Kernel Density Estimation (KDE) plots create a smooth continuous estimate of a distribution by summing kernel functions placed at each data point. Unlike histograms, they are not affected by bin boundary placement and naturally reveal the shape of the distribution including modes, skewness, and tails.

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{
  "title": {
    "text": "Customer Order Value Distribution"
  },
  "data": [
    {
      "value": 12
    },
    {
      "value": 18
    },
    {
      "value": 22
    },
    {
      "value": 28
    },
    {
      "value": 35
    },
    {
      "value": 42
    },
    {
      "value": 38
    },
    {
      "value": 45
    },
    {
      "value": 52
    },
    {
      "value": 58
    },
    {
      "value": 65
    },
    {
      "value": 48
    },
    {
      "value": 72
    },
    {
      "value": 55
    },
    {
      "value": 32
    },
    {
      "value": 25
    },
    {
      "value": 88
    },
    {
      "value": 94
    },
    {
      "value": 102
    },
    {
      "value": 115
    },
    {
      "value": 108
    },
    {
      "value": 122
    },
    {
      "value": 135
    },
    {
      "value": 128
    },
    {
      "value": 142
    },
    {
      "value": 148
    },
    {
      "value": 155
    },
    {
      "value": 162
    },
    {
      "value": 138
    },
    {
      "value": 125
    }
  ],
  "series": [
    {
      "type": "kde",
      "xKey": "value",
      "bandwidth": 0.5,
      "fill": "#6366f1",
      "fillOpacity": 0.35,
      "stroke": "#6366f1",
      "strokeWidth": 2.5
    }
  ],
  "axes": [
    {
      "position": "bottom",
      "title": {
        "text": "Order Value ($)"
      }
    },
    {
      "position": "left",
      "title": {
        "text": "Density"
      }
    }
  ]
}

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A smooth kernel density estimate of customer purchase values reveals the bimodal spending pattern.

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