diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..375f5ca
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11.6
diff --git a/README.md b/README.md
index 741843f..74245e4 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,11 @@
-# parse-csv-sample
\ No newline at end of file
+# Lab Bank CSVエクスポートデータ活用サンプル
+
+本リポジトリは[Lab Bank](https://labbank.jp/)で出力されたCSVエクスポートデータを活用するための、サンプルコードを格納しております。
+
+```
+.
+├── README.md ... 本ドキュメント
+├── export ... サンプルコード向けのダミーのエクスポートデータ
+├── libs ... pandasを簡単に扱うための関数
+└── sample.ipynb ... サンプルコード
+```
\ No newline at end of file
diff --git a/export/project.csv b/export/project.csv
new file mode 100644
index 0000000..6e8f890
--- /dev/null
+++ b/export/project.csv
@@ -0,0 +1,5 @@
+project.__id,project.__record_name,project.client_name,project.deadline,project.purpose,project.memo
+プロジェクト.ID,プロジェクト.プロジェクト名,プロジェクト.顧客名,プロジェクト.期限,プロジェクト.目的,プロジェクト.メモ
+text,text,text,date,text,text
+df9d5402-218b-4d0d-87f0-c486340896ea,test,test,2024/5/29,aaaaa,
+cfd451e2-8e25-4632-9890-33695f583ac6,test2,test2,2024/5/22,test2,test2
\ No newline at end of file
diff --git a/export/theme_b_breaking_evaluation_conditions.csv b/export/theme_b_breaking_evaluation_conditions.csv
new file mode 100644
index 0000000..687799b
--- /dev/null
+++ b/export/theme_b_breaking_evaluation_conditions.csv
@@ -0,0 +1,6 @@
+theme_b_breaking_evaluation_conditions.__id,theme_b_breaking_evaluation_conditions.speed,theme_b_breaking_evaluation_conditions.wet_road,theme_b_breaking_evaluation_conditions.vehicle
+制動性能評価.条件ID,制動性能評価.開始速度,制動性能評価.ウェット路面,制動性能評価.試験車両
+text,number,boolean,text
+43dcdd39-f9ed-456c-b1bc-17918e866061,60,true,10daf7c0-941a-42e9-8913-07cb65ad6e81
+e187d3b1-e610-49b3-9879-fd070b792ed3,80,true,10daf7c0-941a-42e9-8913-07cb65ad6e81
+35960a3e-5d72-482b-82ae-f8b068a65a4d,100,true,10daf7c0-941a-42e9-8913-07cb65ad6e81
\ No newline at end of file
diff --git a/export/theme_b_breaking_evaluation_results.csv b/export/theme_b_breaking_evaluation_results.csv
new file mode 100644
index 0000000..ed39ebd
--- /dev/null
+++ b/export/theme_b_breaking_evaluation_results.csv
@@ -0,0 +1,33 @@
+theme_b_breaking_evaluation_results.__id,theme_b_breaking_evaluation_results.condition_id,make_form.__id,theme_b_breaking_evaluation_results.distance,theme_b_breaking_evaluation_results.duration,theme_b_breaking_evaluation_results.uneven_wear,theme_b_breaking_evaluation_results.squeal_sound,theme_b_breaking_evaluation_results.date,theme_b_breaking_evaluation_results.image
+制動性能評価.結果ID,制動性能評価.条件ID,成形.ID,制動性能評価.制動距離,制動性能評価.制動時間,制動性能評価.偏摩耗,制動性能評価.スキール音,制動性能評価.実施日,制動性能評価.参考画像
+text,text,text,number,number,boolean,number,date,number
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\ No newline at end of file
diff --git a/export/theme_b_carbon_black.csv b/export/theme_b_carbon_black.csv
new file mode 100644
index 0000000..87f59e8
--- /dev/null
+++ b/export/theme_b_carbon_black.csv
@@ -0,0 +1,8 @@
+theme_b_carbon_black.__id,theme_b_carbon_black.name,theme_b_carbon_black.param1,theme_b_carbon_black.param2,theme_b_carbon_black.maker
+カーボンブラック.ID,カーボンブラック.名前,カーボンブラック.物性1,カーボンブラック.物性2,カーボンブラック.製造元
+text,text,number,number,text
+1c9acec0-fe04-4c11-960e-ef1c50edb503,carbon_black1,100,38,ef47e88b-c316-4503-9e62-1597778a72c0
+b29bd7a2-a6d5-4c0a-b0a0-24c6e878d681,carbon_black2,120,40,1ac9039b-ac81-4b9c-8f22-3c285adcb508
+1be837e0-a159-4e95-9cfb-f06ada79e8cc,carbon_black3,98,42,838ec269-6d25-4f14-81bd-b09e14da4ade
+c9b28d45-c80c-4bdc-b8d2-ad75fb67c403,carbon_black4,121,39,24cbf099-108a-4ce1-a6c9-4f96e636734d
+38dbf93a-5ae1-4a70-a671-ec6533a1b2a5,carbon_black5,94.5,36,eacb7a51-9eb4-45e1-932c-72711fea02c1
\ No newline at end of file
diff --git a/export/theme_b_make_form.csv b/export/theme_b_make_form.csv
