AI在用户行为分析中的应用：实现精准洞察与决策优化

# 创建并激活虚拟环境
python3 -m venv venv
source venv/bin/activate

# 安装所需依赖库
pip install numpy pandas scikit-learn matplotlib

import pandas as pd

# 读取用户行为数据
data = pd.read_csv('user_behavior.csv')

# 查看数据结构
print(data.head())

# 数据清洗：处理缺失值
data = data.fillna(method='ffill')

# 数据规范化
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data.drop(columns=['user_id']))
scaled_data = pd.DataFrame(scaled_data, columns=data.columns[1:])

# 计算浏览频率
data['visit_frequency'] = data.groupby('user_id')['visit_count'].transform('count')

# 计算平均停留时间
data['avg_stay_time'] = data.groupby('user_id')['stay_time'].transform('mean')

# 数据预处理：选择特征
features = ['visit_frequency', 'avg_stay_time', 'total_purchases', 'last_visit_days']
X = data[features]
y = data['purchase_intent']  # 假设我们要预测用户的购买意图

# 数据分割：划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

from sklearn.ensemble import RandomForestClassifier

# 构建随机森林分类模型
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 训练模型
model.fit(X_train, y_train)

# 进行预测
y_pred_train = model.predict(X_train)
y_pred_test = model.predict(X_test)

# 模型评估
from sklearn.metrics import classification_report, confusion_matrix

print("训练集评估结果:")
print(classification_report(y_train, y_pred_train))
print("测试集评估结果:")
print(classification_report(y_test, y_pred_test))

# 绘制混淆矩阵
import matplotlib.pyplot as plt
import seaborn as sns

conf_matrix = confusion_matrix(y_test, y_pred_test)
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['No Purchase Intent', 'Purchase Intent'], yticklabels=['No Purchase Intent', 'Purchase Intent'])
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.show()

# 读取实时用户行为数据
real_time_data = pd.read_csv('real_time_user_data.csv')

# 数据预处理
real_time_data = real_time_data.fillna(method='ffill')
scaled_real_time_data = scaler.transform(real_time_data[features])
scaled_real_time_data = pd.DataFrame(scaled_real_time_data, columns=features)

# 进行预测
real_time_predictions = model.predict(scaled_real_time_data)

# 显示预测结果
real_time_data['predicted_purchase_intent'] = real_time_predictions
print(real_time_data[['user_id', 'predicted_purchase_intent']])