyplot(fig)

dbscan_stock_model.pkl

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+oid sha256:7d79f445d36381918a010805245cac0d13111b8f04535dfebb853c5eab582a4e
+size 12727

gmm_stock_model.pkl

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+oid sha256:c59cef6dd1f677d5fdc08f6a8553b90b0f8cb367148b367b5ebc7ec246d0e91e
+size 1742

hierarchical_stock_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c38beacb44e402b74041b7770da954aee34e6197f70e540e20741d4a29df967
+size 8033

kmeans_stock_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:00564ad761caee0d83ad3d3b8929964615122ae323e07b7c821cbbbea054c331
+size 1831

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+joblib
+pandas
+matplotlib 
+seaborn
+scikit-learn
+datasets