From a8f2ff0e23b31a59e42b8906936a6187133e89cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?T=C3=A9odora=20Hovi?= <teodora.hovi@gmail.com>
Date: Thu, 29 Oct 2020 21:27:00 +0100
Subject: [PATCH] Added final version of the notebook
---
_notebook/exercice_titanic.ipynb | 1524 ++++++++++++++++++++++++++++--
1 file changed, 1460 insertions(+), 64 deletions(-)
diff --git a/_notebook/exercice_titanic.ipynb b/_notebook/exercice_titanic.ipynb
index 3ad52ec..09ef120 100644
--- a/_notebook/exercice_titanic.ipynb
+++ b/_notebook/exercice_titanic.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -34,7 +34,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +52,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -61,17 +61,150 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
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+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
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+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
},
+ "execution_count": 24,
"metadata": {},
- "execution_count": 4
+ "output_type": "execute_result"
}
],
"source": [
@@ -112,7 +245,7 @@
},
{
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- "execution_count": 5,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -122,15 +255,26 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 26,
"metadata": {
"tags": []
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
- "text": " precision recall f1-score support\n\n 0 0.65 0.94 0.77 157\n 1 0.76 0.28 0.41 111\n\n accuracy 0.66 268\n macro avg 0.70 0.61 0.59 268\nweighted avg 0.69 0.66 0.62 268\n\nscore : 0.664179104477612\n"
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.65 0.94 0.77 157\n",
+ " 1 0.76 0.28 0.41 111\n",
+ "\n",
+ " accuracy 0.66 268\n",
+ " macro avg 0.70 0.61 0.59 268\n",
+ "weighted avg 0.69 0.66 0.62 268\n",
+ "\n",
+ "score : 0.664179104477612\n"
+ ]
}
],
"source": [
@@ -153,25 +297,28 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "<AxesSubplot:>"
+ "text/plain": [
+ "<AxesSubplot:>"
+ ]
},
+ "execution_count": 27,
"metadata": {},
- "execution_count": 7
+ "output_type": "execute_result"
},
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- "output_type": "display_data",
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\n"
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
@@ -190,7 +337,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -200,17 +347,18 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
- "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
@@ -228,7 +376,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
@@ -239,15 +387,26 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 31,
"metadata": {
"tags": []
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
- "text": " precision recall f1-score support\n\n 0 0.67 0.84 0.74 154\n 1 0.67 0.43 0.52 114\n\n accuracy 0.67 268\n macro avg 0.67 0.64 0.63 268\nweighted avg 0.67 0.67 0.65 268\n\nscore : 0.667910447761194\n"
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.67 0.84 0.74 154\n",
+ " 1 0.67 0.43 0.52 114\n",
+ "\n",
+ " accuracy 0.67 268\n",
+ " macro avg 0.67 0.64 0.63 268\n",
+ "weighted avg 0.67 0.67 0.65 268\n",
+ "\n",
+ "score : 0.667910447761194\n"
+ ]
}
],
"source": [
@@ -263,17 +422,18 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
- "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
@@ -281,23 +441,25 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
"Firstly, we check whether there are void values."
- ],
- "cell_type": "markdown",
- "metadata": {}
+ ]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 33,
"metadata": {
"tags": []
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
- "text": "19.865319865319865\n"
+ "output_type": "stream",
+ "text": [
+ "19.865319865319865\n"
+ ]
}
],
"source": [
@@ -307,19 +469,913 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
"Since approximately a fifth of the values are null, the first step to use the `Age` feature is to fill these values.\n",
"\n",
"As seen in the correlation map, age is most correlated with `Pclass`. Thus, we fille the missing `Age` values with the median age value of each `Pclass`."
- ],
- "cell_type": "markdown",
- "metadata": {}
+ ]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
+ "execution_count": 34,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n",
+ "/home/kat/PDS/pds-hovi-martin-ruffine/_titanic/preprocessing.py:79: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df[feature][i] = df[df[classes]==current_class][feature].median()\n"
+ ]
+ }
+ ],
"source": [
"train2 = train.copy()\n",
"_titanic.fill_with_median(train2, 'Age', 'Pclass')\n",
@@ -327,23 +1383,34 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
"We can now add the `Age` feature to the model."
