diff --git a/your-code/boston_data.csv b/your-code/boston_data.csv
new file mode 100644
index 0000000..996fc2f
--- /dev/null
+++ b/your-code/boston_data.csv
@@ -0,0 +1,405 @@
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+0.02055,85.0,0.74,0.0,0.41,6.383,35.7,9.1876,2.0,313.0,17.3,396.9,5.77,24.7
+0.08244,30.0,4.93,0.0,0.428,6.481,18.5,6.1899,6.0,300.0,16.6,379.41,6.36,23.7
diff --git a/your-code/lab_boston_housing.ipynb b/your-code/lab_boston_housing.ipynb
index 4a25b49..fdb57c8 100644
--- a/your-code/lab_boston_housing.ipynb
+++ b/your-code/lab_boston_housing.ipynb
@@ -35,11 +35,178 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
- "# Your code here"
+ "\n",
+ "# Your code here\n",
+ "\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " crim | \n",
+ " zn | \n",
+ " indus | \n",
+ " chas | \n",
+ " nox | \n",
+ " rm | \n",
+ " age | \n",
+ " dis | \n",
+ " rad | \n",
+ " tax | \n",
+ " ptratio | \n",
+ " black | \n",
+ " lstat | \n",
+ " medv | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.15876 | \n",
+ " 0.0 | \n",
+ " 10.81 | \n",
+ " 0.0 | \n",
+ " 0.413 | \n",
+ " 5.961 | \n",
+ " 17.5 | \n",
+ " 5.2873 | \n",
+ " 4.0 | \n",
+ " 305.0 | \n",
+ " 19.2 | \n",
+ " 376.94 | \n",
+ " 9.88 | \n",
+ " 21.7 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.10328 | \n",
+ " 25.0 | \n",
+ " 5.13 | \n",
+ " 0.0 | \n",
+ " 0.453 | \n",
+ " 5.927 | \n",
+ " 47.2 | \n",
+ " 6.9320 | \n",
+ " 8.0 | \n",
+ " 284.0 | \n",
+ " 19.7 | \n",
+ " 396.90 | \n",
+ " 9.22 | \n",
+ " 19.6 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.34940 | \n",
+ " 0.0 | \n",
+ " 9.90 | \n",
+ " 0.0 | \n",
+ " 0.544 | \n",
+ " 5.972 | \n",
+ " 76.7 | \n",
+ " 3.1025 | \n",
+ " 4.0 | \n",
+ " 304.0 | \n",
+ " 18.4 | \n",
+ " 396.24 | \n",
+ " 9.97 | \n",
+ " 20.3 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2.73397 | \n",
+ " 0.0 | \n",
+ " 19.58 | \n",
+ " 0.0 | \n",
+ " 0.871 | \n",
+ " 5.597 | \n",
+ " 94.9 | \n",
+ " 1.5257 | \n",
+ " 5.0 | \n",
+ " 403.0 | \n",
+ " 14.7 | \n",
+ " 351.85 | \n",
+ " 21.45 | \n",
+ " 15.4 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.04337 | \n",
+ " 21.0 | \n",
+ " 5.64 | \n",
+ " 0.0 | \n",
+ " 0.439 | \n",
+ " 6.115 | \n",
+ " 63.0 | \n",
+ " 6.8147 | \n",
+ " 4.0 | \n",
+ " 243.0 | \n",
+ " 16.8 | \n",
+ " 393.97 | \n",
+ " 9.43 | \n",
+ " 20.5 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Your plots here\n",
+ "sns.pairplot(df, y_vars=df.columns, x_vars=\"medv\", kind = \"scatter\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "crim 0\n",
+ "zn 0\n",
+ "indus 0\n",
+ "chas 0\n",
+ "nox 0\n",
+ "rm 0\n",
+ "age 0\n",
+ "dis 0\n",
+ "rad 0\n",
+ "tax 0\n",
+ "ptratio 0\n",
+ "black 0\n",
+ "lstat 0\n",
+ "medv 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "boston.isna().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# Your plots here"
+ "# https://seaborn.pydata.org/generated/seaborn.heatmap.html\n",
+ "corr = boston.corr()\n",
+ "ax = sns.heatmap(corr)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -88,7 +373,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# Your response here"
+ "# Your response here\n"
]
},
{
@@ -150,13 +435,267 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
- "# Your code here"
+ "# Your code here\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# nimmt sample aus Gesamtheit\n",
+ "boston_shuffle = boston.sample(len(boston))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(404, 14)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "boston.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " crim | \n",
+ " zn | \n",
+ " indus | \n",
+ " chas | \n",
+ " nox | \n",
+ " rm | \n",
+ " age | \n",
+ " dis | \n",
+ " rad | \n",
+ " tax | \n",
+ " ptratio | \n",
+ " black | \n",
+ " lstat | \n",
+ " medv | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 42 | \n",
+ " 0.04462 | \n",
+ " 25.0 | \n",
+ " 4.86 | \n",
+ " 0.0 | \n",
+ " 0.426 | \n",
+ " 6.619 | \n",
+ " 70.4 | \n",
+ " 5.4007 | \n",
+ " 4.0 | \n",
+ " 281.0 | \n",
+ " 19.0 | \n",
+ " 395.63 | \n",
+ " 7.22 | \n",
+ " 23.9 | \n",
+ "
\n",
+ " \n",
+ " | 159 | \n",
+ " 9.59571 | \n",
+ " 0.0 | \n",
+ " 18.10 | \n",
+ " 0.0 | \n",
+ " 0.693 | \n",
+ " 6.404 | \n",
+ " 100.0 | \n",
+ " 1.6390 | \n",
+ " 24.0 | \n",
+ " 666.0 | \n",
+ " 20.2 | \n",
+ " 376.11 | \n",
+ " 20.31 | \n",
+ " 12.1 | \n",
+ "
\n",
+ " \n",
+ " | 94 | \n",
+ " 8.71675 | \n",
+ " 0.0 | \n",
+ " 18.10 | \n",
+ " 0.0 | \n",
+ " 0.693 | \n",
+ " 6.471 | \n",
+ " 98.8 | \n",
+ " 1.7257 | \n",
+ " 24.0 | \n",
+ " 666.0 | \n",
+ " 20.2 | \n",
+ " 391.98 | \n",
+ " 17.12 | \n",
+ " 13.1 | \n",
+ "
\n",
+ " \n",
+ " | 100 | \n",
+ " 0.12744 | \n",
+ " 0.0 | \n",
+ " 6.91 | \n",
+ " 0.0 | \n",
+ " 0.448 | \n",
+ " 6.770 | \n",
+ " 2.9 | \n",
+ " 5.7209 | \n",
+ " 3.0 | \n",
+ " 233.0 | \n",
+ " 17.9 | \n",
+ " 385.41 | \n",
+ " 4.84 | \n",
+ " 26.6 | \n",
+ "
\n",
+ " \n",
+ " | 193 | \n",
+ " 0.09849 | \n",
+ " 0.0 | \n",
+ " 25.65 | \n",
+ " 0.0 | \n",
+ " 0.581 | \n",
+ " 5.879 | \n",
+ " 95.8 | \n",
+ " 2.0063 | \n",
+ " 2.0 | \n",
+ " 188.0 | \n",
+ " 19.1 | \n",
+ " 379.38 | \n",
+ " 17.58 | \n",
+ " 18.8 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "