NDV_Code_By_NandiniM_CustomerSegmentation

NDV_Code_By_NandiniM_CustomerSegmentation/CustomerSegmentation.py

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+#Customer Segmentation
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.cluster import KMeans
+from koko import files
+import io
+import zipfile
+import warnings
+warnings.filterwarnings('ignore')
+
+uploaded = files.upload()
+
+for filename in uploaded.keys():
+    if filename.endswith('.zip'):
+        with zipfile.ZipFile(io.BytesIO(uploaded[filename]), 'r') as zip_ref:
+            zip_ref.extractall()
+            for name in zip_ref.namelist():
+                if name.endswith('.csv'):
+                    df = pd.read_csv(name)
+    elif filename.endswith('.csv'):
+        df = pd.read_csv(io.BytesIO(uploaded[filename]))
+
+df.columns = df.columns.str.strip()
+print(df.columns.tolist())
+
+if 'CustomerID' in df.columns:
+    df.drop(columns=["CustomerID"], inplace=True)
+
+gender_col = 'Gender' if 'Gender' in df.columns else ('Genre' if 'Genre' in df.columns else None)
+
+if gender_col:
+    plt.figure(figsize=(8,7))
+    sns.countplot(data=df, x=gender_col)
+    plt.show()
+
+    plt.figure(figsize=(20,7))
+    sns.countplot(data=df, x="Age", hue=gender_col)
+    plt.show()
+
+plt.figure(figsize=(10,8))
+sns.histplot(df["Age"], color="green", bins=20)
+plt.show()
+
+plt.figure(figsize=(18,7))
+sns.countplot(x="Age", data=df)
+plt.show()
+
+plt.figure(figsize=(30,7))
+for i, column in enumerate(df.columns[1:], 1):
+    plt.subplot(1, 4, i)
+    sns.histplot(df[column], kde=True, color="green")
+    plt.xlabel(column)
+plt.tight_layout()
+plt.show()
+
+X = df.iloc[:, [2, 3]].values
+
+plt.figure(figsize=(12,8))
+plt.scatter(X[:, 0], X[:, 1])
+plt.xlabel("Annual Income (k$)")
+plt.ylabel("Spending Score (1-100)")
+plt.show()
+
+kmeans = KMeans(n_clusters=2, random_state=42)
+kmeans.fit(X)
+plt.figure(figsize=(12,8))
+plt.scatter(X[:,0], X[:,1], c=kmeans.labels_, cmap='rainbow')
+plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], color='black')
+plt.show()
+
+kmeans_10 = KMeans(n_clusters=10, random_state=42)
+kmeans_10.fit(X)
+plt.figure(figsize=(12,8))
+plt.scatter(X[:,0], X[:,1], c=kmeans_10.labels_, cmap='rainbow')
+plt.scatter(kmeans_10.cluster_centers_[:,0], kmeans_10.cluster_centers_[:,1], color='black')
+plt.show()
+
+wcss = []
+for i in range(1, 11):
+    km = KMeans(n_clusters=i, random_state=42)
+    km.fit(X)
+    wcss.append(km.inertia_)
+
+plt.figure(figsize=(12, 8))
+plt.plot(range(1, 11), wcss, marker='8', color='blue')
+plt.xticks(range(1, 11))
+plt.grid(True)
+plt.show()