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<?xml version="1.0" encoding="UTF-8"?>
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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{
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"cells": [
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "da1135f1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"arr = np.array([4.,7.,2.,6.]).reshape(2,2)\n",
|
||||
"mat = np.matrix(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0108e1d6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Macierze "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "19ba64f7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
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||||
"text": [
|
||||
"[[4. 7.]\n",
|
||||
" [2. 6.]]\n",
|
||||
"[[4. 7.]\n",
|
||||
" [2. 6.]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# obiekty array i matrix wyświetlają się tak samo\n",
|
||||
"\n",
|
||||
"print(arr)\n",
|
||||
"print(mat)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "3f697dfd",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[0.25 0.14285714]\n",
|
||||
" [0.5 0.16666667]]\n",
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# jednak dla działania **-1 wynik wychodzi zupełnie inny\n",
|
||||
"# dla obiektu array działanie **-1 powoduję odwrócenie każdej liczby w tablicy\n",
|
||||
"# dla obiektu matrix działanie **-1 powoduję odwrócenie całej macierzy tak że 'mat * mat^-1 = 1'\n",
|
||||
"\n",
|
||||
"print(arr**-1)\n",
|
||||
"print(mat**-1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "ea293b68",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n",
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(mat**-1)\n",
|
||||
"print(np.linalg.inv(mat))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
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"display_name": "Python 3 (ipykernel)",
|
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"language": "python",
|
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"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
|
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"version": 3
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},
|
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"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
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@ -0,0 +1,129 @@
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|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "61b00965",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"arr = np.arange(float(1),float(17)).reshape(4,4)\n",
|
||||
"mat = np.matrix(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fba618ad",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Macierze "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "aa353455",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[ 1. 2. 3. 4.]\n",
|
||||
" [ 5. 6. 7. 8.]\n",
|
||||
" [ 9. 10. 11. 12.]\n",
|
||||
" [13. 14. 15. 16.]]\n",
|
||||
"[[ 1. 2. 3. 4.]\n",
|
||||
" [ 5. 6. 7. 8.]\n",
|
||||
" [ 9. 10. 11. 12.]\n",
|
||||
" [13. 14. 15. 16.]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(arr)\n",
|
||||
"print(mat)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "177edde8",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[1. 0.5 0.33333333 0.25 ]\n",
|
||||
" [0.2 0.16666667 0.14285714 0.125 ]\n",
|
||||
" [0.11111111 0.1 0.09090909 0.08333333]\n",
|
||||
" [0.07692308 0.07142857 0.06666667 0.0625 ]]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "LinAlgError",
|
||||
"evalue": "Singular matrix",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn [22], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(arr\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(mat\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
|
||||
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py:231\u001b[0m, in \u001b[0;36mmatrix.__pow__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__pow__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m--> 231\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmatrix_power\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mmatrix_power\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
|
||||
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py:643\u001b[0m, in \u001b[0;36mmatrix_power\u001b[0;34m(a, n)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 643\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 644\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mabs\u001b[39m(n)\n\u001b[1;32m 646\u001b[0m \u001b[38;5;66;03m# short-cuts.\u001b[39;00m\n",
|
||||
"File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
|
||||
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py:545\u001b[0m, in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 543\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 544\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_singular)\n\u001b[0;32m--> 545\u001b[0m ainv \u001b[38;5;241m=\u001b[39m \u001b[43m_umath_linalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minv\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap(ainv\u001b[38;5;241m.\u001b[39mastype(result_t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n",
|
||||
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py:88\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[0;32m---> 88\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSingular matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||||
"\u001b[0;31mLinAlgError\u001b[0m: Singular matrix"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(arr**-1)\n",
|
||||
"print(mat**-1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9c8bdb64",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6470536f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,3 @@
|
|||
# X^+Y^3
|
||||
# X^2-Y^2
|
||||
# punkt siadłowy
|
|
@ -0,0 +1,120 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "da1135f1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"arr = np.array([4.,7.,2.,6.]).reshape(2,2)\n",
|
||||
"mat = np.matrix(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0108e1d6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Macierze "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "19ba64f7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[4. 7.]\n",
|
||||
" [2. 6.]]\n",
|
||||
"[[4. 7.]\n",
|
||||
" [2. 6.]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# obiekty array i matrix wyświetlają się tak samo\n",
|
||||
"\n",
|
||||
"print(arr)\n",
|
||||
"print(mat)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "3f697dfd",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[0.25 0.14285714]\n",
|
||||
" [0.5 0.16666667]]\n",
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# jednak dla działania **-1 wynik wychodzi zupełnie inny\n",
|
||||
"# dla obiektu array działanie **-1 powoduję odwrócenie każdej liczby w tablicy\n",
|
||||
"# dla obiektu matrix działanie **-1 powoduję odwrócenie całej macierzy tak że 'mat * mat^-1 = 1'\n",
|
||||
"\n",
|
||||
"print(arr**-1)\n",
|
||||
"print(mat**-1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "ea293b68",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n",
|
||||
"[[ 0.6 -0.7]\n",
|
||||
" [-0.2 0.4]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(mat**-1)\n",
|
||||
"print(np.linalg.inv(mat))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,6 @@
|
|||
import numpy as np
|
||||
|
||||
X = np.matrix([[1.,2.,3.],[1.,3.,6.]])
|
||||
y = np.matrix([[5.],[6.]])
|
||||
result = ((X.T * X) ** -1) * X.T * y
|
||||
print(result)
|
|
@ -0,0 +1,10 @@
|
|||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
data = pd.read_csv("data2.csv")
|
||||
data = data.to_numpy()
|
||||
|
||||
plt.plot(data[:,1], data[:,6], "go")
|
||||
plt.xlabel("X - 2nd column")
|
||||
plt.ylabel("Y - 7th column")
|
||||
plt.show()
|
|
@ -0,0 +1,19 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
X = np.arange(-1.0, 1.0, 0.025)
|
||||
|
||||
a = 9
|
||||
b = 6
|
||||
c = 8
|
||||
|
||||
Y = (a-4)*(X**2) + (b-5)*X + (c-6)
|
||||
|
||||
G = np.exp(X)/(np.exp(X) + 1)
|
||||
|
||||
plt.plot(X,Y, 'g', label="F(x) = 5x^2 + x + 2")
|
||||
plt.plot(X,G, 'y', label="G(x) = e^x/(e^x+1)")
|
||||
plt.legend(loc="upper left")
|
||||
|
||||
plt.show()
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
from mpl_toolkits.mplot3d import Axes3D
|
||||
from matplotlib import cm
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
fig = plt.figure(figsize=(10, 8))
|
||||
ax = fig.add_subplot(111, projection="3d")
|
||||
|
||||
X = np.arange(-5, 5, 0.25)
|
||||
Y = np.arange(-5, 5, 0.25)
|
||||
X, Y = np.meshgrid(X, Y)
|
||||
|
||||
Z =-1*(X**2 + Y**3)
|
||||
|
||||
surf = ax.plot_surface(
|
||||
X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0, antialiased=True
|
||||
)
|
||||
|
||||
plt.show()
|
|
@ -0,0 +1,178 @@
|
|||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
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3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
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3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
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3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
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3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
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3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
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3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
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3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
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3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
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3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
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||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
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3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
