File size: 1,511 Bytes
065036e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # -*- 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)) |