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8481103e4d
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cheese.ipynb
64
cheese.ipynb
@ -335,6 +335,17 @@
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" return df.copy()"
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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": "fda6aaad-7b1e-4daa-8d28-cd049df9cec2",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_features=filter_df(data)\n",
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"data_features"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1eb67d7-d16b-4b93-8486-582830ac3903",
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@ -407,9 +418,11 @@
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]
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},
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{
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"cell_type": "markdown",
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"id": "48cbb634-6754-4956-a945-539d329812ef",
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"cell_type": "code",
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"execution_count": null,
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"id": "24e7ff6e-c308-4cc8-aeac-eeb372f4c479",
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"metadata": {},
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"outputs": [],
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"source": [
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"In this part, we achieved to do two things for the classification: create a decision tree on the database and, given a cheese and its characteristics, find where it originates from. \n",
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"\n"
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@ -507,8 +520,27 @@
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"metadata": {},
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"source": [
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"Not good, even quite bad. \n",
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"We cannot find the region a cheese originates from given its characteristic. \n",
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"\n",
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"In short, it seems that we cannot find the region a cheese originates from given its characteristic. "
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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": "7fd507d0-1a68-4cd7-a12e-12c9ab1061e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"model.predict(X)"
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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": "9faf2aee-84f5-4633-b3de-039af42d31d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"yprime=pd.DataFrame(model.predict(X),columns=[\"latitude\",\"longitude\"])\n",
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"\n"
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]
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},
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@ -578,9 +610,11 @@
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]
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},
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{
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"cell_type": "markdown",
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"id": "84bf779a-36d0-4aa2-b3a2-0da9bb25fc01",
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"cell_type": "code",
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"execution_count": null,
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"id": "78ef08e7-1436-440f-b035-8b480af1cc7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"For Pattern Mining, we only kept relevant columns (binary attributes) thus dropping RGB colors and any location based information, keeping only information relevant to the final cheese itself.\n",
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"\n",
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@ -588,24 +622,6 @@
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"\n",
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"Est-ce que les fromages artisanaux ont souvent plus de \"goûts\" que les autres ?\n"
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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": "ce4db7c9-8049-4838-af30-b9fe2bca2925",
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"metadata": {},
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"outputs": [],
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"source": [
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"len(data_features[data_features[\"pecorino\"] == True]), len(data_features[data_features[\"pecorino\"] == 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": "7d9f17c2-6c42-4f24-b0d0-e8640a661801",
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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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