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import pandas as pd |
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import numpy as np |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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from sklearn.preprocessing import LabelEncoder, StandardScaler |
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from sklearn.feature_selection import SelectKBest, f_classif |
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from sklearn.model_selection import train_test_split |
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from sklearn.cluster import KMeans, AgglomerativeClustering |
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from sklearn.pipeline import Pipeline |
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from sklearn.metrics import silhouette_score |
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file_path = 'customer_profile_marketing.csv' |
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df = pd.read_csv(file_path) |
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print("Initial DataFrame Info:") |
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df.info() |
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print("\nInitial DataFrame Head:") |
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print(df.head()) |
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df = df.drop(columns=['Unnamed: 0', 'ID', 'Dt_Customer', 'Z_CostContact', 'Z_Revenue']) |
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print("Missing Values: {}", df.isna().sum()) |
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df = df.dropna() |
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df['Age'] = 2024 - df['Year_Birth'] # Assuming the current year is 2024 |
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df = df.drop(columns=['Year_Birth']) |
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label_encoder = LabelEncoder() |
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df['Education'] = label_encoder.fit_transform(df['Education']) |
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df['Marital_Status'] = label_encoder.fit_transform(df['Marital_Status']) |
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df.to_csv("./data_pre_processed_v1") |
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corr_matrix = df.corr() |
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plt.figure(figsize=(16, 12)) |
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sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True) |
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plt.title('Correlation Matrix') |
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plt.show() |
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X = df.drop(columns=['Response']) # Features |
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y = df['Response'] # Target variable |
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selector = SelectKBest(score_func=f_classif, k=10) |
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X_new = selector.fit_transform(X, y) |
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selected_features = X.columns[selector.get_support()] |
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print("Selected Features:") |
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print(selected_features) |
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df_processed = df[selected_features] |
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df_processed['Response'] = y |
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scaler = StandardScaler() |
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numerical_cols = df_processed.select_dtypes(include=[np.number]).columns.tolist() |
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numerical_cols.remove('Response') # Remove the target variable from the list |
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df_processed[numerical_cols] = scaler.fit_transform(df_processed[numerical_cols]) |
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X_train, X_test, y_train, y_test = train_test_split(df_processed.drop(columns=['Response']), |
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df_processed['Response'], |
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test_size=0.3, |
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random_state=42) |
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kmeans = KMeans(n_clusters=2, random_state=42) |
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kmeans.fit(X_train) |
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kmeans_labels = kmeans.predict(X_test) |
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kmeans_silhouette = silhouette_score(X_test, kmeans_labels) |
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print(f"KMeans Silhouette Score: {kmeans_silhouette:.2f}") |
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agg_clustering = AgglomerativeClustering(n_clusters=2) |
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agg_labels = agg_clustering.fit_predict(X_test) |
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agg_silhouette = silhouette_score(X_test, agg_labels) |
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print(f"Agglomerative Clustering Silhouette Score: {agg_silhouette:.2f}") |
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print("\nSummary of Clustering Results:") |
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print(f"KMeans Silhouette Score: {kmeans_silhouette:.2f}") |
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print(f"Agglomerative Clustering Silhouette Score: {agg_silhouette:.2f}") |
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