new file mode 100644
index 0000000..de42d51
--- /dev/null
+++ b/export/theme_b_make_form.csv
@@ -0,0 +1,13 @@
+theme_b_make_form.__id,dependencyDevStep.__id,theme_b_make_form.tread_width,theme_b_make_form.carcass_length
+成形.ID,前工程.ID,成形.トレッド幅,成形.カーカス長
+text,text,number,number
+3277d707-df85-4444-b3ef-10b988e005a6,2ed6f86e-9e23-4baa-ae57-33ef1817e160,225,450
+82a44f81-0f9e-43ed-8b42-3b774a5a3124,e51ee3f1-2ef9-4685-9bb1-717dedecd32a,225,450
+b51ad19f-34dd-4e2d-971e-31494e5b0b7f,7ffb28c8-ad14-42f2-938b-c83bc2c2eae9,225,450
+193a304b-2e7b-4b01-8bb2-6aff8ff7df5f,f56206b8-1766-4d3e-85f9-3eea07661c45,225,450
+0542caed-ebe0-47c2-8a9e-ec9dc137853c,66c2148c-df73-4896-b4b0-5e83aedb3f7a,225,450
+229d1635-2af7-4bab-9c14-c610a7cc9aaf,2ed6f86e-9e23-4baa-ae57-33ef1817e160,230,450
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+39109815-1f26-41d3-ad70-50201cc2c674,f56206b8-1766-4d3e-85f9-3eea07661c45,230,450
+1dd85480-95d5-4383-b998-0709e6eaf020,66c2148c-df73-4896-b4b0-5e83aedb3f7a,230,450
\ No newline at end of file
diff --git a/export/theme_b_maker.csv b/export/theme_b_maker.csv
new file mode 100644
index 0000000..da780fc
--- /dev/null
+++ b/export/theme_b_maker.csv
@@ -0,0 +1,8 @@
+theme_b_maker.__id,theme_b_maker.name
+製造元.ID,製造元.社名
+text,text
+ef47e88b-c316-4503-9e62-1597778a72c0,Aidemy
+1ac9039b-ac81-4b9c-8f22-3c285adcb508,Bidemy
+838ec269-6d25-4f14-81bd-b09e14da4ade,Cidemy
+24cbf099-108a-4ce1-a6c9-4f96e636734d,Didemy
+eacb7a51-9eb4-45e1-932c-72711fea02c1,Eidemy
\ No newline at end of file
diff --git a/export/theme_b_recipe_material_amounts.csv b/export/theme_b_recipe_material_amounts.csv
new file mode 100644
index 0000000..848db44
--- /dev/null
+++ b/export/theme_b_recipe_material_amounts.csv
@@ -0,0 +1,22 @@
+theme_b_recipe_material_amounts.__id,theme_b_recipe_material_amounts.__sub_recipe_id,theme_b_recipe_material_amounts.__master_name,theme_b_recipe_material_amounts.__master_id,theme_b_recipe_material_amounts.amount
+レシピ.ID,レシピ.サブレシピID,レシピ.材料マスタ,レシピ.材料マスタID,レシピ.分量
+text,text,text,text,text
+137bb1d9-4132-4044-9e30-1463f5967079,b12cd3a9-2b87-4aea-964a-4e64f4bdf0cc,theme_b_rubber,aa610527-eb34-4430-bbc0-fb1bbfb3f27d,100
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+72ae2e2f-3b3f-4138-b0b9-6524daed796f,71cf5a34-ecf5-4481-828d-ab48b00ec23d,theme_b_rubber,df2225e3-67d4-4670-9dcb-5bfd0e42bc7b,30
+6acef913-c0b8-4872-8a5f-8f8bdb0d05d3,71cf5a34-ecf5-4481-828d-ab48b00ec23d,theme_b_carbon_black,b29bd7a2-a6d5-4c0a-b0a0-24c6e878d681,10
+0d0d1771-d0b8-47ac-989e-87ffa52147fa,09687ba8-6c96-4d74-a83d-bd875bbc73d5,theme_b_rubber,df2225e3-67d4-4670-9dcb-5bfd0e42bc7b,30
+41a65d10-350f-4b28-ad50-48357e691db7,09687ba8-6c96-4d74-a83d-bd875bbc73d5,theme_b_carbon_black,1c9acec0-fe04-4c11-960e-ef1c50edb503,10
+6d4a71bb-52d7-4c6e-bec7-d92bf071bfc1,ca89ec11-ef1f-4141-8826-9fef6ab8739d,theme_b_rubber,df2225e3-67d4-4670-9dcb-5bfd0e42bc7b,20
+671affa3-666f-4dd0-a24e-c8bd607710fb,ca89ec11-ef1f-4141-8826-9fef6ab8739d,theme_b_carbon_black,1c9acec0-fe04-4c11-960e-ef1c50edb503,15
\ No newline at end of file
diff --git a/export/theme_b_recipe_recipes.csv b/export/theme_b_recipe_recipes.csv
new file mode 100644
index 0000000..3d89b3b
--- /dev/null
+++ b/export/theme_b_recipe_recipes.csv
@@ -0,0 +1,8 @@
+theme_b_recipe_recipes.__id,theme_b_recipe_recipes.__recipe_name,project.__id,theme_b_recipe_recipes.temp,theme_b_recipe_recipes.additive
+レシピ.ID,レシピ.レシピ名,プロジェクト.ID,レシピ.温度,レシピ.添加剤
+text,text,text,number,boolean
+2ed6f86e-9e23-4baa-ae57-33ef1817e160,レシピ1,df9d5402-218b-4d0d-87f0-c486340896ea,150,true
+e51ee3f1-2ef9-4685-9bb1-717dedecd32a,レシピ2,df9d5402-218b-4d0d-87f0-c486340896ea,150,false
