Upload silverchairprediction_159.py

silverchairprediction_159.py

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+# -*- coding: utf-8 -*-
+"""silverChairprediction.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1oUsaV8V9oOXQWEeYS_uQYUu3WuVk21rP
+"""
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+file_path = 'customer_purchase_data.csv'
+df = pd.read_csv(file_path)
+
+df.head()
+
+df.info()
+
+df.describe()
+
+plt.figure(figsize=(10,6))
+sns.histplot(df['Age'], kde=True, bins=30)
+plt.title('Distribution of Age')
+plt.xlabel('Age')
+plt.ylabel('Frequency')
+plt.show()
+
+plt.figure(figsize=(10, 6))
+sns.histplot(df['AnnualIncome'], kde=True, bins=30)
+plt.title('Distribution of Annual Income')
+plt.xlabel('Annual Income')
+plt.ylabel('Frequency')
+plt.show
+
+numeric_df = df.select_dtypes(include=[np.number])
+plt.figure(figsize=(12, 8))
+sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f')
+plt.title('Correlation Heatmap')
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+
+X = df.drop('PurchaseStatus', axis=1)
+y = df['PurchaseStatus']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))