CYL代码

system/start/src/main/java/org/jeecg/cyl/analysis.scala

@@ -0,0 +1,68 @@
+package org.jeecg.cyl
+
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.functions._
+
+
+object analysis {
+
+  def main(args: Array[String]): Unit = {
+    // 创建Spark Session
+    val spark = SparkSession.builder()
+      .appName("Weather Temperature Prediction")
+      .master("local[*]")
+      .getOrCreate()
+
+    // 加载数据
+    val mumBabyDF = spark.read
+      .option("header", "true") // 使用首行作为列名
+      .option("inferSchema", "true") // 推断数据类型
+      .csv("C:\\Users\\23972\\Desktop\\Completion-template-cmd\\system\\start\\src\\main\\java\\org\\jeecg\\cyl\\tianchi_mum_baby.csv")
+
+    val tradeHistoryDF = spark.read
+      .option("header", "true")
+      .option("inferSchema", "true")
+      .csv("C:\\Users\\23972\\Desktop\\Completion-template-cmd\\system\\start\\src\\main\\java\\org\\jeecg\\cyl\\tianchi_mum_baby_trade_history.csv")
+
+    // 统计各产品类别的购买次数，并按次数降序排列
+    val popularCategories = tradeHistoryDF
+      .groupBy("category")
+      .count()
+      .orderBy(desc("count"))
+
+    popularCategories.show()
+
+    // 假设tradeHistoryDF包含日期字段`date`，格式为'yyyy-MM-dd'
+    val monthlySalesTrend = tradeHistoryDF
+      .withColumn("month", month(col("date")))
+      .groupBy("month")
+      .agg(sum("price").alias("total_sales"))
+      .orderBy("month")
+
+    monthlySalesTrend.show()
+    // 对每个产品类别，计算平均购买价格和平均购买数量
+    val avgPriceAndQuantity = tradeHistoryDF
+      .groupBy("category")
+      .agg(
+        avg("price").alias("average_price"),
+        avg("quantity").alias("average_quantity")
+      )
+      .orderBy(desc("average_price"))
+
+    avgPriceAndQuantity.show()
+    // 统计每位顾客的总消费额，并找出消费最高的顶级顾客
+    val topCustomers = tradeHistoryDF
+      .groupBy("customer_id")
+      .agg(sum("price").alias("total_spent"))
+      .orderBy(desc("total_spent"))
+
+    topCustomers.show()
+
+    popularCategories.write.option("header", "true").csv("path_to_popular_categories.csv")
+    topCustomers.write.option("header", "true").csv("path_to_top_customers.csv")
+    monthlySalesTrend.write.option("header", "true").csv("path_to_monthly_sales_trend.csv")
+    avgPriceAndQuantity.write.option("header", "true").csv("path_to_avg_price_and_quantity.csv")
+
+  }
+}

system/start/src/main/java/org/jeecg/cyl/forecast.scala

@@ -0,0 +1,48 @@
+package org.jeecg.cyl
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.ml.feature.VectorAssembler
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.catalyst.dsl.expressions.StringToAttributeConversionHelper
+object forecast {
+  def main(args: Array[String]): Unit = {
+    import org.apache.spark.sql.SparkSession
+    import org.apache.spark.sql.functions._
+
+    val spark = SparkSession.builder()
+      .appName("Weather Temperature Prediction")
+      .master("local[*]")
+      .getOrCreate()
+
+
+    val data = spark.read.option("header", "true").option("inferSchema", "true")
+      .csv("C:\\Users\\23972\\Desktop\\Completion-template-cmd\\system\\start\\src\\main\\java\\org\\jeecg\\cyl\\tianchi_mum_baby_trade_history.csv")
+
+    val filteredData = data.filter(col("category_1").equalTo(50013636))
+
+    // 准备特征和标签
+    val assembler = new VectorAssembler()
+      .setInputCols(Array("day"))  // 可以添加更多的特征列
+      .setOutputCol("features")
+
+    // 定义线性回归模型
+    val lr = new LinearRegression()
+      .setLabelCol("buy_mount")  // 替换为你的销量列名
+      .setFeaturesCol("features")
+
+    // 创建pipeline
+    val pipeline = new Pipeline().setStages(Array(assembler, lr))
+
+    // 训练模型
+    val model = pipeline.fit(filteredData)
+
+    // 进行预测
+    val predictions = model.transform(filteredData)
+
+    // 显示预测结果
+    predictions.select("day", "buy_mount", "prediction").show()
+
+    spark.stop()
+  }
+}

system/start/src/main/java/org/jeecg/cyl/handle.scala

@@ -0,0 +1,57 @@
+package org.jeecg.cyl
+
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.functions._
+
+object handle {
+  def main(args: Array[String]): Unit = {
+
+    // 创建Spark Session
+    val spark = SparkSession.builder()
+      .appName("Weather Temperature Prediction")
+      .master("local[*]")
+      .getOrCreate()
+
+    // 加载数据
+    val mumBabyDF = spark.read
+      .option("header", "true") // 使用首行作为列名
+      .option("inferSchema", "true") // 推断数据类型
+      .csv("C:\\Users\\23972\\Desktop\\Completion-template-cmd\\system\\start\\src\\main\\java\\org\\jeecg\\cyl\\tianchi_mum_baby.csv")
+
+    val tradeHistoryDF = spark.read
+      .option("header", "true")
+      .option("inferSchema", "true")
+      .csv("C:\\Users\\23972\\Desktop\\Completion-template-cmd\\system\\start\\src\\main\\java\\org\\jeecg\\cyl\\tianchi_mum_baby_trade_history.csv")
+    // 显示数据的缺失值情况
+    mumBabyDF.describe().show()
+    tradeHistoryDF.describe().show()
+
+    // 去除含有任何缺失值的行
+    val mumBabyNoMissingDF = mumBabyDF.na.drop()
+    val tradeHistoryNoMissingDF = tradeHistoryDF.na.drop()
+
+    // 或者使用某些默认值填充缺失值
+    //    val mumBabyFilledDF = mumBabyDF.na.fill("Unknown")
+    val tradeHistoryFilledDF = tradeHistoryDF.na.fill(Map("buy_mount" -> 0.0))
+
+    // 使用IQR识别并过滤异常值
+    def removeOutliers(df: DataFrame, colName: String): DataFrame = {
+      val quantiles = df.stat.approxQuantile(colName, Array(0.25, 0.75), 0.05)
+      val Q1 = quantiles(0)
+      val Q3 = quantiles(1)
+      val IQR = Q3 - Q1
+      val lowerRange = Q1 - 1.5 * IQR
+      val upperRange = Q3 + 1.5 * IQR
+
+      df.filter(col(colName).between(lowerRange, upperRange))
+    }
+
+    val tradeHistoryNoOutliersDF = removeOutliers(tradeHistoryFilledDF, "buy_mount")
+    // 将清洗后的数据保存为新的CSV文件
+    mumBabyNoMissingDF.write.option("header", "true").csv("new_mum_baby.csv")
+    tradeHistoryNoOutliersDF.write.option("header", "true").csv("new_trade_history.csv")
+
+  }
+
+}

system/start/src/main/java/org/jeecg/cyl/tianchi_mum_baby.csv

@@ -0,0 +1,954 @@
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