+7ffb28c8-ad14-42f2-938b-c83bc2c2eae9,レシピ3,df9d5402-218b-4d0d-87f0-c486340896ea,200,true
+f56206b8-1766-4d3e-85f9-3eea07661c45,レシピ4,df9d5402-218b-4d0d-87f0-c486340896ea,200,false
+66c2148c-df73-4896-b4b0-5e83aedb3f7a,レシピ5,df9d5402-218b-4d0d-87f0-c486340896ea,120,true
\ No newline at end of file
diff --git a/export/theme_b_recipe_sub_recipes.csv b/export/theme_b_recipe_sub_recipes.csv
new file mode 100644
index 0000000..0a44677
--- /dev/null
+++ b/export/theme_b_recipe_sub_recipes.csv
@@ -0,0 +1,13 @@
+theme_b_recipe_sub_recipes.__id,theme_b_recipe_recipes.__id,theme_b_recipe_sub_recipes.__name,theme_b_recipe_sub_recipes.additive
+レシピ.サブレシピID,レシピ.レシピID,レシピ.サブレシピ名,レシピ.添加剤
+text,text,text,boolean
+b12cd3a9-2b87-4aea-964a-4e64f4bdf0cc,2ed6f86e-9e23-4baa-ae57-33ef1817e160,配合ステップ1,false
+45c3aafd-d8ed-418f-8ac4-60a6bacc4e06,e51ee3f1-2ef9-4685-9bb1-717dedecd32a,配合ステップ1,false
+07c6f1a1-2032-40dc-9f3e-b8102915a981,7ffb28c8-ad14-42f2-938b-c83bc2c2eae9,配合ステップ1,true
+f0c69b4b-235b-446b-ba8d-13a210cfd19d,f56206b8-1766-4d3e-85f9-3eea07661c45,配合ステップ1,true
+f5047c36-d953-459f-b681-464fd43b2c1d,66c2148c-df73-4896-b4b0-5e83aedb3f7a,配合ステップ1,false
+71cf5a34-ecf5-4481-828d-ab48b00ec23d,2ed6f86e-9e23-4baa-ae57-33ef1817e160,配合ステップ2,true
+a0a85502-aca2-4efa-bdac-4631b9366b35,e51ee3f1-2ef9-4685-9bb1-717dedecd32a,配合ステップ2,false
+09687ba8-6c96-4d74-a83d-bd875bbc73d5,7ffb28c8-ad14-42f2-938b-c83bc2c2eae9,配合ステップ2,true
+200a639c-4973-4f4a-ac13-b4d90870232e,f56206b8-1766-4d3e-85f9-3eea07661c45,配合ステップ2,false
+ca89ec11-ef1f-4141-8826-9fef6ab8739d,66c2148c-df73-4896-b4b0-5e83aedb3f7a,配合ステップ2,true
\ No newline at end of file
diff --git a/export/theme_b_rubber.csv b/export/theme_b_rubber.csv
new file mode 100644
index 0000000..ed248f3
--- /dev/null
+++ b/export/theme_b_rubber.csv
@@ -0,0 +1,8 @@
+theme_b_rubber.__id,theme_b_rubber.name,theme_b_rubber.param1,theme_b_rubber.param2,theme_b_rubber.maker
+ゴム.ID,ゴム.名前,ゴム.物性1,ゴム.物性2,ゴム.製造元
+text,text,number,number,text
+aa610527-eb34-4430-bbc0-fb1bbfb3f27d,rubber1,50,12,ef47e88b-c316-4503-9e62-1597778a72c0
+df2225e3-67d4-4670-9dcb-5bfd0e42bc7b,rubber2,80,11.5,1ac9039b-ac81-4b9c-8f22-3c285adcb508
+00c40efd-3a6b-46fd-9d9b-89c3af0b277c,rubber3,88,22.1,838ec269-6d25-4f14-81bd-b09e14da4ade
+5c7bcbc8-9dd6-4757-b29d-c3643c07612a,rubber4,90,18.6,24cbf099-108a-4ce1-a6c9-4f96e636734d
+4f581462-34b2-40b5-9488-0de1b17ec7de,rubber5,56.3,11,eacb7a51-9eb4-45e1-932c-72711fea02c1
\ No newline at end of file
diff --git a/export/theme_b_vehicle.csv b/export/theme_b_vehicle.csv
new file mode 100644
index 0000000..3f8f80d
--- /dev/null
+++ b/export/theme_b_vehicle.csv
@@ -0,0 +1,5 @@
+theme_b_vehicle.__id,theme_b_vehicle.name,theme_b_vehicle.weight
+試験車両.ID,試験車両.車体番号,試験車両.車体重量
+text,text,number
+b63d5516-89ad-4f19-b98d-da67d1de5d97,001,1200
+10daf7c0-941a-42e9-8913-07cb65ad6e81,002,1480
\ No newline at end of file
diff --git a/libs/combine.py b/libs/combine.py
new file mode 100644
index 0000000..40d45d2
--- /dev/null
+++ b/libs/combine.py
@@ -0,0 +1,33 @@
+import pandas as pd
+
+
+def merge_columns_df(df: pd.DataFrame, target_keys: list[str], merged_key: str) -> pd.DataFrame:
+    """
+    target_keysで指定された列を結合し、新しくmerged_keyで指定された列を作成する。
+    同じ行の中でNaNではない値が存在する場合はそれを採用する、そのため欠損値がNaN以外の場合は動作しない。
+    複数の列でNaN以外が存在する場合は、target_keysのインデックスが小さい値が優先される。
+    また関数が実行されると、引数で渡されたdfの元の変数も書き換わる。
+
+    ex.
+    --- code ---
+    df = pd.DataFrame({
+        'a': [1, 2, 3],
+        'b': [4, NaN, 6],
+        'c': [NaN, 8, NaN]
+    })
+    merge_columns_df(df, ['b', 'c'], 'd')
+    print(df)
+
+    --- output ---
+    0  a  d
+    1  1  4
+    2  2  8
+    3  3  6
+    """
+
+    def merge_value(row: pd.Series) -> str:
+        return list(filter(lambda v: not pd.isna(v), [row[k] for k in target_keys]))[0]
