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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+a2z_insurance.sas7bdat filter=lfs diff=lfs merge=lfs -text

a2z_insurance.sas7bdat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18fdae65702cad280f8bc8ed6a5ebfad7a0053308d176587b92adca3dbd20364
+size 1507328

clustering.py

@@ -0,0 +1,43 @@
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+from sklearn.preprocessing import StandardScaler
+import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from sklearn.decomposition import PCA
+
+
+def perform_clustering(df, n_clusters):
+    df = df.dropna()
+
+    scaler = StandardScaler()
+    df_value_scaled = scaler.fit_transform(df)
+
+    # Apply KMeans with the selected number of clusters
+    model = KMeans(n_clusters=n_clusters, random_state=42)
+    model.fit(df_value_scaled)
+    labels = model.predict(df_value_scaled)
+    score = silhouette_score(df_value_scaled, labels)
+
+    df['Cluster'] = labels
+    return df, score, df_value_scaled, labels, model
+
+def plot_clusters(df_value_scaled, labels, new_data_point=None):
+    pca = PCA(n_components=2)
+    components = pca.fit_transform(df_value_scaled)
+    df_components = pd.DataFrame(data=components, columns=['PC1', 'PC2'])
+    df_components['Cluster'] = labels
+
+    plt.figure(figsize=(10, 6))
+    sns.scatterplot(x='PC1', y='PC2', hue='Cluster', data=df_components, palette='viridis', s=100, alpha=0.7)
+
+    # Plot new data point if provided
+    if new_data_point is not None:
+        plt.scatter(new_data_point[:, 0], new_data_point[:, 1], color='red', marker='o', s=100, label='New Data Point')
+
+    plt.title('Cluster Visualization')
+    plt.xlabel('Principal Component 1')
+    plt.ylabel('Principal Component 2')
+    plt.legend(title='Cluster')
+    st.pyplot(plt)

data_preparation.py

@@ -0,0 +1,20 @@
+import pandas as pd
+import numpy as np
+
+def preprocess_data(data):
+    if 'CustID' in data.columns:
+        data = data.drop(columns=['CustID'])
+    if 'Channel' in data.columns:
+        data = data.drop(columns=['Channel'])
+    if 'Region' in data.columns:
+        data = data.drop(columns=['Region'])
+    
+
+    data = remove_outliers(data)
+    return data
+
+def remove_outliers(df, threshold=3):
+    df_numeric = df.select_dtypes(include=[float, int])
+    z_scores = np.abs((df_numeric - df_numeric.mean()) / df_numeric.std())
+    df_clean = df[(z_scores < threshold).all(axis=1)]
+    return df_clean

feature_selection.py

@@ -0,0 +1,59 @@
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import SimpleImputer
+from sklearn.decomposition import PCA
+from sklearn.feature_selection import RFE
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+
+def select_features_pca(df, n_components):
+    df_numeric = df.select_dtypes(include=[float, int])
+    
+    imputer = SimpleImputer(strategy='mean')
+    df_imputed = imputer.fit_transform(df_numeric)
+    
+    scaler = StandardScaler()
+    df_scaled = scaler.fit_transform(df_imputed)
+    
+    pca = PCA(n_components=n_components)
+    components = pca.fit_transform(df_scaled)
+    
+    feature_names = df_numeric.columns
+    important_features = feature_names[:n_components]
+    
+    return df[important_features], important_features
+
+def select_features_rfe(df, n_features_to_select):
+    df_numeric = df.select_dtypes(include=[float, int])
+    
+    imputer = SimpleImputer(strategy='mean')
+    df_imputed = imputer.fit_transform(df_numeric)
+    
+    scaler = StandardScaler()
+    df_scaled = scaler.fit_transform(df_imputed)
+    
+    model = LinearRegression()
+    rfe = RFE(estimator=model, n_features_to_select=n_features_to_select)
+    rfe.fit(df_scaled, df_scaled[:, 0])
+    
+    important_features = [df_numeric.columns[i] for i in range(len(rfe.support_)) if rfe.support_[i]]
+    
+    return df[important_features], important_features
+
+def select_features_rf(df, n_features_to_select):
+    df_numeric = df.select_dtypes(include=[float, int])
+    
+    imputer = SimpleImputer(strategy='mean')
+    df_imputed = imputer.fit_transform(df_numeric)
+    
+    scaler = StandardScaler()
+    df_scaled = scaler.fit_transform(df_imputed)
+    
+    model = RandomForestRegressor()
+    model.fit(df_scaled, df_scaled[:, 0])
+    
+    importances = model.feature_importances_
+    indices = importances.argsort()[-n_features_to_select:][::-1]
+    
+    important_features = [df_numeric.columns[i] for i in indices]
+    
+    return df[important_features], important_features

predict.py

@@ -0,0 +1,18 @@
+import streamlit as st
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+def predict_cluster(model, selected_features):
+    st.write("### Predict Cluster")
+
+    user_input = {}
+    for feature in selected_features:
+        user_input[feature] = st.number_input(f'Enter {feature}', value=0.0)
+
+    user_df = pd.DataFrame(user_input, index=[0])
+
+    scaler = StandardScaler()
+    user_df_scaled = scaler.fit_transform(user_df)
+
+    cluster = model.predict(user_df_scaled)
+    st.write(f'The predicted cluster for the input data is: {cluster[0]}')

requirements.txt

@@ -0,0 +1,10 @@
+matplotlib
+plotly
+scikit-learn
+streamlit
+seaborn
+squarify
+import-ipynb
+utils
+squarify
+findspark

retaildata.csv

@@ -0,0 +1,441 @@
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