antitheft159
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Upload silverchairprediction_159.py
Browse files- silverchairprediction_159.py +62 -0
silverchairprediction_159.py
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# -*- coding: utf-8 -*-
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"""silverChairprediction.159
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1oUsaV8V9oOXQWEeYS_uQYUu3WuVk21rP
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"""
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import warnings
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warnings.filterwarnings('ignore')
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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file_path = 'customer_purchase_data.csv'
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df = pd.read_csv(file_path)
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df.head()
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df.info()
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df.describe()
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plt.figure(figsize=(10,6))
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sns.histplot(df['Age'], kde=True, bins=30)
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plt.title('Distribution of Age')
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plt.xlabel('Age')
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plt.ylabel('Frequency')
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plt.show()
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plt.figure(figsize=(10, 6))
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sns.histplot(df['AnnualIncome'], kde=True, bins=30)
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plt.title('Distribution of Annual Income')
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plt.xlabel('Annual Income')
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plt.ylabel('Frequency')
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plt.show
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numeric_df = df.select_dtypes(include=[np.number])
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plt.figure(figsize=(12, 8))
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sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', fmt='.2f')
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plt.title('Correlation Heatmap')
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plt.show()
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import classification_report, confusion_matrix
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X = df.drop('PurchaseStatus', axis=1)
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y = df['PurchaseStatus']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
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model = RandomForestClassifier(random_state=42)
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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print(confusion_matrix(y_test, y_pred))
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print(classification_report(y_test, y_pred))
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