+
+    df[merged_key] = df.apply(merge_value, axis=1)
+    df.drop(target_keys, axis=1, inplace=True)
+    return df
diff --git a/libs/read_csv.py b/libs/read_csv.py
new file mode 100644
index 0000000..cf68af8
--- /dev/null
+++ b/libs/read_csv.py
@@ -0,0 +1,39 @@
+import pandas as pd
+
+
+def csv_to_df(path: str):
+    """
+    CSVデータを読み込んでpandasのDataFrameに変換する
+    """
+
+    # CSVは3行のヘッダがある
+    # 英語カラム名: CSVの中で一意、操作やクエリの実行は英語カラム名を指定
+    # 日本語カラム名: CSVの中で一意ではないため操作やクエリには使わない、表示などに使用する
+    # 型: text, number, dateなどそれぞれの値をどういった型として扱うべきかを示す
+
+    # CSVの型表記とpandasのdtype表記の対応
+    to_dtype = {
+        "number": "float",
+        "boolean": "bool",
+        "text": "object",
+        "date": "object",
+        "datetime": "object",
+    }
+    type_df = (pd.read_csv(path, header=0, na_filter=True))[1:2]
+    dtypes = {k: to_dtype[type_df[k][1]] for k in type_df.columns}
+
+    # データフレームのヘッダとして扱うのは0=1行目、以降2行(日本語カラム名、型)を読み飛ばし
+    data = pd.read_csv(path, header=0, na_filter=True).drop([0, 1])
+    return data.astype(dtypes)
+
+
+def key_to_display_name(paths: list[str]):
+    """
+    DataFrameのヘッダと表示用の日本語表記の対応を辞書型で返す
+    """
+    res = dict()
+    for p in paths:
+        df = pd.read_csv(p, header=0, na_filter=True)[0:1]
+        for k in df.columns:
+            res[k] = df[k][0]
+    return res
diff --git a/renovate.json b/renovate.json
new file mode 100644
index 0000000..5db72dd
--- /dev/null
+++ b/renovate.json
@@ -0,0 +1,6 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": [
+    "config:recommended"
+  ]
+}
diff --git a/sample.ipynb b/sample.ipynb
new file mode 100644
index 0000000..70595e6
--- /dev/null
+++ b/sample.ipynb
@@ -0,0 +1,612 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "26cb0b2f-a2bd-4bb8-87b6-50110117a2d5.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "cb547276-b51c-471b-b1ac-b00d6fd945c9",
+   "metadata": {},
+   "source": [
+    "## 各種CSVをDataFrameに読みこむ\n",
+    "\n",
+    "リレーショナルデータベースのように工程ごとにCSVを分けて正規化しています。\n",
+    "レシピ、製造、試験とそれぞれの工程ごとに若干テーブルの構造が異なっており、レシピと試験工程に関しては1工程につき複数のテーブルを抱えています。\n",
+    "\n",
+    "- 材料や機器などを保持するマスタ情報\n",
+    "  - ゴム\n",
+    "  - カーボンブラック\n",
+    "  - 製造元\n",
+    "  - 試験車両\n",
+    "- レシピ工程\n",
+    "  - レシピ全体 (*_recipes.csv)\n",
+    "  - サブレシピ全体 (*_sub_recipes.csv) ← サブレシピを設定していない場合は出力されません\n",
+    "  - 材料・量情報 (*_material_amounts.csv)\n",
+    "- 製造工程\n",
+    "- 試験工程\n",
+    "  - 試験条件 (*_conditions.csv)\n",
+    "  - 試験結果 (*_result.csv)\n",
+    "\n",
+    "材料・量情報とレシピについての補足\n",
+    "![レシピ工程.png](attachment:26cb0b2f-a2bd-4bb8-87b6-50110117a2d5.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97a59631-bc1f-4dae-81dc-c4ec51a130a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-- 読み取ったデータはprintで確認可能 --\n",
+      "                    theme_b_rubber.__id theme_b_rubber.name  \\\n",
+      "2  aa610527-eb34-4430-bbc0-fb1bbfb3f27d             rubber1   \n",
+      "3  df2225e3-67d4-4670-9dcb-5bfd0e42bc7b             rubber2   \n",
+      "4  00c40efd-3a6b-46fd-9d9b-89c3af0b277c             rubber3   \n",
+      "5  5c7bcbc8-9dd6-4757-b29d-c3643c07612a             rubber4   \n",
+      "6  4f581462-34b2-40b5-9488-0de1b17ec7de             rubber5   \n",
+      "\n",
+      "   theme_b_rubber.param1  theme_b_rubber.param2  \\\n",
+      "2                   50.0                   12.0   \n",
+      "3                   80.0                   11.5   \n",
+      "4                   88.0                   22.1   \n",
+      "5                   90.0                   18.6   \n",
+      "6                   56.3                   11.0   \n",
+      "\n",
+      "                   theme_b_rubber.maker  \n",
+      "2  ef47e88b-c316-4503-9e62-1597778a72c0  \n",