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3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
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||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
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||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
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3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
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||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
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||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
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||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
|
|
@ -0,0 +1,12 @@
|
|||
import pandas as pd
|
||||
import matplotlib.pylot as plt
|
||||
|
||||
|
||||
data = pd.read_csv("data2.csv")
|
||||
data_array = data.to_numpy()
|
||||
|
||||
x = data_array[:,2]
|
||||
y = data_array[:,7]
|
||||
|
||||
plt.plot(x,y,"gx")
|
||||
plt.show()
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
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|
|||
6.2,29
|
||||
9.5,44
|
||||
10.5,36
|
||||
7.7,37
|
||||
8.6,53
|
||||
34.1,68
|
||||
11,75
|
||||
6.9,18
|
||||
7.3,31
|
||||
15.1,25
|
||||
29.1,34
|
||||
2.2,14
|
||||
5.7,11
|
||||
2,11
|
||||
2.5,22
|
||||
4,16
|
||||
5.4,27
|
||||
2.2,9
|
||||
7.2,29
|
||||
15.1,30
|
||||
16.5,40
|
||||
18.4,32
|
||||
36.2,41
|
||||
39.7,147
|
||||
18.5,22
|
||||
23.3,29
|
||||
12.2,46
|
||||
5.6,23
|
||||
21.8,4
|
||||
21.6,31
|
||||
9,39
|
||||
3.6,15
|
||||
5,32
|
||||
28.6,27
|
||||
17.4,32
|
||||
11.3,34
|
||||
3.4,17
|
||||
11.9,46
|
||||
10.5,42
|
||||
10.7,43
|
||||
10.8,34
|
||||
4.8,19
|
|
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|
@ -0,0 +1,582 @@
|
|||
{
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|
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|
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <tr>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass \\\n",
|
||||
"0 0 530 2 \n",
|
||||
"1 0 466 3 \n",
|
||||
"2 0 753 3 \n",
|
||||
"3 0 855 2 \n",
|
||||
"4 0 333 1 \n",
|
||||
"\n",
|
||||
" Name Sex Age SibSp Parch \\\n",
|
||||
"0 Hocking\\t Mr. Richard George male 23.0 2 1 \n",
|
||||
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|
||||
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|
||||
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|
||||
"4 Graham\\t Mr. George Edward male 38.0 0 1 \n",
|
||||
"\n",
|
||||
" Ticket Fare Cabin Embarked \n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
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|
||||
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|
||||
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|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"\n",
|
||||
"data = pd.read_csv('titanic.tsv',sep='\\t')\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
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|
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|
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" <th>2</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>753</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>33.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>345780</td>\n",
|
||||
" <td>9.5000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>44.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>244252</td>\n",
|
||||
" <td>26.0000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>333</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>PC 17582</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>C91</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 29104 \n",
|
||||
"1 0 466 3 1 38.0 0 0 SOTON/O.Q. 3101306 \n",
|
||||
"2 0 753 3 1 33.0 0 0 345780 \n",
|
||||
"3 0 855 2 0 44.0 1 0 244252 \n",
|
||||
"4 0 333 1 1 38.0 0 1 PC 17582 \n",
|
||||
"\n",
|
||||
" Fare Cabin Embarked Name_to_num \n",
|
||||
"0 11.5000 NaN S 1 \n",
|
||||
"1 7.0500 NaN S 1 \n",
|
||||
"2 9.5000 NaN S 1 \n",
|
||||
"3 26.0000 NaN S 0 \n",
|
||||
"4 153.4625 C91 S 1 "
|
||||
]
|
||||
},
|
||||
"execution_count": 255,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
|
||||
"data['Name_to_num'] = data['Name'].apply(\n",
|
||||
" lambda x: 1 if 'Mr.' in x else 0\n",
|
||||
")\n",
|
||||
"del data['Name']\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 256,
|
||||
"id": "9253cb6e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Cabin'])\n",
|
||||
"vector = vectorizer.transform(data['Cabin']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Cabin']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 257,
|
||||
"id": "0d915dab",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"data = pd.get_dummies(data,columns=['Embarked'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 258,
|
||||
"id": "2f641e28",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"28.00 139\n",
|
||||
"22.00 20\n",
|
||||
"21.00 19\n",
|
||||
"24.00 19\n",
|
||||
"19.00 17\n",
|
||||
" ... \n",
|
||||
"61.00 1\n",
|
||||
"70.50 1\n",
|
||||
"0.75 1\n",
|
||||
"10.00 1\n",
|
||||
"46.00 1\n",
|
||||
"Name: Age, Length: 82, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 258,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
|
||||
"data[['Age']] = imputer.fit_transform(data[['Age']])\n",
|
||||
"data['Age'].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 259,
|
||||
"id": "536d5bd7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Ticket'])\n",
|
||||
"vector = vectorizer.transform(data['Ticket']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Ticket']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 260,
|
||||
"id": "74e47288",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Survived</th>\n",
|
||||
" <th>PassengerId</th>\n",
|
||||
" <th>Pclass</th>\n",
|
||||
" <th>Sex</th>\n",
|
||||
" <th>Age</th>\n",
|
||||
" <th>SibSp</th>\n",
|
||||
" <th>Parch</th>\n",
|
||||
" <th>Ticket</th>\n",
|
||||
" <th>Fare</th>\n",
|
||||
" <th>Cabin</th>\n",
|
||||
" <th>Name_to_num</th>\n",
|
||||
" <th>Embarked_C</th>\n",
|
||||
" <th>Embarked_Q</th>\n",
|
||||
" <th>Embarked_S</th>\n",
|
||||
" <th>Embarked_Undefined</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>530</td>\n",
|
||||
" <td>2</td>\n",
|
||||
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|
||||
" <td>23.0</td>\n",
|
||||
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|
||||
" <td>1</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>11.5000</td>\n",
|
||||
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|
||||
" <td>1</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>466</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.391284</td>\n",
|
||||
" <td>7.0500</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>3</td>\n",
|
||||
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|
||||
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|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>9.5000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
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|
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|
||||
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|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>44.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>26.0000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <td>0</td>\n",
|
||||
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|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>333</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.365721</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket Fare \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 1.000000 11.5000 \n",
|
||||
"1 0 466 3 1 38.0 0 0 1.391284 7.0500 \n",
|
||||
"2 0 753 3 1 33.0 0 0 1.000000 9.5000 \n",
|
||||
"3 0 855 2 0 44.0 1 0 1.000000 26.0000 \n",
|
||||
"4 0 333 1 1 38.0 0 1 1.365721 153.4625 \n",
|
||||
"\n",
|
||||
" Cabin Name_to_num Embarked_C Embarked_Q Embarked_S Embarked_Undefined \n",
|
||||
"0 1.0 1 0 0 1 0 \n",
|
||||
"1 1.0 1 0 0 1 0 \n",
|
||||
"2 1.0 1 0 0 1 0 \n",
|
||||
"3 1.0 0 0 0 1 0 \n",
|
||||
"4 1.0 1 0 0 1 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 260,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.head()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
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|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,582 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 254,
|
||||
"id": "8b45b299",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Survived</th>\n",
|
||||
" <th>PassengerId</th>\n",
|
||||
" <th>Pclass</th>\n",
|
||||
" <th>Name</th>\n",
|
||||
" <th>Sex</th>\n",
|
||||
" <th>Age</th>\n",
|
||||
" <th>SibSp</th>\n",
|
||||
" <th>Parch</th>\n",
|
||||
" <th>Ticket</th>\n",
|
||||
" <th>Fare</th>\n",
|
||||
" <th>Cabin</th>\n",
|
||||
" <th>Embarked</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>530</td>\n",
|
||||
" <td>2</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <td>2</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>466</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Goncalves\\t Mr. Manuel Estanslas</td>\n",
|
||||
" <td>male</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>SOTON/O.Q. 3101306</td>\n",
|
||||
" <td>7.0500</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
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|
||||
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|
||||
" <td>753</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Vande Velde\\t Mr. Johannes Joseph</td>\n",
|
||||
" <td>male</td>\n",
|
||||
" <td>33.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>345780</td>\n",
|
||||
" <td>9.5000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>Carter\\t Mrs. Ernest Courtenay (Lilian Hughes)</td>\n",
|
||||
" <td>female</td>\n",
|
||||
" <td>44.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>244252</td>\n",
|
||||
