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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)) |