+      "3  1ac9039b-ac81-4b9c-8f22-3c285adcb508  \n",
+      "4  838ec269-6d25-4f14-81bd-b09e14da4ade  \n",
+      "5  24cbf099-108a-4ce1-a6c9-4f96e636734d  \n",
+      "6  eacb7a51-9eb4-45e1-932c-72711fea02c1  \n",
+      "-- 必要なパラメタのみ抽出することも可能 --\n",
+      "  theme_b_rubber.name  theme_b_rubber.param2\n",
+      "2             rubber1                   12.0\n",
+      "3             rubber2                   11.5\n",
+      "4             rubber3                   22.1\n",
+      "5             rubber4                   18.6\n",
+      "6             rubber5                   11.0\n",
+      "-- そのまま読みづらい場合はヘッダを日本語へ変換することも可能 --\n",
+      "                                  ゴム.ID    ゴム.名前  ゴム.物性1  ゴム.物性2  \\\n",
+      "2  aa610527-eb34-4430-bbc0-fb1bbfb3f27d  rubber1    50.0    12.0   \n",
+      "3  df2225e3-67d4-4670-9dcb-5bfd0e42bc7b  rubber2    80.0    11.5   \n",
+      "4  00c40efd-3a6b-46fd-9d9b-89c3af0b277c  rubber3    88.0    22.1   \n",
+      "5  5c7bcbc8-9dd6-4757-b29d-c3643c07612a  rubber4    90.0    18.6   \n",
+      "6  4f581462-34b2-40b5-9488-0de1b17ec7de  rubber5    56.3    11.0   \n",
+      "\n",
+      "                                 ゴム.製造元  \n",
+      "2  ef47e88b-c316-4503-9e62-1597778a72c0  \n",
+      "3  1ac9039b-ac81-4b9c-8f22-3c285adcb508  \n",
+      "4  838ec269-6d25-4f14-81bd-b09e14da4ade  \n",
+      "5  24cbf099-108a-4ce1-a6c9-4f96e636734d  \n",
+      "6  eacb7a51-9eb4-45e1-932c-72711fea02c1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from libs.read_csv import csv_to_df, key_to_display_name\n",
+    "from libs.combine import merge_columns_df\n",
+    "import pandas as pd\n",
+    "\n",
+    "# マスタの読み込み\n",
+    "rubber_df = csv_to_df('./export/theme_b_rubber.csv')\n",
+    "carbon_black_df = csv_to_df('./export/theme_b_carbon_black.csv')\n",
+    "maker_df = csv_to_df('./export/theme_b_maker.csv')\n",
+    "vehicle_df = csv_to_df('./export/theme_b_vehicle.csv')\n",
+    "# レシピ工程の読み込み\n",
+    "recipe_df = csv_to_df('./export/theme_b_recipe_recipes.csv')\n",
+    "subrecipe_df = csv_to_df('./export/theme_b_recipe_sub_recipes.csv')\n",
+    "material_amount_df = csv_to_df('./export/theme_b_recipe_material_amounts.csv')\n",
+    "# 製造工程の読み込み\n",
+    "make_form_df = csv_to_df('./export/theme_b_make_form.csv')\n",
+    "# 試験工程の読み込み\n",
+    "breaking_condition_df = csv_to_df('./export/theme_b_breaking_evaluation_conditions.csv')\n",
+    "breaking_result_df = csv_to_df('./export/theme_b_breaking_evaluation_results.csv')\n",
+    "\n",
+    "print('-- 読み取ったデータはprintで確認可能 --')\n",
+    "print(rubber_df)\n",
+    "\n",
+    "print('-- 必要なパラメタのみ抽出することも可能 --')\n",
+    "print(rubber_df[['theme_b_rubber.name', 'theme_b_rubber.param2']])\n",
+    "\n",
+    "print('-- そのまま読みづらい場合はヘッダを日本語へ変換することも可能 --')\n",
+    "key_mapper = key_to_display_name(['./export/theme_b_rubber.csv'])\n",
+    "print(rubber_df.rename(columns=key_mapper))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "541b2057-d321-4ea3-9607-f08949d41672",
+   "metadata": {},
+   "source": [
+    "## DataFrame同士を結合する\n",
+    "\n",
+    "DataFrameには正規化されたデータしか入っていないため、結合や集計を行い処理を行える状態に持っていきます。\n",
+    "\n",
+    "```mermaid\n",
+    "---\n",
+    "title: DataFarame間の関係\n",
+    "---\n",
+    "erDiagram\n",
+    "    maker_df }|--o{ rubber_df : uses\n",
+    "    maker_df }|--o{ carbon_black_df : uses\n",
+    "    rubber_df }|--o{ material_amount_df : uses\n",
+    "    carbon_black_df }|--o{ material_amount_df : uses\n",
+    "    material_amount_df }|--o| sub_recipe_df: contains\n",
+    "    sub_recipe_df }o--|| recipe_df: contains\n",
+    "    recipe_df ||--o{ make_form_df: uses\n",