" <td>26.0000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>333</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>Graham\\t Mr. George Edward</td>\n",
|
||||
" <td>male</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>PC 17582</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>C91</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass \\\n",
|
||||
"0 0 530 2 \n",
|
||||
"1 0 466 3 \n",
|
||||
"2 0 753 3 \n",
|
||||
"3 0 855 2 \n",
|
||||
"4 0 333 1 \n",
|
||||
"\n",
|
||||
" Name Sex Age SibSp Parch \\\n",
|
||||
"0 Hocking\\t Mr. Richard George male 23.0 2 1 \n",
|
||||
"1 Goncalves\\t Mr. Manuel Estanslas male 38.0 0 0 \n",
|
||||
"2 Vande Velde\\t Mr. Johannes Joseph male 33.0 0 0 \n",
|
||||
"3 Carter\\t Mrs. Ernest Courtenay (Lilian Hughes) female 44.0 1 0 \n",
|
||||
"4 Graham\\t Mr. George Edward male 38.0 0 1 \n",
|
||||
"\n",
|
||||
" Ticket Fare Cabin Embarked \n",
|
||||
"0 29104 11.5000 NaN S \n",
|
||||
"1 SOTON/O.Q. 3101306 7.0500 NaN S \n",
|
||||
"2 345780 9.5000 NaN S \n",
|
||||
"3 244252 26.0000 NaN S \n",
|
||||
"4 PC 17582 153.4625 C91 S "
|
||||
]
|
||||
},
|
||||
"execution_count": 254,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"\n",
|
||||
"data = pd.read_csv('titanic.tsv',sep='\\t')\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 255,
|
||||
"id": "8b3702f6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Survived</th>\n",
|
||||
" <th>PassengerId</th>\n",
|
||||
" <th>Pclass</th>\n",
|
||||
" <th>Sex</th>\n",
|
||||
" <th>Age</th>\n",
|
||||
" <th>SibSp</th>\n",
|
||||
" <th>Parch</th>\n",
|
||||
" <th>Ticket</th>\n",
|
||||
" <th>Fare</th>\n",
|
||||
" <th>Cabin</th>\n",
|
||||
" <th>Embarked</th>\n",
|
||||
" <th>Name_to_num</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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||||
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|
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" <th>1</th>\n",
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|
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|
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|
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" <td>38.0</td>\n",
|
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" <td>0</td>\n",
|
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" <td>0</td>\n",
|
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|
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|
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
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" <tr>\n",
|
||||
" <th>3</th>\n",
|
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" <td>0</td>\n",
|
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|
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" <td>0</td>\n",
|
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
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" <td>0</td>\n",
|
||||
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|
||||
" <td>PC 17582</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>C91</td>\n",
|
||||
" <td>S</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
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|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 29104 \n",
|
||||
"1 0 466 3 1 38.0 0 0 SOTON/O.Q. 3101306 \n",
|
||||
"2 0 753 3 1 33.0 0 0 345780 \n",
|
||||
"3 0 855 2 0 44.0 1 0 244252 \n",
|
||||
"4 0 333 1 1 38.0 0 1 PC 17582 \n",
|
||||
"\n",
|
||||
" Fare Cabin Embarked Name_to_num \n",
|
||||
"0 11.5000 NaN S 1 \n",
|
||||
"1 7.0500 NaN S 1 \n",
|
||||
"2 9.5000 NaN S 1 \n",
|
||||
"3 26.0000 NaN S 0 \n",
|
||||
"4 153.4625 C91 S 1 "
|
||||
]
|
||||
},
|
||||
"execution_count": 255,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
|
||||
"data['Name_to_num'] = data['Name'].apply(\n",
|
||||
" lambda x: 1 if 'Mr.' in x else 0\n",
|
||||
")\n",
|
||||
"del data['Name']\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 256,
|
||||
"id": "9253cb6e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Cabin'])\n",
|
||||
"vector = vectorizer.transform(data['Cabin']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Cabin']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 257,
|
||||
"id": "0d915dab",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"data = pd.get_dummies(data,columns=['Embarked'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 258,
|
||||
"id": "2f641e28",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"28.00 139\n",
|
||||
"22.00 20\n",
|
||||
"21.00 19\n",
|
||||
"24.00 19\n",
|
||||
"19.00 17\n",
|
||||
" ... \n",
|
||||
"61.00 1\n",
|
||||
"70.50 1\n",
|
||||
"0.75 1\n",
|
||||
"10.00 1\n",
|
||||
"46.00 1\n",
|
||||
"Name: Age, Length: 82, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 258,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
|
||||
"data[['Age']] = imputer.fit_transform(data[['Age']])\n",
|
||||
"data['Age'].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 259,
|
||||
"id": "536d5bd7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Ticket'])\n",
|
||||
"vector = vectorizer.transform(data['Ticket']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Ticket']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 260,
|
||||
"id": "74e47288",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket Fare \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 1.000000 11.5000 \n",
|
||||
"1 0 466 3 1 38.0 0 0 1.391284 7.0500 \n",
|
||||
"2 0 753 3 1 33.0 0 0 1.000000 9.5000 \n",
|
||||
"3 0 855 2 0 44.0 1 0 1.000000 26.0000 \n",
|
||||
"4 0 333 1 1 38.0 0 1 1.365721 153.4625 \n",
|
||||
"\n",
|
||||
" Cabin Name_to_num Embarked_C Embarked_Q Embarked_S Embarked_Undefined \n",
|
||||
"0 1.0 1 0 0 1 0 \n",
|
||||
"1 1.0 1 0 0 1 0 \n",
|
||||
"2 1.0 1 0 0 1 0 \n",
|
||||
"3 1.0 0 0 0 1 0 \n",
|
||||
"4 1.0 1 0 0 1 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 260,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.head()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,624 @@
|
|||
"Survived" "PassengerId" "Pclass" "Name" "Sex" "Age" "SibSp" "Parch" "Ticket" "Fare" "Cabin" "Embarked"
|
||||
0 530 2 "Hocking Mr. Richard George" "male" 23 2 1 "29104" 11.5 "" "S"
|
||||
0 466 3 "Goncalves Mr. Manuel Estanslas" "male" 38 0 0 "SOTON/O.Q. 3101306" 7.05 "" "S"
|
||||
0 753 3 "Vande Velde Mr. Johannes Joseph" "male" 33 0 0 "345780" 9.5 "" "S"
|
||||
0 855 2 "Carter Mrs. Ernest Courtenay (Lilian Hughes)" "female" 44 1 0 "244252" 26 "" "S"
|
||||
0 333 1 "Graham Mr. George Edward" "male" 38 0 1 "PC 17582" 153.4625 "C91" "S"
|
||||
0 39 3 "Vander Planke Miss. Augusta Maria" "female" 18 2 0 "345764" 18 "" "S"
|
||||
0 236 3 "Harknett Miss. Alice Phoebe" "female" NA 0 0 "W./C. 6609" 7.55 "" "S"
|
||||
0 303 3 "Johnson Mr. William Cahoone Jr" "male" 19 0 0 "LINE" 0 "" "S"
|
||||
1 18 2 "Williams Mr. Charles Eugene" "male" NA 0 0 "244373" 13 "" "S"
|
||||
1 505 1 "Maioni Miss. Roberta" "female" 16 0 0 "110152" 86.5 "B79" "S"
|
||||
1 670 1 "Taylor Mrs. Elmer Zebley (Juliet Cummins Wright)" "female" NA 1 0 "19996" 52 "C126" "S"
|
||||
1 316 3 "Nilsson Miss. Helmina Josefina" "female" 26 0 0 "347470" 7.8542 "" "S"
|
||||
1 690 1 "Madill Miss. Georgette Alexandra" "female" 15 0 1 "24160" 211.3375 "B5" "S"
|
||||
1 718 2 "Troutt Miss. Edwina Celia ""Winnie""" "female" 27 0 0 "34218" 10.5 "E101" "S"
|
||||
1 580 3 "Jussila Mr. Eiriik" "male" 32 0 0 "STON/O 2. 3101286" 7.925 "" "S"
|
||||
0 697 3 "Kelly Mr. James" "male" 44 0 0 "363592" 8.05 "" "S"
|
||||
1 193 3 "Andersen-Jensen Miss. Carla Christine Nielsine" "female" 19 1 0 "350046" 7.8542 "" "S"
|
||||
0 634 1 "Parr Mr. William Henry Marsh" "male" NA 0 0 "112052" 0 "" "S"
|
||||
1 858 1 "Daly Mr. Peter Denis " "male" 51 0 0 "113055" 26.55 "E17" "S"
|
||||
1 329 3 "Goldsmith Mrs. Frank John (Emily Alice Brown)" "female" 31 1 1 "363291" 20.525 "" "S"
|
||||
0 784 3 "Johnston Mr. Andrew G" "male" NA 1 2 "W./C. 6607" 23.45 "" "S"
|
||||
0 407 3 "Widegren Mr. Carl/Charles Peter" "male" 51 0 0 "347064" 7.75 "" "S"
|
||||
0 747 3 "Abbott Mr. Rossmore Edward" "male" 16 1 1 "C.A. 2673" 20.25 "" "S"
|
||||
0 492 3 "Windelov Mr. Einar" "male" 21 0 0 "SOTON/OQ 3101317" 7.25 "" "S"
|
||||
1 534 3 "Peter Mrs. Catherine (Catherine Rizk)" "female" NA 0 2 "2668" 22.3583 "" "C"
|
||||
0 862 2 "Giles Mr. Frederick Edward" "male" 21 1 0 "28134" 11.5 "" "S"
|
||||
0 724 2 "Hodges Mr. Henry Price" "male" 50 0 0 "250643" 13 "" "S"
|
||||
0 685 2 "Brown Mr. Thomas William Solomon" "male" 60 1 1 "29750" 39 "" "S"
|
||||
0 344 2 "Sedgwick Mr. Charles Frederick Waddington" "male" 25 0 0 "244361" 13 "" "S"
|
||||
1 751 2 "Wells Miss. Joan" "female" 4 1 1 "29103" 23 "" "S"
|
||||
0 734 2 "Berriman Mr. William John" "male" 23 0 0 "28425" 13 "" "S"
|
||||
0 496 3 "Yousseff Mr. Gerious" "male" NA 0 0 "2627" 14.4583 "" "C"
|
||||
0 296 1 "Lewy Mr. Ervin G" "male" NA 0 0 "PC 17612" 27.7208 "" "C"
|
||||
0 516 1 "Walker Mr. William Anderson" "male" 47 0 0 "36967" 34.0208 "D46" "S"
|
||||
0 282 3 "Olsson Mr. Nils Johan Goransson" "male" 28 0 0 "347464" 7.8542 "" "S"
|
||||
0 218 2 "Jacobsohn Mr. Sidney Samuel" "male" 42 1 0 "243847" 27 "" "S"
|
||||
1 107 3 "Salkjelsvik Miss. Anna Kristine" "female" 21 0 0 "343120" 7.65 "" "S"
|
||||
0 878 3 "Petroff Mr. Nedelio" "male" 19 0 0 "349212" 7.8958 "" "S"
|
||||
1 291 1 "Barber Miss. Ellen ""Nellie""" "female" 26 0 0 "19877" 78.85 "" "S"
|
||||
0 722 3 "Jensen Mr. Svend Lauritz" "male" 17 1 0 "350048" 7.0542 "" "S"
|
||||
0 826 3 "Flynn Mr. John" "male" NA 0 0 "368323" 6.95 "" "Q"
|
||||
0 434 3 "Kallio Mr. Nikolai Erland" "male" 17 0 0 "STON/O 2. 3101274" 7.125 "" "S"
|
||||
0 404 3 "Hakkarainen Mr. Pekka Pietari" "male" 28 1 0 "STON/O2. 3101279" 15.85 "" "S"
|
||||
1 210 1 "Blank Mr. Henry" "male" 40 0 0 "112277" 31 "A31" "C"
|
||||
1 166 3 "Goldsmith Master. Frank John William ""Frankie""" "male" 9 0 2 "363291" 20.525 "" "S"
|
||||
0 201 3 "Vande Walle Mr. Nestor Cyriel" "male" 28 0 0 "345770" 9.5 "" "S"
|
||||
0 361 3 "Skoog Mr. Wilhelm" "male" 40 1 4 "347088" 27.9 "" "S"
|
||||
1 53 1 "Harper Mrs. Henry Sleeper (Myna Haxtun)" "female" 49 1 0 "PC 17572" 76.7292 "D33" "C"
|
||||