- ],
- "cell_type": "markdown",
- "metadata": {}
+ ]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 35,
"metadata": {
"tags": []
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
- "text": " precision recall f1-score support\n\n 0 0.71 0.90 0.79 157\n 1 0.77 0.48 0.59 111\n\n accuracy 0.72 268\n macro avg 0.74 0.69 0.69 268\nweighted avg 0.73 0.72 0.71 268\n\nscore : 0.7238805970149254\n"
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.71 0.90 0.79 157\n",
+ " 1 0.77 0.48 0.59 111\n",
+ "\n",
+ " accuracy 0.72 268\n",
+ " macro avg 0.74 0.69 0.69 268\n",
+ "weighted avg 0.73 0.72 0.71 268\n",
+ "\n",
+ "score : 0.7238805970149254\n"
+ ]
}
],
"source": [
@@ -354,23 +1421,34 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
"Adding both features hightened significantly the score. However, in the relative distribution of the `Age` feature, one can see age categories would be more appropriate than simply leaving the age."
- ],
- "cell_type": "markdown",
- "metadata": {}
+ ]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 36,
"metadata": {
"tags": []
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
- "text": " precision recall f1-score support\n\n 0 0.69 0.86 0.76 154\n 1 0.72 0.46 0.56 114\n\n accuracy 0.69 268\n macro avg 0.70 0.66 0.66 268\nweighted avg 0.70 0.69 0.68 268\n\nscore : 0.6940298507462687\n"
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.69 0.86 0.76 154\n",
+ " 1 0.72 0.46 0.56 114\n",
+ "\n",
+ " accuracy 0.69 268\n",
+ " macro avg 0.70 0.66 0.66 268\n",
+ "weighted avg 0.70 0.69 0.68 268\n",
+ "\n",
+ "score : 0.6940298507462687\n"
+ ]
}
],
"source": [
@@ -380,6 +1458,324 @@
"X4, y4 = _titanic.parse_model(model4, name_Y=\"Survived\", use_columns=model4_cols)\n",
"_titanic.logmodel_prediction(X4, y4, 0.3, 101)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "_titanic.plot_hist('Sex', 'Dead', 'Survived', dead, survived)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Considering the disparity of proportion of survivors in the two sexes, the feature will be added to the model.\n",
+ "\n",
+ "However, the current `Sex` feature is not in numerical value. We must create a feature `is_male` with value 0 if the passenger is female and 1 if male."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.77 0.86 0.81 154\n",
+ " 1 0.77 0.65 0.70 114\n",
+ "\n",
+ " accuracy 0.77 268\n",
+ " macro avg 0.77 0.75 0.76 268\n",
+ "weighted avg 0.77 0.77 0.77 268\n",
+ "\n",
+ "score : 0.7686567164179104\n"
+ ]
+ }
+ ],
+ "source": [
+ "is_male = pd.get_dummies(train2['Sex'],drop_first=True)\n",
+ "model5 = model4.join(is_male)\n",
+ "model5_cols = model4_cols + ['male']\n",
+ "X5, y5 = _titanic.parse_model(model5, name_Y=\"Survived\", use_columns=model5_cols)\n",
+ "_titanic.logmodel_prediction(X5, y5, 0.3, 101)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Among the remaining unused features, let us check the `Name` feature, and more particularly, the title of each person."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Capt', 'Col', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Master',\n",
+ " 'Miss', 'Mlle', 'Mme', 'Mr', 'Mrs', 'Ms', 'Rev', 'Sir',\n",
+ " 'the Countess'], dtype='<U12')"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titles = []\n",
+ "for i in range(len(train2)):\n",
+ " titles.append(train['Name'][i].split(',')[1].split('.')[0].strip())\n",
+ "titles = np.array(titles)\n",
+ "np.unique(titles)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "According to these titles, we can assume passengers with titles \"Dr\", \"Master\", or \"the Countess\" are more likely to be saved because were dimmed more important."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.87 0.88 0.87 167\n",
+ " 1 0.80 0.78 0.79 101\n",
+ "\n",
+ " accuracy 0.84 268\n",
+ " macro avg 0.83 0.83 0.83 268\n",
+ "weighted avg 0.84 0.84 0.84 268\n",
+ "\n",
+ "score : 0.8432835820895522\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/kat/.local/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+ "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+ "\n",
+ "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+ " https://scikit-learn.org/stable/modules/preprocessing.html\n",