+    "    make_form_df ||--o{ breaking_result_df: uses\n",
+    "    breaking_condition_df ||--o{ breaking_result_df: uses\n",
+    "    vehicle_df ||--o{ breaking_condition_df: uses\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "604d828d-a919-4ce7-b57e-89d83a7bcee8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   theme_b_rubber.name theme_b_carbon_black.name  \\\n",
+      "0              rubber1                       NaN   \n",
+      "1                  NaN             carbon_black4   \n",
+      "2              rubber1                       NaN   \n",
+      "3                  NaN             carbon_black4   \n",
+      "4              rubber1                       NaN   \n",
+      "5                  NaN             carbon_black4   \n",
+      "6                  NaN             carbon_black3   \n",
+      "7              rubber1                       NaN   \n",
+      "8                  NaN             carbon_black4   \n",
+      "9                  NaN             carbon_black3   \n",
+      "10             rubber2                       NaN   \n",
+      "11                 NaN             carbon_black4   \n",
+      "12             rubber2                       NaN   \n",
+      "13             rubber2                       NaN   \n",
+      "14                 NaN             carbon_black2   \n",
+      "15             rubber2                       NaN   \n",
+      "16                 NaN             carbon_black1   \n",
+      "17             rubber2                       NaN   \n",
+      "18                 NaN             carbon_black1   \n",
+      "\n",
+      "   theme_b_recipe_material_amounts.amount theme_b_maker.name  \n",
+      "0                                     100             Aidemy  \n",
+      "1                                      50             Didemy  \n",
+      "2                                      80             Aidemy  \n",
+      "3                                      70             Didemy  \n",
+      "4                                      70             Aidemy  \n",
+      "5                                      40             Didemy  \n",
+      "6                                      40             Cidemy  \n",
+      "7                                      50             Aidemy  \n",
+      "8                                      20             Didemy  \n",
+      "9                                      30             Cidemy  \n",
+      "10                                     50             Bidemy  \n",
+      "11                                     50             Didemy  \n",
+      "12                                    100             Bidemy  \n",
+      "13                                     30             Bidemy  \n",
+      "14                                     10             Bidemy  \n",
+      "15                                     30             Bidemy  \n",
+      "16                                     10             Aidemy  \n",
+      "17                                     20             Bidemy  \n",
+      "18                                     15             Aidemy  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from libs.combine import merge_columns_df\n",
+    "\n",
+    "# ======\n",
+    "# マスタ情報とレシピ情報を結合\n",
+    "# ======\n",
+    "\n",
+    "# 材料・量のペアと材料マスタのパラメタを結合\n",
+    "df = pd.merge(\n",
+    "    material_amount_df,\n",
+    "    rubber_df,\n",
+    "    left_on='theme_b_recipe_material_amounts.__master_id',\n",
+    "    right_on='theme_b_rubber.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    carbon_black_df,\n",
+    "    left_on='theme_b_recipe_material_amounts.__master_id',\n",
