Delete silverchairpredictin_159.py

silverchairpredictin_159.py

@@ -1,62 +0,0 @@
-# -*- coding: utf-8 -*-
-"""silverChairpredictin.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))