0 612 3 "Jardin Mr. Jose Neto" "male" NA 0 0 "SOTON/O.Q. 3101305" 7.05 "" "S"
|
||||
0 60 3 "Goodwin Master. William Frederick" "male" 11 5 2 "CA 2144" 46.9 "" "S"
|
||||
1 262 3 "Asplund Master. Edvin Rojj Felix" "male" 3 4 2 "347077" 31.3875 "" "S"
|
||||
1 674 2 "Wilhelms Mr. Charles" "male" 31 0 0 "244270" 13 "" "S"
|
||||
0 63 1 "Harris Mr. Henry Birkhardt" "male" 45 1 0 "36973" 83.475 "C83" "S"
|
||||
0 215 3 "Kiernan Mr. Philip" "male" NA 1 0 "367229" 7.75 "" "Q"
|
||||
0 618 3 "Lobb Mrs. William Arthur (Cordelia K Stanlick)" "female" 26 1 0 "A/5. 3336" 16.1 "" "S"
|
||||
1 789 3 "Dean Master. Bertram Vere" "male" 1 1 2 "C.A. 2315" 20.575 "" "S"
|
||||
1 312 1 "Ryerson Miss. Emily Borie" "female" 18 2 2 "PC 17608" 262.375 "B57 B59 B63 B66" "C"
|
||||
0 113 3 "Barton Mr. David John" "male" 22 0 0 "324669" 8.05 "" "S"
|
||||
0 314 3 "Hendekovic Mr. Ignjac" "male" 28 0 0 "349243" 7.8958 "" "S"
|
||||
1 517 2 "Lemore Mrs. (Amelia Milley)" "female" 34 0 0 "C.A. 34260" 10.5 "F33" "S"
|
||||
0 174 3 "Sivola Mr. Antti Wilhelm" "male" 21 0 0 "STON/O 2. 3101280" 7.925 "" "S"
|
||||
1 803 1 "Carter Master. William Thornton II" "male" 11 1 2 "113760" 120 "B96 B98" "S"
|
||||
0 479 3 "Karlsson Mr. Nils August" "male" 22 0 0 "350060" 7.5208 "" "S"
|
||||
1 445 3 "Johannesen-Bratthammer Mr. Bernt" "male" NA 0 0 "65306" 8.1125 "" "S"
|
||||
0 684 3 "Goodwin Mr. Charles Edward" "male" 14 5 2 "CA 2144" 46.9 "" "S"
|
||||
1 597 2 "Leitch Miss. Jessie Wills" "female" NA 0 0 "248727" 33 "" "S"
|
||||
0 229 2 "Fahlstrom Mr. Arne Jonas" "male" 18 0 0 "236171" 13 "" "S"
|
||||
0 121 2 "Hickman Mr. Stanley George" "male" 21 2 0 "S.O.C. 14879" 73.5 "" "S"
|
||||
0 526 3 "Farrell Mr. James" "male" 40.5 0 0 "367232" 7.75 "" "Q"
|
||||
1 360 3 "Mockler Miss. Helen Mary ""Ellie""" "female" NA 0 0 "330980" 7.8792 "" "Q"
|
||||
1 20 3 "Masselmani Mrs. Fatima" "female" NA 0 0 "2649" 7.225 "" "C"
|
||||
0 28 1 "Fortune Mr. Charles Alexander" "male" 19 3 2 "19950" 263 "C23 C25 C27" "S"
|
||||
0 761 3 "Garfirth Mr. John" "male" NA 0 0 "358585" 14.5 "" "S"
|
||||
1 487 1 "Hoyt Mrs. Frederick Maxfield (Jane Anne Forby)" "female" 35 1 0 "19943" 90 "C93" "S"
|
||||
0 385 3 "Plotcharsky Mr. Vasil" "male" NA 0 0 "349227" 7.8958 "" "S"
|
||||
0 364 3 "Asim Mr. Adola" "male" 35 0 0 "SOTON/O.Q. 3101310" 7.05 "" "S"
|
||||
1 124 2 "Webber Miss. Susan" "female" 32.5 0 0 "27267" 13 "E101" "S"
|
||||
0 165 3 "Panula Master. Eino Viljami" "male" 1 4 1 "3101295" 39.6875 "" "S"
|
||||
0 668 3 "Rommetvedt Mr. Knud Paust" "male" NA 0 0 "312993" 7.775 "" "S"
|
||||
0 596 3 "Van Impe Mr. Jean Baptiste" "male" 36 1 1 "345773" 24.15 "" "S"
|
||||
0 845 3 "Culumovic Mr. Jeso" "male" 17 0 0 "315090" 8.6625 "" "S"
|
||||
1 510 3 "Lang Mr. Fang" "male" 26 0 0 "1601" 56.4958 "" "S"
|
||||
0 135 2 "Sobey Mr. Samuel James Hayden" "male" 25 0 0 "C.A. 29178" 13 "" "S"
|
||||
1 196 1 "Lurette Miss. Elise" "female" 58 0 0 "PC 17569" 146.5208 "B80" "C"
|
||||
1 377 3 "Landergren Miss. Aurora Adelia" "female" 22 0 0 "C 7077" 7.25 "" "S"
|
||||
1 745 3 "Stranden Mr. Juho" "male" 31 0 0 "STON/O 2. 3101288" 7.925 "" "S"
|
||||
0 729 2 "Bryhl Mr. Kurt Arnold Gottfrid" "male" 25 1 0 "236853" 26 "" "S"
|
||||
1 661 1 "Frauenthal Dr. Henry William" "male" 50 2 0 "PC 17611" 133.65 "" "S"
|
||||
0 437 3 "Ford Miss. Doolina Margaret ""Daisy""" "female" 21 2 2 "W./C. 6608" 34.375 "" "S"
|
||||
1 9 3 "Johnson Mrs. Oscar W (Elisabeth Vilhelmina Berg)" "female" 27 0 2 "347742" 11.1333 "" "S"
|
||||
0 835 3 "Allum Mr. Owen George" "male" 18 0 0 "2223" 8.3 "" "S"
|
||||
0 794 1 "Hoyt Mr. William Fisher" "male" NA 0 0 "PC 17600" 30.6958 "" "C"
|
||||
0 891 3 "Dooley Mr. Patrick" "male" 32 0 0 "370376" 7.75 "" "Q"
|
||||
0 851 3 "Andersson Master. Sigvard Harald Elias" "male" 4 4 2 "347082" 31.275 "" "S"
|
||||
1 854 1 "Lines Miss. Mary Conover" "female" 16 0 1 "PC 17592" 39.4 "D28" "S"
|
||||
0 552 2 "Sharp Mr. Percival James R" "male" 27 0 0 "244358" 26 "" "S"
|
||||
1 671 2 "Brown Mrs. Thomas William Solomon (Elizabeth Catherine Ford)" "female" 40 1 1 "29750" 39 "" "S"
|
||||
0 228 3 "Lovell Mr. John Hall (""Henry"")" "male" 20.5 0 0 "A/5 21173" 7.25 "" "S"
|
||||
1 763 3 "Barah Mr. Hanna Assi" "male" 20 0 0 "2663" 7.2292 "" "C"
|
||||
0 591 3 "Rintamaki Mr. Matti" "male" 35 0 0 "STON/O 2. 3101273" 7.125 "" "S"
|
||||
0 155 3 "Olsen Mr. Ole Martin" "male" NA 0 0 "Fa 265302" 7.3125 "" "S"
|
||||
0 104 3 "Johansson Mr. Gustaf Joel" "male" 33 0 0 "7540" 8.6542 "" "S"
|
||||
0 443 3 "Petterson Mr. Johan Emil" "male" 25 1 0 "347076" 7.775 "" "S"
|
||||
1 592 1 "Stephenson Mrs. Walter Bertram (Martha Eustis)" "female" 52 1 0 "36947" 78.2667 "D20" "C"
|
||||
0 584 1 "Ross Mr. John Hugo" "male" 36 0 0 "13049" 40.125 "A10" "C"
|
||||
0 277 3 "Lindblom Miss. Augusta Charlotta" "female" 45 0 0 "347073" 7.75 "" "S"
|
||||
1 514 1 "Rothschild Mrs. Martin (Elizabeth L. Barrett)" "female" 54 1 0 "PC 17603" 59.4 "" "C"
|
||||
1 83 3 "McDermott Miss. Brigdet Delia" "female" NA 0 0 "330932" 7.7875 "" "Q"
|
||||
1 856 3 "Aks Mrs. Sam (Leah Rosen)" "female" 18 0 1 "392091" 9.35 "" "S"
|
||||
1 390 2 "Lehmann Miss. Bertha" "female" 17 0 0 "SC 1748" 12 "" "C"
|
||||
0 579 3 "Caram Mrs. Joseph (Maria Elias)" "female" NA 1 0 "2689" 14.4583 "" "C"
|
||||
0 500 3 "Svensson Mr. Olof" "male" 24 0 0 "350035" 7.7958 "" "S"
|
||||
0 412 3 "Hart Mr. Henry" "male" NA 0 0 "394140" 6.8583 "" "Q"
|
||||
0 687 3 "Panula Mr. Jaako Arnold" "male" 14 4 1 "3101295" 39.6875 "" "S"
|
||||
0 73 2 "Hood Mr. Ambrose Jr" "male" 21 0 0 "S.O.C. 14879" 73.5 "" "S"
|
||||
0 887 2 "Montvila Rev. Juozas" "male" 27 0 0 "211536" 13 "" "S"
|
||||
0 503 3 "O'Sullivan Miss. Bridget Mary" "female" NA 0 0 "330909" 7.6292 "" "Q"
|
||||
0 41 3 "Ahlin Mrs. Johan (Johanna Persdotter Larsson)" "female" 40 1 0 "7546" 9.475 "" "S"
|
||||
1 654 3 "O'Leary Miss. Hanora ""Norah""" "female" NA 0 0 "330919" 7.8292 "" "Q"
|
||||
1 484 3 "Turkula Mrs. (Hedwig)" "female" 63 0 0 "4134" 9.5875 "" "S"
|
||||
0 426 3 "Wiseman Mr. Phillippe" "male" NA 0 0 "A/4. 34244" 7.25 "" "S"
|
||||
1 438 2 "Richards Mrs. Sidney (Emily Hocking)" "female" 24 2 3 "29106" 18.75 "" "S"
|
||||
0 625 3 "Bowen Mr. David John ""Dai""" "male" 21 0 0 "54636" 16.1 "" "S"
|
||||
0 114 3 "Jussila Miss. Katriina" "female" 20 1 0 "4136" 9.825 "" "S"
|
||||
0 614 3 "Horgan Mr. John" "male" NA 0 0 "370377" 7.75 "" "Q"
|
||||
1 731 1 "Allen Miss. Elisabeth Walton" "female" 29 0 0 "24160" 211.3375 "B5" "S"
|
||||
0 675 2 "Watson Mr. Ennis Hastings" "male" NA 0 0 "239856" 0 "" "S"
|
||||
0 712 1 "Klaber Mr. Herman" "male" NA 0 0 "113028" 26.55 "C124" "S"
|
||||
0 657 3 "Radeff Mr. Alexander" "male" NA 0 0 "349223" 7.8958 "" "S"
|
||||
0 848 3 "Markoff Mr. Marin" "male" 35 0 0 "349213" 7.8958 "" "C"
|
||||
0 809 2 "Meyer Mr. August" "male" 39 0 0 "248723" 13 "" "S"
|
||||
1 577 2 "Garside Miss. Ethel" "female" 34 0 0 "243880" 13 "" "S"
|
||||
0 343 2 "Collander Mr. Erik Gustaf" "male" 28 0 0 "248740" 13 "" "S"
|
||||
0 252 3 "Strom Mrs. Wilhelm (Elna Matilda Persson)" "female" 29 1 1 "347054" 10.4625 "G6" "S"
|
||||
0 518 3 "Ryan Mr. Patrick" "male" NA 0 0 "371110" 24.15 "" "Q"
|
||||
0 515 3 "Coleff Mr. Satio" "male" 24 0 0 "349209" 7.4958 "" "S"
|
||||
0 327 3 "Nysveen Mr. Johan Hansen" "male" 61 0 0 "345364" 6.2375 "" "S"
|
||||
0 144 3 "Burke Mr. Jeremiah" "male" 19 0 0 "365222" 6.75 "" "Q"
|
||||
0 629 3 "Bostandyeff Mr. Guentcho" "male" 26 0 0 "349224" 7.8958 "" "S"
|
||||
0 469 3 "Scanlan Mr. James" "male" NA 0 0 "36209" 7.725 "" "Q"
|
||||
1 199 3 "Madigan Miss. Margaret ""Maggie""" "female" NA 0 0 "370370" 7.75 "" "Q"
|
||||
1 459 2 "Toomey Miss. Ellen" "female" 50 0 0 "F.C.C. 13531" 10.5 "" "S"
|
||||
1 485 1 "Bishop Mr. Dickinson H" "male" 25 1 0 "11967" 91.0792 "B49" "C"
|
||||
1 349 3 "Coutts Master. William Loch ""William""" "male" 3 1 1 "C.A. 37671" 15.9 "" "S"
|
||||
0 716 3 "Soholt Mr. Peter Andreas Lauritz Andersen" "male" 19 0 0 "348124" 7.65 "F G73" "S"
|
||||
0 742 1 "Cavendish Mr. Tyrell William" "male" 36 1 0 "19877" 78.85 "C46" "S"
|
||||
0 520 3 "Pavlovic Mr. Stefo" "male" 32 0 0 "349242" 7.8958 "" "S"
|
||||
1 764 1 "Carter Mrs. William Ernest (Lucile Polk)" "female" 36 1 2 "113760" 120 "B96 B98" "S"
|
||||
0 19 3 "Vander Planke Mrs. Julius (Emelia Maria Vandemoortele)" "female" 31 1 0 "345763" 18 "" "S"
|
||||
0 563 2 "Norman Mr. Robert Douglas" "male" 28 0 0 "218629" 13.5 "" "S"
|
||||
1 331 3 "McCoy Miss. Agnes" "female" NA 2 0 "367226" 23.25 "" "Q"
|
||||
0 251 3 "Reed Mr. James George" "male" NA 0 0 "362316" 7.25 "" "S"
|
||||
1 850 1 "Goldenberg Mrs. Samuel L (Edwiga Grabowska)" "female" NA 1 0 "17453" 89.1042 "C92" "C"
|
||||
1 778 3 "Emanuel Miss. Virginia Ethel" "female" 5 0 0 "364516" 12.475 "" "S"
|
||||
1 458 1 "Kenyon Mrs. Frederick R (Marion)" "female" NA 1 0 "17464" 51.8625 "D21" "S"
|
||||
0 457 1 "Millet Mr. Francis Davis" "male" 65 0 0 "13509" 26.55 "E38" "S"
|
||||
1 433 2 "Louch Mrs. Charles Alexander (Alice Adelaide Slow)" "female" 42 1 0 "SC/AH 3085" 26 "" "S"
|
||||
1 743 1 "Ryerson Miss. Susan Parker ""Suzette""" "female" 21 2 2 "PC 17608" 262.375 "B57 B59 B63 B66" "C"
|
||||
1 741 1 "Hawksford Mr. Walter James" "male" NA 0 0 "16988" 30 "D45" "S"
|
||||
1 234 3 "Asplund Miss. Lillian Gertrud" "female" 5 4 2 "347077" 31.3875 "" "S"
|
||||
1 57 2 "Rugg Miss. Emily" "female" 21 0 0 "C.A. 31026" 10.5 "" "S"
|
||||
0 816 1 "Fry Mr. Richard" "male" NA 0 0 "112058" 0 "B102" "S"
|
||||
1 56 1 "Woolner Mr. Hugh" "male" NA 0 0 "19947" 35.5 "C52" "S"
|
||||
0 169 1 "Baumann Mr. John D" "male" NA 0 0 "PC 17318" 25.925 "" "S"
|
||||
0 168 3 "Skoog Mrs. William (Anna Bernhardina Karlsson)" "female" 45 1 4 "347088" 27.9 "" "S"
|
||||
0 158 3 "Corn Mr. Harry" "male" 30 0 0 "SOTON/OQ 392090" 8.05 "" "S"
|
||||
1 270 1 "Bissette Miss. Amelia" "female" 35 0 0 "PC 17760" 135.6333 "C99" "S"
|
||||
0 243 2 "Coleridge Mr. Reginald Charles" "male" 29 0 0 "W./C. 14263" 10.5 "" "S"
|
||||
0 240 2 "Hunt Mr. George Henry" "male" 33 0 0 "SCO/W 1585" 12.275 "" "S"
|
||||
0 177 3 "Lefebre Master. Henry Forbes" "male" NA 3 1 "4133" 25.4667 "" "S"
|
||||