+    "    right_on='theme_b_carbon_black.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# 材料マスタとメーカーマスタのパラメタを結合\n",
+    "\n",
+    "\"\"\"\n",
+    "'theme_b_carbon_black.maker', 'theme_b_rubber.maker' それぞれに対して結合を行うと、キーが被ってしまうmakerの値が重複して片方が上書きされてしまう。\n",
+    "回避のため先に列を結合しておく。\n",
+    "\"\"\"\n",
+    "merge_columns_df(df, ['theme_b_carbon_black.maker', 'theme_b_rubber.maker'], 'theme_b_maker.__id')\n",
+    "\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    maker_df,\n",
+    "    left_on='theme_b_maker.__id',\n",
+    "    right_on='theme_b_maker.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# 材料・量とレシピのパラメタを結合\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    subrecipe_df,\n",
+    "    left_on='theme_b_recipe_material_amounts.__sub_recipe_id',\n",
+    "    right_on='theme_b_recipe_sub_recipes.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    recipe_df,\n",
+    "    left_on='theme_b_recipe_recipes.__id',\n",
+    "    right_on='theme_b_recipe_recipes.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(df[['theme_b_rubber.name', 'theme_b_carbon_black.name', 'theme_b_recipe_material_amounts.amount', 'theme_b_maker.name']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b403c71-2b53-4377-912f-ece5aa881266",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['theme_b_recipe_material_amounts.__id',\n",
+      "       'theme_b_recipe_material_amounts.__sub_recipe_id',\n",
+      "       'theme_b_recipe_material_amounts.__master_name',\n",
+      "       'theme_b_recipe_material_amounts.__master_id',\n",
+      "       'theme_b_recipe_material_amounts.amount', 'theme_b_rubber.__id',\n",
+      "       'theme_b_rubber.name', 'theme_b_rubber.param1', 'theme_b_rubber.param2',\n",
+      "       'theme_b_carbon_black.__id', 'theme_b_carbon_black.name',\n",
+      "       'theme_b_carbon_black.param1', 'theme_b_carbon_black.param2',\n",
+      "       'theme_b_maker.__id', 'theme_b_maker.name',\n",
+      "       'theme_b_recipe_sub_recipes.__id', 'theme_b_recipe_recipes.__id',\n",
+      "       'theme_b_recipe_sub_recipes.__name',\n",
+      "       'theme_b_recipe_sub_recipes.additive',\n",
+      "       'theme_b_recipe_recipes.__recipe_name', 'project.__id',\n",
+      "       'theme_b_recipe_recipes.temp', 'theme_b_recipe_recipes.additive',\n",
+      "       'theme_b_make_form.__id', 'dependencyDevStep.__id',\n",
+      "       'theme_b_make_form.tread_width', 'theme_b_make_form.carcass_length',\n",
+      "       'theme_b_breaking_evaluation_results.__id',\n",
+      "       'theme_b_breaking_evaluation_results.condition_id', 'make_form.__id',\n",
+      "       'theme_b_breaking_evaluation_results.distance',\n",
+      "       'theme_b_breaking_evaluation_results.duration',\n",
+      "       'theme_b_breaking_evaluation_results.uneven_wear',\n",
+      "       'theme_b_breaking_evaluation_results.squeal_sound',\n",
+      "       'theme_b_breaking_evaluation_results.date',\n",
+      "       'theme_b_breaking_evaluation_results.image',\n",
+      "       'theme_b_breaking_evaluation_conditions.__id',\n",
+      "       'theme_b_breaking_evaluation_conditions.speed',\n",
+      "       'theme_b_breaking_evaluation_conditions.wet_road',\n",
+      "       'theme_b_breaking_evaluation_conditions.vehicle',\n",
+      "       'theme_b_vehicle.__id', 'theme_b_vehicle.name',\n",
+      "       'theme_b_vehicle.weight'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ======\n",
+    "# 製造工程や試験工程と結合\n",
+    "# ======\n",
+    "\n",
+    "# レシピ工程:成形工程 = 1:n なので、right join で結合\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    make_form_df,\n",
+    "    left_on='theme_b_recipe_recipes.__id',\n",
+    "    right_on='dependencyDevStep.__id',\n",
+    "    how='right'\n",
+    ")\n",
+    "\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    breaking_result_df,\n",
+    "    left_on='theme_b_make_form.__id',\n",
+    "    right_on='make_form.__id',\n",
+    "    how='right'\n",
+    ")\n",
+    "\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    breaking_condition_df,\n",
+    "    left_on='theme_b_breaking_evaluation_results.condition_id',\n",
+    "    right_on='theme_b_breaking_evaluation_conditions.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "df = pd.merge(\n",
+    "    df,\n",
+    "    vehicle_df,\n",
+    "    left_on='theme_b_breaking_evaluation_conditions.vehicle',\n",
+    "    right_on='theme_b_vehicle.__id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# 結合した全てのDataFrameのキーを保持する\n",
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "526e503e-41a8-4561-bbc9-2c0945393eef",
+   "metadata": {},
+   "source": [
+    "## データを可視化する\n",
+    "\n",
+    "結合したデータを様々なライブラリを用いて可視化することも可能です。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4a251cff-9b10-4d95-8d61-cb1cf0560a2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# グラフの描画\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "df.plot.scatter(x='theme_b_breaking_evaluation_conditions.speed', y='theme_b_breaking_evaluation_results.duration', s=100)\n",
+    "plt.title('Relation Start Speed - Breaking Time')\n",
+    "plt.xlabel('Start Speed')\n",
+    "plt.ylabel('Breaking Time')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b734674-1aeb-4787-a7c9-2acbb18a65a6",
+   "metadata": {},
+   "source": [
+    "## 前処理と学習\n",
+    "\n",
+    "機械学習にかける前に前処理を行い、適切なモデル学習を行いましょう。\n",
+    "- 目的変数と説明変数を絞る\n",
+    "- 欠損値を埋める\n",
+    "- ラベルエンコード etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ff5c74d9-4975-4a0b-ba08-e68e4cb6c624",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
+      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
+      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
+      "1.0768654617049684\n"
+     ]
+    }
+   ],
+   "source": [
+    "import lightgbm as lgb\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# 説明変数と目的変数の分離\n",
+    "X = df[[\n",
+    "    'theme_b_rubber.param1',\n",
+    "    'theme_b_rubber.param2',\n",
+    "    'theme_b_carbon_black.param1',\n",
+    "    'theme_b_carbon_black.param2',\n",
+    "    'theme_b_vehicle.weight',\n",
+    "    'theme_b_make_form.tread_width',\n",
+    "    'theme_b_breaking_evaluation_conditions.speed',\n",
+    "    'theme_b_breaking_evaluation_results.uneven_wear'\n",
+    "]]\n",
+    "y = df[['theme_b_breaking_evaluation_results.duration']]\n",
+    "\n",
+    "# 訓練データとテストデータに分離\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# LightGBMのデータセットに変換\n",
+    "train_data = lgb.Dataset(X_train, label=y_train)\n",
+    "test_data = lgb.Dataset(X_test, label=y_test, reference=train_data)\n",
+    "\n",
+    "# ハイパーパラメータの設定\n",
+    "params = {\n",
+    "    \"objective\": \"regression\",\n",
+    "    \"metric\": \"mse\",\n",
+    "    \"max_depth\": 20,\n",
+    "    \"min_data_in_bin\": 3,\n",
+    "    \"min_data_in_leaf\": 3,\n",
+    "    \"verbose\": 0\n",
+    "  }\n",
+    "\n",
+    "# モデルの訓練\n",
+    "model = lgb.train(params, train_data, valid_sets=[train_data, test_data], num_boost_round=100)\n",
+    "\n",
+    "# 予測\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# 精度の計算\n",
+    "mse: float = mean_squared_error(y_test, y_pred)\n",
+    "print(mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b03e23ac-7bfe-44a6-aedc-7ce015f9dcd0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "492017ff-b1d2-44cb-87fd-b5b2e2665aaa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}