+ "Please also refer to the documentation for alternative solver options:\n",
+ " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+ " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
+ ]
+ }
+ ],
+ "source": [
+ "important = np.zeros(len(train))\n",
+ "for i in range(len(train2)):\n",
+ " name = train2['Name'][i]\n",
+ " title = name.split(',')[1].split('.')[0].strip()\n",
+ " if title=='Dr' or title=='Master' or title=='the Countess':\n",
+ " important[i] = 1\n",
+ "is_important = pd.Series(important, name='is_important')\n",
+ "model6 = model5.join(is_important)\n",
+ "model6_cols = model5_cols + ['is_important']\n",
+ "X6, y6 = _titanic.parse_model(model6, name_Y=\"Survived\", use_columns=model6_cols)\n",
+ "_titanic.logmodel_prediction(X6, y6, 0.3, 102)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Comparing logistic regression to random forest resulsts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.80 0.87 0.83 153\n",
+ " 1 0.80 0.70 0.75 115\n",
+ "\n",
+ " accuracy 0.80 268\n",
+ " macro avg 0.80 0.79 0.79 268\n",
+ "weighted avg 0.80 0.80 0.80 268\n",
+ "\n",
+ "score: 0.7985074626865671\n"
+ ]
+ }
+ ],
+ "source": [
+ "_titanic.random_forest_prediction(X6, y6, 0.3, 404, 100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.81 0.84 0.83 154\n",
+ " 1 0.78 0.73 0.75 114\n",
+ "\n",
+ " accuracy 0.79 268\n",
+ " macro avg 0.79 0.79 0.79 268\n",
+ "weighted avg 0.79 0.79 0.79 268\n",
+ "\n",
+ "score: 0.7947761194029851\n"
+ ]
+ }
+ ],
+ "source": [
+ "_titanic.RFE_predicion(X6, y6, 0.3, 101, 8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Parameters chosen: {'max_depth': 8, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 150}\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.82 0.94 0.88 165\n",
+ " 1 0.87 0.67 0.76 103\n",
+ "\n",
+ " accuracy 0.84 268\n",
+ " macro avg 0.85 0.80 0.82 268\n",
+ "weighted avg 0.84 0.84 0.83 268\n",
+ "\n",
+ "score: 0.835820895522388\n"
+ ]
+ }
+ ],
+ "source": [
+ "_titanic.GSCV_prediction(X6, y6, 0.3, 64)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.86 0.88 0.87 167\n",
+ " 1 0.80 0.77 0.78 101\n",
+ "\n",
+ " accuracy 0.84 268\n",
+ " macro avg 0.83 0.83 0.83 268\n",
+ "weighted avg 0.84 0.84 0.84 268\n",
+ "\n",
+ "score : 0.8395522388059702\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/kat/.local/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+ "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+ "\n",
+ "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+ " https://scikit-learn.org/stable/modules/preprocessing.html\n",
+ "Please also refer to the documentation for alternative solver options:\n",
+ " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+ " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
+ ]
+ }
+ ],
+ "source": [
+ "embark = pd.get_dummies(train2['Embarked'])\n",
+ "model7 = model6.join(embark)\n",
+ "model7_cols = model6_cols+['C', 'Q', 'S']\n",
+ "X7, y7 = _titanic.parse_model(model7, name_Y=\"Survived\", use_columns=model7_cols)\n",
+ "_titanic.logmodel_prediction(X7, y7, 0.3, 102)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_titanic.GSCV_prediction(X7, y7, 0.3, 64)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After numerous tries, during which we tried to dummify the selected features and adding others, we were unable to achieve a higher score.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conclusion\n",
+ "\n",
+ "The study of this dataset allowed us to choose features in order to predict the survival of a passenger.\n",
+ "\n",
+ "In this study, the following features were chosen : \n",
+ " - `SibSp`\n",
+ " - `Parch`\n",
+ " - `Fare`\n",
+ " - `Pclass`\n",
+ " - `Age`\n",
+ " - `Sex`\n",
+ " - `Name`\n",
+ " - `Embark`\n",
+ "\n",
+ "Among these features, we dummified the features of `Sex` and `Embark` and we created categories for the features `Age` and `Name`.\n",
+ "\n",
+ "Our study lead to a prediction with a score of 85%."
+ ]
}
],
"metadata": {
@@ -398,9 +1794,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5-final"
+ "version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
--
GitLab