1 608 1 "Daniel Mr. Robert Williams" "male" 27 0 0 "113804" 30.5 "" "S"
|
||||
0 587 2 "Jarvis Mr. John Denzil" "male" 47 0 0 "237565" 15 "" "S"
|
||||
1 508 1 "Bradley Mr. George (""George Arthur Brayton"")" "male" NA 0 0 "111427" 26.55 "" "S"
|
||||
1 648 1 "Simonius-Blumer Col. Oberst Alfons" "male" 56 0 0 "13213" 35.5 "A26" "C"
|
||||
0 255 3 "Rosblom Mrs. Viktor (Helena Wilhelmina)" "female" 41 0 2 "370129" 20.2125 "" "S"
|
||||
0 602 3 "Slabenoff Mr. Petco" "male" NA 0 0 "349214" 7.8958 "" "S"
|
||||
0 136 2 "Richard Mr. Emile" "male" 23 0 0 "SC/PARIS 2133" 15.0458 "" "C"
|
||||
0 464 2 "Milling Mr. Jacob Christian" "male" 48 0 0 "234360" 13 "" "S"
|
||||
1 570 3 "Jonsson Mr. Carl" "male" 32 0 0 "350417" 7.8542 "" "S"
|
||||
1 126 3 "Nicola-Yarred Master. Elias" "male" 12 1 0 "2651" 11.2417 "" "C"
|
||||
1 721 2 "Harper Miss. Annie Jessie ""Nina""" "female" 6 0 1 "248727" 33 "" "S"
|
||||
0 31 1 "Uruchurtu Don. Manuel E" "male" 40 0 0 "PC 17601" 27.7208 "" "C"
|
||||
0 261 3 "Smith Mr. Thomas" "male" NA 0 0 "384461" 7.75 "" "Q"
|
||||
0 621 3 "Yasbeck Mr. Antoni" "male" 27 1 0 "2659" 14.4542 "" "C"
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||||
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0 477 2 "Renouf Mr. Peter Henry" "male" 34 1 0 "31027" 21 "" "S"
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1 347 2 "Smith Miss. Marion Elsie" "female" 40 0 0 "31418" 13 "" "S"
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1 339 3 "Dahl Mr. Karl Edwart" "male" 45 0 0 "7598" 8.05 "" "S"
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1 69 3 "Andersson Miss. Erna Alexandra" "female" 17 4 2 "3101281" 7.925 "" "S"
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1 519 2 "Angle Mrs. William A (Florence ""Mary"" Agnes Hughes)" "female" 36 1 0 "226875" 26 "" "S"
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1 185 3 "Kink-Heilmann Miss. Luise Gretchen" "female" 4 0 2 "315153" 22.025 "" "S"
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1 248 2 "Hamalainen Mrs. William (Anna)" "female" 24 0 2 "250649" 14.5 "" "S"
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1 616 2 "Herman Miss. Alice" "female" 24 1 2 "220845" 65 "" "S"
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0 149 2 "Navratil Mr. Michel (""Louis M Hoffman"")" "male" 36.5 0 2 "230080" 26 "F2" "S"
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1 292 1 "Bishop Mrs. Dickinson H (Helen Walton)" "female" 19 1 0 "11967" 91.0792 "B49" "C"
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1 302 3 "McCoy Mr. Bernard" "male" NA 2 0 "367226" 23.25 "" "Q"
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1 678 3 "Turja Miss. Anna Sofia" "female" 18 0 0 "4138" 9.8417 "" "S"
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0 379 3 "Betros Mr. Tannous" "male" 20 0 0 "2648" 4.0125 "" "C"
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1 357 1 "Bowerman Miss. Elsie Edith" "female" 22 0 1 "113505" 55 "E33" "S"
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1 454 1 "Goldenberg Mr. Samuel L" "male" 49 1 0 "17453" 89.1042 "C92" "C"
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0 52 3 "Nosworthy Mr. Richard Cater" "male" 21 0 0 "A/4. 39886" 7.8 "" "S"
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||||
0 640 3 "Thorneycroft Mr. Percival" "male" NA 1 0 "376564" 16.1 "" "S"
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||||
0 325 3 "Sage Mr. George John Jr" "male" NA 8 2 "CA. 2343" 69.55 "" "S"
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||||
1 541 1 "Crosby Miss. Harriet R" "female" 36 0 2 "WE/P 5735" 71 "B22" "S"
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||||
0 442 3 "Hampe Mr. Leon" "male" 20 0 0 "345769" 9.5 "" "S"
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||||
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||||
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||||
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1 341 2 "Navratil Master. Edmond Roger" "male" 2 1 1 "230080" 26 "F2" "S"
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||||
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||||
0 133 3 "Robins Mrs. Alexander A (Grace Charity Laury)" "female" 47 1 0 "A/5. 3337" 14.5 "" "S"
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1 652 2 "Doling Miss. Elsie" "female" 18 0 1 "231919" 23 "" "S"
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||||
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||||
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||||
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||||
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||||
1 578 1 "Silvey Mrs. William Baird (Alice Munger)" "female" 39 1 0 "13507" 55.9 "E44" "S"
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||||
0 279 3 "Rice Master. Eric" "male" 7 4 1 "382652" 29.125 "" "Q"
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||||
0 493 1 "Molson Mr. Harry Markland" "male" 55 0 0 "113787" 30.5 "C30" "S"
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||||
0 286 3 "Stankovic Mr. Ivan" "male" 33 0 0 "349239" 8.6625 "" "C"
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0 123 2 "Nasser Mr. Nicholas" "male" 32.5 1 0 "237736" 30.0708 "" "C"
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||||
0 175 1 "Smith Mr. James Clinch" "male" 56 0 0 "17764" 30.6958 "A7" "C"
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||||
1 391 1 "Carter Mr. William Ernest" "male" 36 1 2 "113760" 120 "B96 B98" "S"
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||||
1 511 3 "Daly Mr. Eugene Patrick" "male" 29 0 0 "382651" 7.75 "" "Q"
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||||
1 802 2 "Collyer Mrs. Harvey (Charlotte Annie Tate)" "female" 31 1 1 "C.A. 31921" 26.25 "" "S"
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||||
1 346 2 "Brown Miss. Amelia ""Mildred""" "female" 24 0 0 "248733" 13 "F33" "S"
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1 142 3 "Nysten Miss. Anna Sofia" "female" 22 0 0 "347081" 7.75 "" "S"
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0 568 3 "Palsson Mrs. Nils (Alma Cornelia Berglund)" "female" 29 0 4 "349909" 21.075 "" "S"
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||||
0 817 3 "Heininen Miss. Wendla Maria" "female" 23 0 0 "STON/O2. 3101290" 7.925 "" "S"
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||||
0 847 3 "Sage Mr. Douglas Bullen" "male" NA 8 2 "CA. 2343" 69.55 "" "S"
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||||
1 417 2 "Drew Mrs. James Vivian (Lulu Thorne Christian)" "female" 34 1 1 "28220" 32.5 "" "S"
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||||
1 766 1 "Hogeboom Mrs. John C (Anna Andrews)" "female" 51 1 0 "13502" 77.9583 "D11" "S"
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||||
1 408 2 "Richards Master. William Rowe" "male" 3 1 1 "29106" 18.75 "" "S"
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0 103 1 "White Mr. Richard Frasar" "male" 21 0 1 "35281" 77.2875 "D26" "S"
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0 266 2 "Reeves Mr. David" "male" 36 0 0 "C.A. 17248" 10.5 "" "S"
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||||
0 737 3 "Ford Mrs. Edward (Margaret Ann Watson)" "female" 48 1 3 "W./C. 6608" 34.375 "" "S"
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||||
1 26 3 "Asplund Mrs. Carl Oscar (Selma Augusta Emilia Johansson)" "female" 38 1 5 "347077" 31.3875 "" "S"
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|
||||
0 569 3 "Doharr Mr. Tannous" "male" NA 0 0 "2686" 7.2292 "" "C"
|
||||
0 664 3 "Coleff Mr. Peju" "male" 36 0 0 "349210" 7.4958 "" "S"
|
||||
1 330 1 "Hippach Miss. Jean Gertrude" "female" 16 0 1 "111361" 57.9792 "B18" "C"
|
||||
1 642 1 "Sagesser Mlle. Emma" "female" 24 0 0 "PC 17477" 69.3 "B35" "C"
|
||||
0 398 2 "McKane Mr. Peter David" "male" 46 0 0 "28403" 26 "" "S"
|
||||
0 700 3 "Humblen Mr. Adolf Mathias Nicolai Olsen" "male" 42 0 0 "348121" 7.65 "F G63" "S"
|
||||
0 78 3 "Moutal Mr. Rahamin Haim" "male" NA 0 0 "374746" 8.05 "" "S"
|
||||
0 699 1 "Thayer Mr. John Borland" "male" 49 1 1 "17421" 110.8833 "C68" "C"
|
||||
1 428 2 "Phillips Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")" "female" 19 0 0 "250655" 26 "" "S"
|
||||
0 70 3 "Kink Mr. Vincenz" "male" 26 2 0 "315151" 8.6625 "" "S"
|
||||
0 846 3 "Abbing Mr. Anthony" "male" 42 0 0 "C.A. 5547" 7.55 "" "S"
|
||||
0 768 3 "Mangan Miss. Mary" "female" 30.5 0 0 "364850" 7.75 "" "Q"
|
||||
1 646 1 "Harper Mr. Henry Sleeper" "male" 48 1 0 "PC 17572" 76.7292 "D33" "C"
|
||||
1 497 1 "Eustis Miss. Elizabeth Mussey" "female" 54 1 0 "36947" 78.2667 "D20" "C"
|
||||
0 617 3 "Danbom Mr. Ernst Gilbert" "male" 34 1 1 "347080" 14.4 "" "S"
|
||||
0 181 3 "Sage Miss. Constance Gladys" "female" NA 8 2 "CA. 2343" 69.55 "" "S"
|
||||
0 65 1 "Stewart Mr. Albert A" "male" NA 0 0 "PC 17605" 27.7208 "" "C"
|
||||
0 779 3 "Kilgannon Mr. Thomas J" "male" NA 0 0 "36865" 7.7375 "" "Q"
|
||||
1 342 1 "Fortune Miss. Alice Elizabeth" "female" 24 3 2 "19950" 263 "C23 C25 C27" "S"
|
||||
0 553 3 "O'Brien Mr. Timothy" "male" NA 0 0 "330979" 7.8292 "" "Q"
|
||||
0 207 3 "Backstrom Mr. Karl Alfred" "male" 32 1 0 "3101278" 15.85 "" "S"
|
||||
1 319 1 "Wick Miss. Mary Natalie" "female" 31 0 2 "36928" 164.8667 "C7" "S"
|
||||
0 358 2 "Funk Miss. Annie Clemmer" "female" 38 0 0 "237671" 13 "" "S"
|
||||
0 178 1 "Isham Miss. Ann Elizabeth" "female" 50 0 0 "PC 17595" 28.7125 "C49" "C"
|
||||
0 213 3 "Perkin Mr. John Henry" "male" 22 0 0 "A/5 21174" 7.25 "" "S"
|
||||
0 462 3 "Morley Mr. William" "male" 34 0 0 "364506" 8.05 "" "S"
|
||||
0 170 3 "Ling Mr. Lee" "male" 28 0 0 "1601" 56.4958 "" "S"
|
||||
0 865 2 "Gill Mr. John William" "male" 24 0 0 "233866" 13 "" "S"
|
||||
1 217 3 "Honkanen Miss. Eliina" "female" 27 0 0 "STON/O2. 3101283" 7.925 "" "S"
|
||||
1 66 3 "Moubarek Master. Gerios" "male" NA 1 1 "2661" 15.2458 "" "C"
|
||||
0 389 3 "Sadlier Mr. Matthew" "male" NA 0 0 "367655" 7.7292 "" "Q"
|
||||
0 293 2 "Levy Mr. Rene Jacques" "male" 36 0 0 "SC/Paris 2163" 12.875 "D" "C"
|
||||
0 214 2 "Givard Mr. Hans Kristensen" "male" 30 0 0 "250646" 13 "" "S"
