manually merge different versions
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@ -11,7 +11,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "3eb339fa-ef85-4544-9ad0-bc22d4de9f1a",
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"metadata": {},
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"outputs": [
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@ -158,7 +158,7 @@
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"4 0.0 3.0 0 "
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]
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},
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -190,7 +190,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"id": "6f7e6a3a-63cb-40e2-8746-937c24b184ef",
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"metadata": {},
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"outputs": [
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@ -214,7 +214,7 @@
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"dtype: int64"
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]
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},
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"execution_count": 2,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -225,7 +225,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"id": "d1639e92-d401-49fb-a1f1-67250ffa2c81",
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"metadata": {},
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"outputs": [],
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@ -233,16 +233,45 @@
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"df.dropna(inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "192da26d-0fb1-4b06-a046-a41b66576ed0",
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"metadata": {},
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"source": [
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"<h1>Preprocessing</h1>\n",
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"<p>Split</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 17,
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"id": "24675f41-d48f-4e27-a3d8-e303556ee7d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"df['goal'] = df['goal'].replace({0: 0, 1: 1, 2: 1, 3: 1, 4: 1})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "d7bf2c46-7885-4dfe-a4e7-8b8439cf0434",
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"metadata": {},
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"outputs": [],
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"source": [
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"# save 'cleaned' dataset as csv file for further processing\n",
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"import os\n",
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"os.makedirs('data', exist_ok=True)\n",
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"df.to_csv('./data/dataset_cleaned.csv', index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ce134731-e15e-4a2a-aa4e-a28de45fd3af",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -261,7 +290,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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