|
||||
1 326 1 "Young Miss. Marie Grice" "female" 36 0 0 "PC 17760" 135.6333 "C32" "C"
|
||||
0 283 3 "de Pelsmaeker Mr. Alfons" "male" 16 0 0 "345778" 9.5 "" "S"
|
||||
0 481 3 "Goodwin Master. Harold Victor" "male" 9 5 2 "CA 2144" 46.9 "" "S"
|
||||
0 192 2 "Carbines Mr. William" "male" 19 0 0 "28424" 13 "" "S"
|
||||
0 345 2 "Fox Mr. Stanley Hubert" "male" 36 0 0 "229236" 13 "" "S"
|
||||
1 609 2 "Laroche Mrs. Joseph (Juliette Marie Louise Lafargue)" "female" 22 1 2 "SC/Paris 2123" 41.5792 "" "C"
|
||||
0 660 1 "Newell Mr. Arthur Webster" "male" 58 0 2 "35273" 113.275 "D48" "C"
|
||||
1 85 2 "Ilett Miss. Bertha" "female" 17 0 0 "SO/C 14885" 10.5 "" "S"
|
||||
1 521 1 "Perreault Miss. Anne" "female" 30 0 0 "12749" 93.5 "B73" "S"
|
||||
1 644 3 "Foo Mr. Choong" "male" NA 0 0 "1601" 56.4958 "" "S"
|
||||
1 831 3 "Yasbeck Mrs. Antoni (Selini Alexander)" "female" 15 1 0 "2659" 14.4542 "" "C"
|
|
|
@ -0,0 +1,316 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 139,
|
||||
"id": "02249c82",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.metrics import precision_recall_fscore_support"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 140,
|
||||
"id": "4be6437d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = pd.read_csv('titanic.tsv',sep='\\t')\n",
|
||||
"\n",
|
||||
"# formatowanie danych\n",
|
||||
"data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
|
||||
"data['Name_to_num'] = data['Name'].apply(\n",
|
||||
" lambda x: 1 if 'Mr.' in x else 0\n",
|
||||
")\n",
|
||||
"del data['Name']\n",
|
||||
"\n",
|
||||
"data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Cabin'])\n",
|
||||
"vector = vectorizer.transform(data['Cabin']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Cabin']=vector_sum\n",
|
||||
"\n",
|
||||
"data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"data = pd.get_dummies(data,columns=['Embarked'])\n",
|
||||
"\n",
|
||||
"imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
|
||||
"data[['Age']] = imputer.fit_transform(data[['Age']])\n",
|
||||
"\n",
|
||||
"data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Ticket'])\n",
|
||||
"vector = vectorizer.transform(data['Ticket']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Ticket']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 141,
|
||||
"id": "618e8841",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Survived</th>\n",
|
||||
" <th>PassengerId</th>\n",
|
||||
" <th>Pclass</th>\n",
|
||||
" <th>Sex</th>\n",
|
||||
" <th>Age</th>\n",
|
||||
" <th>SibSp</th>\n",
|
||||
" <th>Parch</th>\n",
|
||||
" <th>Ticket</th>\n",
|
||||
" <th>Fare</th>\n",
|
||||
" <th>Cabin</th>\n",
|
||||
" <th>Name_to_num</th>\n",
|
||||
" <th>Embarked_C</th>\n",
|
||||
" <th>Embarked_Q</th>\n",
|
||||
" <th>Embarked_S</th>\n",
|
||||
" <th>Embarked_Undefined</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>530</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>23.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>11.5000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>466</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.391284</td>\n",
|
||||
" <td>7.0500</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>753</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>33.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>9.5000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>44.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>26.0000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>333</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.365721</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket Fare \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 1.000000 11.5000 \n",
|
||||
"1 0 466 3 1 38.0 0 0 1.391284 7.0500 \n",
|
||||
"2 0 753 3 1 33.0 0 0 1.000000 9.5000 \n",
|
||||
"3 0 855 2 0 44.0 1 0 1.000000 26.0000 \n",
|
||||
"4 0 333 1 1 38.0 0 1 1.365721 153.4625 \n",
|
||||
"\n",
|
||||
" Cabin Name_to_num Embarked_C Embarked_Q Embarked_S Embarked_Undefined \n",
|
||||
"0 1.0 1 0 0 1 0 \n",
|
||||
"1 1.0 1 0 0 1 0 \n",
|
||||
"2 1.0 1 0 0 1 0 \n",
|
||||
"3 1.0 0 0 0 1 0 \n",
|
||||
"4 1.0 1 0 0 1 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 141,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 142,
|
||||
"id": "7134ea55",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Podział danych na zbiór uczący i zbiór testowy\n",
|
||||
"data_train,data_test = train_test_split(data, test_size=0.2)\n",
|
||||
"\n",
|
||||
"# zdefiniowanie cech\n",
|
||||
"FEATURES = ['Sex','Age','Embarked_C','Embarked_Q','Embarked_S']\n",
|
||||
"x_train = pd.DataFrame(data_train[FEATURES])\n",
|
||||
"y_train = pd.DataFrame(data_train['Survived'])\n",
|
||||
"model = LogisticRegression()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 143,
|
||||
"id": "1ff85122",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# uczenie modelu\n",
|
||||
"model.fit(x_train,y_train.values.ravel())\n",
|
||||
"\n",
|
||||
"# predykcja wynikow\n",
|
||||
"x_test = pd.DataFrame(data_test[FEATURES])\n",
|
||||
"y_expected = pd.DataFrame(data_test['Survived'])\n",
|
||||
"y_predicted = model.predict(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 144,
|
||||
"id": "0dff77b1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Precision: 0.832\n",
|
||||
"Recall: 0.832\n",
|
||||
"F-score: 0.832\n",
|
||||
"Model score: 0.832\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ewaluacja wynikow\n",
|
||||
"precision, recall, fscore, support = precision_recall_fscore_support(\n",
|
||||
" y_expected, y_predicted, average='micro'\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(f\"Precision: {precision}\")\n",
|
||||
"print(f\"Recall: {recall}\")\n",
|
||||
"print(f\"F-score: {fscore}\")\n",
|
||||
"\n",
|
||||
"score = model.score(x_test, y_expected)\n",
|
||||
"\n",
|
||||
"print(f\"Model score: {score}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,316 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 139,
|
||||
"id": "02249c82",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.metrics import precision_recall_fscore_support"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 140,
|
||||
"id": "4be6437d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = pd.read_csv('titanic.tsv',sep='\\t')\n",
|
||||
"\n",
|
||||
"# formatowanie danych\n",
|
||||
"data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
|
||||
"data['Name_to_num'] = data['Name'].apply(\n",
|
||||
" lambda x: 1 if 'Mr.' in x else 0\n",
|
||||
")\n",
|
||||
"del data['Name']\n",
|
||||
"\n",
|
||||
"data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Cabin'])\n",
|
||||
"vector = vectorizer.transform(data['Cabin']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Cabin']=vector_sum\n",
|
||||
"\n",
|
||||
"data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"data = pd.get_dummies(data,columns=['Embarked'])\n",
|
||||
"\n",
|
||||
"imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
|
||||
"data[['Age']] = imputer.fit_transform(data[['Age']])\n",
|
||||
"\n",
|
||||
"data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
|
||||
"\n",
|
||||
"vectorizer = TfidfVectorizer()\n",
|
||||
"vectorizer.fit(data['Ticket'])\n",
|
||||
"vector = vectorizer.transform(data['Ticket']).toarray()\n",
|
||||
"vector_sum = []\n",
|
||||
"for v in vector:\n",
|
||||
" vector_sum.append(v.sum())\n",
|
||||
"data['Ticket']=vector_sum"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 141,
|
||||
"id": "618e8841",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Survived</th>\n",
|
||||
" <th>PassengerId</th>\n",
|
||||
" <th>Pclass</th>\n",
|
||||
" <th>Sex</th>\n",
|
||||
" <th>Age</th>\n",
|
||||
" <th>SibSp</th>\n",
|
||||
" <th>Parch</th>\n",
|
||||
" <th>Ticket</th>\n",
|
||||
" <th>Fare</th>\n",
|
||||
" <th>Cabin</th>\n",
|
||||
" <th>Name_to_num</th>\n",
|
||||
" <th>Embarked_C</th>\n",
|
||||
" <th>Embarked_Q</th>\n",
|
||||
" <th>Embarked_S</th>\n",
|
||||
" <th>Embarked_Undefined</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>530</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>23.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>11.5000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>466</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.391284</td>\n",
|
||||
" <td>7.0500</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>753</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>33.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>9.5000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>855</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>44.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>26.0000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>333</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>38.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1.365721</td>\n",
|
||||
" <td>153.4625</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Survived PassengerId Pclass Sex Age SibSp Parch Ticket Fare \\\n",
|
||||
"0 0 530 2 1 23.0 2 1 1.000000 11.5000 \n",
|
||||
"1 0 466 3 1 38.0 0 0 1.391284 7.0500 \n",
|
||||
"2 0 753 3 1 33.0 0 0 1.000000 9.5000 \n",
|
||||
"3 0 855 2 0 44.0 1 0 1.000000 26.0000 \n",
|
||||
"4 0 333 1 1 38.0 0 1 1.365721 153.4625 \n",
|
||||
"\n",
|
||||
" Cabin Name_to_num Embarked_C Embarked_Q Embarked_S Embarked_Undefined \n",
|
||||
"0 1.0 1 0 0 1 0 \n",
|
||||
"1 1.0 1 0 0 1 0 \n",
|
||||
"2 1.0 1 0 0 1 0 \n",
|
||||
"3 1.0 0 0 0 1 0 \n",
|
||||
"4 1.0 1 0 0 1 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 141,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 142,
|
||||
"id": "7134ea55",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Podział danych na zbiór uczący i zbiór testowy\n",
|
||||
"data_train,data_test = train_test_split(data, test_size=0.2)\n",
|
||||
"\n",
|
||||
"# zdefiniowanie cech\n",
|
||||
"FEATURES = ['Sex','Age','Embarked_C','Embarked_Q','Embarked_S']\n",
|
||||
"x_train = pd.DataFrame(data_train[FEATURES])\n",
|
||||
"y_train = pd.DataFrame(data_train['Survived'])\n",
|
||||
"model = LogisticRegression()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 143,
|
||||
"id": "1ff85122",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# uczenie modelu\n",
|
||||
"model.fit(x_train,y_train.values.ravel())\n",
|
||||
"\n",
|
||||
"# predykcja wynikow\n",
|
||||
"x_test = pd.DataFrame(data_test[FEATURES])\n",
|
||||
"y_expected = pd.DataFrame(data_test['Survived'])\n",
|
||||
"y_predicted = model.predict(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 144,
|
||||
"id": "0dff77b1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Precision: 0.832\n",
|
||||
"Recall: 0.832\n",
|
||||
"F-score: 0.832\n",
|
||||
"Model score: 0.832\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ewaluacja wynikow\n",
|
||||
"precision, recall, fscore, support = precision_recall_fscore_support(\n",
|
||||
" y_expected, y_predicted, average='micro'\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(f\"Precision: {precision}\")\n",
|
||||
"print(f\"Recall: {recall}\")\n",
|
||||
"print(f\"F-score: {fscore}\")\n",
|
||||
"\n",
|
||||
"score = model.score(x_test, y_expected)\n",
|
||||
"\n",
|
||||
"print(f\"Model score: {score}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
|
@ -0,0 +1,624 @@
|
|||
"Survived" "PassengerId" "Pclass" "Name" "Sex" "Age" "SibSp" "Parch" "Ticket" "Fare" "Cabin" "Embarked"
|
||||
0 530 2 "Hocking Mr. Richard George" "male" 23 2 1 "29104" 11.5 "" "S"
|
||||
0 466 3 "Goncalves Mr. Manuel Estanslas" "male" 38 0 0 "SOTON/O.Q. 3101306" 7.05 "" "S"
|
||||
0 753 3 "Vande Velde Mr. Johannes Joseph" "male" 33 0 0 "345780" 9.5 "" "S"
|
||||
0 855 2 "Carter Mrs. Ernest Courtenay (Lilian Hughes)" "female" 44 1 0 "244252" 26 "" "S"
|
||||
0 333 1 "Graham Mr. George Edward" "male" 38 0 1 "PC 17582" 153.4625 "C91" "S"
|
||||
0 39 3 "Vander Planke Miss. Augusta Maria" "female" 18 2 0 "345764" 18 "" "S"
|
||||
0 236 3 "Harknett Miss. Alice Phoebe" "female" NA 0 0 "W./C. 6609" 7.55 "" "S"
|
||||
0 303 3 "Johnson Mr. William Cahoone Jr" "male" 19 0 0 "LINE" 0 "" "S"
|
||||
1 18 2 "Williams Mr. Charles Eugene" "male" NA 0 0 "244373" 13 "" "S"
|
||||
1 505 1 "Maioni Miss. Roberta" "female" 16 0 0 "110152" 86.5 "B79" "S"
|
||||
1 670 1 "Taylor Mrs. Elmer Zebley (Juliet Cummins Wright)" "female" NA 1 0 "19996" 52 "C126" "S"
|
||||
1 316 3 "Nilsson Miss. Helmina Josefina" "female" 26 0 0 "347470" 7.8542 "" "S"
|
||||
1 690 1 "Madill Miss. Georgette Alexandra" "female" 15 0 1 "24160" 211.3375 "B5" "S"
|
||||
1 718 2 "Troutt Miss. Edwina Celia ""Winnie""" "female" 27 0 0 "34218" 10.5 "E101" "S"
|
||||
1 580 3 "Jussila Mr. Eiriik" "male" 32 0 0 "STON/O 2. 3101286" 7.925 "" "S"
|
||||
0 697 3 "Kelly Mr. James" "male" 44 0 0 "363592" 8.05 "" "S"
|
||||
1 193 3 "Andersen-Jensen Miss. Carla Christine Nielsine" "female" 19 1 0 "350046" 7.8542 "" "S"
|
||||
0 634 1 "Parr Mr. William Henry Marsh" "male" NA 0 0 "112052" 0 "" "S"
|
||||
1 858 1 "Daly Mr. Peter Denis " "male" 51 0 0 "113055" 26.55 "E17" "S"
|
||||
1 329 3 "Goldsmith Mrs. Frank John (Emily Alice Brown)" "female" 31 1 1 "363291" 20.525 "" "S"
|
||||
0 784 3 "Johnston Mr. Andrew G" "male" NA 1 2 "W./C. 6607" 23.45 "" "S"
|
||||
0 407 3 "Widegren Mr. Carl/Charles Peter" "male" 51 0 0 "347064" 7.75 "" "S"
|
||||
0 747 3 "Abbott Mr. Rossmore Edward" "male" 16 1 1 "C.A. 2673" 20.25 "" "S"
|
||||
0 492 3 "Windelov Mr. Einar" "male" 21 0 0 "SOTON/OQ 3101317" 7.25 "" "S"
|
||||
1 534 3 "Peter Mrs. Catherine (Catherine Rizk)" "female" NA 0 2 "2668" 22.3583 "" "C"
|
||||
0 862 2 "Giles Mr. Frederick Edward" "male" 21 1 0 "28134" 11.5 "" "S"
|
||||
0 724 2 "Hodges Mr. Henry Price" "male" 50 0 0 "250643" 13 "" "S"
|
||||
0 685 2 "Brown Mr. Thomas William Solomon" "male" 60 1 1 "29750" 39 "" "S"
|
||||
0 344 2 "Sedgwick Mr. Charles Frederick Waddington" "male" 25 0 0 "244361" 13 "" "S"
|
||||
1 751 2 "Wells Miss. Joan" "female" 4 1 1 "29103" 23 "" "S"
|
||||
0 734 2 "Berriman Mr. William John" "male" 23 0 0 "28425" 13 "" "S"
|
||||
0 496 3 "Yousseff Mr. Gerious" "male" NA 0 0 "2627" 14.4583 "" "C"
|
||||
0 296 1 "Lewy Mr. Ervin G" "male" NA 0 0 "PC 17612" 27.7208 "" "C"
|
||||
0 516 1 "Walker Mr. William Anderson" "male" 47 0 0 "36967" 34.0208 "D46" "S"
|
||||
0 282 3 "Olsson Mr. Nils Johan Goransson" "male" 28 0 0 "347464" 7.8542 "" "S"
|
||||
0 218 2 "Jacobsohn Mr. Sidney Samuel" "male" 42 1 0 "243847" 27 "" "S"
|
||||
1 107 3 "Salkjelsvik Miss. Anna Kristine" "female" 21 0 0 "343120" 7.65 "" "S"
|
||||
0 878 3 "Petroff Mr. Nedelio" "male" 19 0 0 "349212" 7.8958 "" "S"
|
||||
1 291 1 "Barber Miss. Ellen ""Nellie""" "female" 26 0 0 "19877" 78.85 "" "S"
|
||||
0 722 3 "Jensen Mr. Svend Lauritz" "male" 17 1 0 "350048" 7.0542 "" "S"
|
||||
0 826 3 "Flynn Mr. John" "male" NA 0 0 "368323" 6.95 "" "Q"
|
||||
0 434 3 "Kallio Mr. Nikolai Erland" "male" 17 0 0 "STON/O 2. 3101274" 7.125 "" "S"
|
||||
0 404 3 "Hakkarainen Mr. Pekka Pietari" "male" 28 1 0 "STON/O2. 3101279" 15.85 "" "S"
|
||||
1 210 1 "Blank Mr. Henry" "male" 40 0 0 "112277" 31 "A31" "C"
|
||||
1 166 3 "Goldsmith Master. Frank John William ""Frankie""" "male" 9 0 2 "363291" 20.525 "" "S"
|
||||
0 201 3 "Vande Walle Mr. Nestor Cyriel" "male" 28 0 0 "345770" 9.5 "" "S"
|
||||
0 361 3 "Skoog Mr. Wilhelm" "male" 40 1 4 "347088" 27.9 "" "S"
|
||||
1 53 1 "Harper Mrs. Henry Sleeper (Myna Haxtun)" "female" 49 1 0 "PC 17572" 76.7292 "D33" "C"
|
||||
0 612 3 "Jardin Mr. Jose Neto" "male" NA 0 0 "SOTON/O.Q. 3101305" 7.05 "" "S"
|
||||
0 60 3 "Goodwin Master. William Frederick" "male" 11 5 2 "CA 2144" 46.9 "" "S"
|
||||
1 262 3 "Asplund Master. Edvin Rojj Felix" "male" 3 4 2 "347077" 31.3875 "" "S"
|
||||
1 674 2 "Wilhelms Mr. Charles" "male" 31 0 0 "244270" 13 "" "S"
|
||||
0 63 1 "Harris Mr. Henry Birkhardt" "male" 45 1 0 "36973" 83.475 "C83" "S"
|
||||
0 215 3 "Kiernan Mr. Philip" "male" NA 1 0 "367229" 7.75 "" "Q"
|
||||
0 618 3 "Lobb Mrs. William Arthur (Cordelia K Stanlick)" "female" 26 1 0 "A/5. 3336" 16.1 "" "S"
|
||||
1 789 3 "Dean Master. Bertram Vere" "male" 1 1 2 "C.A. 2315" 20.575 "" "S"
|
||||
1 312 1 "Ryerson Miss. Emily Borie" "female" 18 2 2 "PC 17608" 262.375 "B57 B59 B63 B66" "C"
|
||||
0 113 3 "Barton Mr. David John" "male" 22 0 0 "324669" 8.05 "" "S"
|
||||
0 314 3 "Hendekovic Mr. Ignjac" "male" 28 0 0 "349243" 7.8958 "" "S"
|
||||
1 517 2 "Lemore Mrs. (Amelia Milley)" "female" 34 0 0 "C.A. 34260" 10.5 "F33" "S"
|
||||
0 174 3 "Sivola Mr. Antti Wilhelm" "male" 21 0 0 "STON/O 2. 3101280" 7.925 "" "S"
|
||||
1 803 1 "Carter Master. William Thornton II" "male" 11 1 2 "113760" 120 "B96 B98" "S"
|
||||
0 479 3 "Karlsson Mr. Nils August" "male" 22 0 0 "350060" 7.5208 "" "S"
|
||||
1 445 3 "Johannesen-Bratthammer Mr. Bernt" "male" NA 0 0 "65306" 8.1125 "" "S"
|
||||
0 684 3 "Goodwin Mr. Charles Edward" "male" 14 5 2 "CA 2144" 46.9 "" "S"
|
||||
1 597 2 "Leitch Miss. Jessie Wills" "female" NA 0 0 "248727" 33 "" "S"
|
||||
0 229 2 "Fahlstrom Mr. Arne Jonas" "male" 18 0 0 "236171" 13 "" "S"
|
||||
0 121 2 "Hickman Mr. Stanley George" "male" 21 2 0 "S.O.C. 14879" 73.5 "" "S"
|
||||
0 526 3 "Farrell Mr. James" "male" 40.5 0 0 "367232" 7.75 "" "Q"
|
||||
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1 287 3 "de Mulder Mr. Theodore" "male" 30 0 0 "345774" 9.5 "" "S"
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0 118 2 "Turpin Mr. William John Robert" "male" 29 1 0 "11668" 21 "" "S"
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0 120 3 "Andersson Miss. Ellis Anna Maria" "female" 2 4 2 "347082" 31.275 "" "S"
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0 842 2 "Mudd Mr. Thomas Charles" "male" 16 0 0 "S.O./P.P. 3" 10.5 "" "S"
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0 313 2 "Lahtinen Mrs. William (Anna Sylfven)" "female" 26 1 1 "250651" 26 "" "S"
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File diff suppressed because one or more lines are too long
|
@ -0,0 +1,20 @@
|
|||
21.252 -555.640
|
||||
179.842 3840.141
|
||||
118.162 2274.989
|
||||
114.269 1146.575
|
||||
121.444 1840.589
|
||||
87.624 1663.894
|
||||
170.039 3504.537
|
||||
192.651 3708.239
|
||||
12.390 -358.240
|
||||
144.264 2444.162
|
||||
169.900 3348.941
|
||||
63.254 271.623
|
||||
72.439 900.423
|
||||
71.108 77.543
|
||||
179.476 3313.424
|
||||
169.084 2525.653
|
||||
99.073 734.413
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195.528 4067.410
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131.023 2182.147
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12.424 490.714
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File diff suppressed because one or more lines are too long
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@ -0,0 +1,703 @@
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||||
" <td>0.84</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.91</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>0.26</td>\n",
|
||||
" <td>0.49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>193</td>\n",
|
||||
" <td>93926</td>\n",
|
||||
" <td>94</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.43</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.89</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.16</td>\n",
|
||||
" <td>0.12</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.81</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.56</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.63</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>33</th>\n",
|
||||
" <td>51</td>\n",
|
||||
" <td>680</td>\n",
|
||||
" <td>47672</td>\n",
|
||||
" <td>52</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.43</td>\n",
|
||||
" <td>0.51</td>\n",
|
||||
" <td>0.58</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.14</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.75</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.60</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>0.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>68</th>\n",
|
||||
" <td>34</td>\n",
|
||||
" <td>23</td>\n",
|
||||
" <td>58200</td>\n",
|
||||
" <td>79</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>0.59</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.73</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.64</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.00</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" <td>0.50</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>74</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>46520</td>\n",
|
||||
" <td>58</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.85</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.21</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.7</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.44</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.14</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1880</th>\n",
|
||||
" <td>34</td>\n",
|
||||
" <td>39</td>\n",
|
||||
" <td>40350</td>\n",
|
||||
" <td>50</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.65</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.85</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.99</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1963</th>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>27</td>\n",
|
||||
" <td>59641</td>\n",
|
||||
" <td>85</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.61</td>\n",
|
||||
" <td>0.47</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.47</td>\n",
|
||||
" <td>0.42</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.49</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.37</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.45</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1981</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>35650</td>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.38</td>\n",
|
||||
" <td>0.17</td>\n",
|
||||
" <td>0.84</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.13</td>\n",
|
||||
" <td>0.17</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.72</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.62</td>\n",
|
||||
" <td>0.15</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1991</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>80070</td>\n",
|
||||
" <td>110</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.16</td>\n",
|
||||
" <td>0.37</td>\n",
|
||||
" <td>0.25</td>\n",
|
||||
" <td>0.69</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.18</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.78</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.91</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1992</th>\n",
|
||||
" <td>25</td>\n",
|
||||
" <td>17</td>\n",
|
||||
" <td>72600</td>\n",
|
||||
" <td>107</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.51</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.87</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.38</td>\n",
|
||||
" <td>0.33</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.79</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" <td>0.18</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>123 rows × 128 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" state county community communityname fold population householdsize \\\n",
|
||||
"16 36 1 1000 0 1 0.15 0.31 \n",
|
||||
"23 19 193 93926 94 1 0.11 0.43 \n",
|
||||
"33 51 680 47672 52 1 0.09 0.43 \n",
|
||||
"68 34 23 58200 79 1 0.05 0.59 \n",
|
||||
"74 9 9 46520 58 1 0.08 0.39 \n",
|
||||
"... ... ... ... ... ... ... ... \n",
|
||||
"1880 34 39 40350 50 10 0.04 0.39 \n",
|
||||
"1963 36 27 59641 85 10 0.03 0.32 \n",
|
||||
"1981 9 9 35650 36 10 0.07 0.38 \n",
|
||||
"1991 9 9 80070 110 10 0.16 0.37 \n",
|
||||
"1992 25 17 72600 107 10 0.08 0.51 \n",
|
||||
"\n",
|
||||
" racepctblack racePctWhite racePctAsian ... LandArea PopDens \\\n",
|
||||
"16 0.40 0.63 0.14 ... 0.06 0.39 \n",
|
||||
"23 0.04 0.89 0.09 ... 0.16 0.12 \n",
|
||||
"33 0.51 0.58 0.04 ... 0.14 0.11 \n",
|
||||
"68 0.23 0.39 0.09 ... 0.01 0.73 \n",
|
||||
"74 0.08 0.85 0.04 ... 0.07 0.21 \n",
|
||||
"... ... ... ... ... ... ... \n",
|
||||
"1880 0.39 0.65 0.09 ... 0.03 0.28 \n",
|
||||
"1963 0.61 0.47 0.09 ... 0.01 0.47 \n",
|
||||
"1981 0.17 0.84 0.11 ... 0.09 0.13 \n",
|
||||
"1991 0.25 0.69 0.04 ... 0.08 0.32 \n",
|
||||
"1992 0.06 0.87 0.22 ... 0.03 0.38 \n",
|
||||
"\n",
|
||||
" PctUsePubTrans PolicCars PolicOperBudg LemasPctPolicOnPatr \\\n",
|
||||
"16 0.84 0.06 0.06 0.91 \n",
|
||||
"23 0.07 0.04 0.01 0.81 \n",
|
||||
"33 0.19 0.05 0.01 0.75 \n",
|
||||
"68 0.28 0 0.02 0.64 \n",
|
||||
"74 0.04 0.02 0.01 0.7 \n",
|
||||
"... ... ... ... ... \n",
|
||||
"1880 0.32 0.02 0.01 0.85 \n",
|
||||
"1963 0.42 0.07 0.08 0.49 \n",
|
||||
"1981 0.17 0.02 0.01 0.72 \n",
|
||||
"1991 0.18 0.08 0.06 0.78 \n",
|
||||
"1992 0.33 0.02 0.02 0.79 \n",
|
||||
"\n",
|
||||
" LemasGangUnitDeploy LemasPctOfficDrugUn PolicBudgPerPop \\\n",
|
||||
"16 0.5 0.88 0.26 \n",
|
||||
"23 1 0.56 0.09 \n",
|
||||
"33 0 0.60 0.1 \n",
|
||||
"68 0 1.00 0.23 \n",
|
||||
"74 1 0.44 0.11 \n",
|
||||
"... ... ... ... \n",
|
||||
"1880 0 0.99 0.19 \n",
|
||||
"1963 0 0.37 1 \n",
|
||||
"1981 0 0.62 0.15 \n",
|
||||
"1991 0 0.91 0.28 \n",
|
||||
"1992 0 0.22 0.18 \n",
|
||||
"\n",
|
||||
" ViolentCrimesPerPop \n",
|
||||
"16 0.49 \n",
|
||||
"23 0.63 \n",
|
||||
"33 0.31 \n",
|
||||
"68 0.50 \n",
|
||||
"74 0.14 \n",
|
||||
"... ... \n",
|
||||
"1880 0.22 \n",
|
||||
"1963 0.45 \n",
|
||||
"1981 0.07 \n",
|
||||
"1991 0.23 \n",
|
||||
"1992 0.19 \n",
|
||||
"\n",
|
||||
"[123 rows x 128 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import sklearn\n",
|
||||
"from sklearn.preprocessing import PolynomialFeatures, LabelEncoder\n",
|
||||
"from sklearn.linear_model import LinearRegression, Ridge, RidgeCV\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"\n",
|
||||
"col_names = [\n",
|
||||
"\"state\",\n",
|
||||
"\"county\",\n",
|
||||
"\"community\",\n",
|
||||
"\"communityname\",\n",
|
||||
"\"fold\",\n",
|
||||
"\"population\",\n",
|
||||
"\"householdsize\",\n",
|
||||
"\"racepctblack\",\n",
|
||||
"\"racePctWhite\",\n",
|
||||
"\"racePctAsian\",\n",
|
||||
"\"racePctHisp\",\n",
|
||||
"\"agePct12t21\",\n",
|
||||
"\"agePct12t29\",\n",
|
||||
"\"agePct16t24\",\n",
|
||||
"\"agePct65up\",\n",
|
||||
"\"numbUrban\",\n",
|
||||
"\"pctUrban\",\n",
|
||||
"\"medIncome\",\n",
|
||||
"\"pctWWage\",\n",
|
||||
"\"pctWFarmSelf\",\n",
|
||||
"\"pctWInvInc\",\n",
|
||||
"\"pctWSocSec\",\n",
|
||||
"\"pctWPubAsst\",\n",
|
||||
"\"pctWRetire\",\n",
|
||||
"\"medFamInc\",\n",
|
||||
"\"perCapInc\",\n",
|
||||
"\"whitePerCap\",\n",
|
||||
"\"blackPerCap\",\n",
|
||||
"\"indianPerCap\",\n",
|
||||
"\"AsianPerCap\",\n",
|
||||
"\"OtherPerCap\",\n",
|
||||
"\"HispPerCap\",\n",
|
||||
"\"NumUnderPov\",\n",
|
||||
"\"PctPopUnderPov\",\n",
|
||||
"\"PctLess9thGrade\",\n",
|
||||
"\"PctNotHSGrad\",\n",
|
||||
"\"PctBSorMore\",\n",
|
||||
"\"PctUnemployed\",\n",
|
||||
"\"PctEmploy\",\n",
|
||||
"\"PctEmplManu\",\n",
|
||||
"\"PctEmplProfServ\",\n",
|
||||
"\"PctOccupManu\",\n",
|
||||
"\"PctOccupMgmtProf\",\n",
|
||||
"\"MalePctDivorce\",\n",
|
||||
"\"MalePctNevMarr\",\n",
|
||||
"\"FemalePctDiv\",\n",
|
||||
"\"TotalPctDiv\",\n",
|
||||
"\"PersPerFam\",\n",
|
||||
"\"PctFam2Par\",\n",
|
||||
"\"PctKids2Par\",\n",
|
||||
"\"PctYoungKids2Par\",\n",
|
||||
"\"PctTeen2Par\",\n",
|
||||
"\"PctWorkMomYoungKids\",\n",
|
||||
"\"PctWorkMom\",\n",
|
||||
"\"NumIlleg\",\n",
|
||||
"\"PctIlleg\",\n",
|
||||
"\"NumImmig\",\n",
|
||||
"\"PctImmigRecent\",\n",
|
||||
"\"PctImmigRec5\",\n",
|
||||
"\"PctImmigRec8\",\n",
|
||||
"\"PctImmigRec10\",\n",
|
||||
"\"PctRecentImmig\",\n",
|
||||
"\"PctRecImmig5\",\n",
|
||||
"\"PctRecImmig8\",\n",
|
||||
"\"PctRecImmig10\",\n",
|
||||
"\"PctSpeakEnglOnly\",\n",
|
||||
"\"PctNotSpeakEnglWell\",\n",
|
||||
"\"PctLargHouseFam\",\n",
|
||||
"\"PctLargHouseOccup\",\n",
|
||||
"\"PersPerOccupHous\",\n",
|
||||
"\"PersPerOwnOccHous\",\n",
|
||||
"\"PersPerRentOccHous\",\n",
|
||||
"\"PctPersOwnOccup\",\n",
|
||||
"\"PctPersDenseHous\",\n",
|
||||
"\"PctHousLess3BR\",\n",
|
||||
"\"MedNumBR\",\n",
|
||||
"\"HousVacant\",\n",
|
||||
"\"PctHousOccup\",\n",
|
||||
"\"PctHousOwnOcc\",\n",
|
||||
"\"PctVacantBoarded\",\n",
|
||||
"\"PctVacMore6Mos\",\n",
|
||||
"\"MedYrHousBuilt\",\n",
|
||||
"\"PctHousNoPhone\",\n",
|
||||
"\"PctWOFullPlumb\",\n",
|
||||
"\"OwnOccLowQuart\",\n",
|
||||
"\"OwnOccMedVal\",\n",
|
||||
"\"OwnOccHiQuart\",\n",
|
||||
"\"RentLowQ\",\n",
|
||||
"\"RentMedian\",\n",
|
||||
"\"RentHighQ\",\n",
|
||||
"\"MedRent\",\n",
|
||||
"\"MedRentPctHousInc\",\n",
|
||||
"\"MedOwnCostPctInc\",\n",
|
||||
"\"MedOwnCostPctIncNoMtg\",\n",
|
||||
"\"NumInShelters\",\n",
|
||||
"\"NumStreet\",\n",
|
||||
"\"PctForeignBorn\",\n",
|
||||
"\"PctBornSameState\",\n",
|
||||
"\"PctSameHouse85\",\n",
|
||||
"\"PctSameCity85\",\n",
|
||||
"\"PctSameState85\",\n",
|
||||
"\"LemasSwornFT\",\n",
|
||||
"\"LemasSwFTPerPop\",\n",
|
||||
"\"LemasSwFTFieldOps\",\n",
|
||||
"\"LemasSwFTFieldPerPop\",\n",
|
||||
"\"LemasTotalReq\",\n",
|
||||
"\"LemasTotReqPerPop\",\n",
|
||||
"\"PolicReqPerOffic\",\n",
|
||||
"\"PolicPerPop\",\n",
|
||||
"\"RacialMatchCommPol\",\n",
|
||||
"\"PctPolicWhite\",\n",
|
||||
"\"PctPolicBlack\",\n",
|
||||
"\"PctPolicHisp\",\n",
|
||||
"\"PctPolicAsian\",\n",
|
||||
"\"PctPolicMinor\",\n",
|
||||
"\"OfficAssgnDrugUnits\",\n",
|
||||
"\"NumKindsDrugsSeiz\",\n",
|
||||
"\"PolicAveOTWorked\",\n",
|
||||
"\"LandArea\",\n",
|
||||
"\"PopDens\",\n",
|
||||
"\"PctUsePubTrans\",\n",
|
||||
"\"PolicCars\",\n",
|
||||
"\"PolicOperBudg\",\n",
|
||||
"\"LemasPctPolicOnPatr\",\n",
|
||||
"\"LemasGangUnitDeploy\",\n",
|
||||
"\"LemasPctOfficDrugUn\",\n",
|
||||
"\"PolicBudgPerPop\",\n",
|
||||
"\"ViolentCrimesPerPop\"]\n",
|
||||
"\n",
|
||||
"df = pd.read_csv('communities.data',names=col_names)\n",
|
||||
"df = df.replace('?',None)\n",
|
||||
"df = df.dropna(axis='rows')\n",
|
||||
"\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"le.fit(df['communityname'].unique())\n",
|
||||
"df['communityname'] = le.transform(df['communityname'])\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"id": "9c0f6f6d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = df.loc[:,df.columns != 'ViolentCrimesPerPop']\n",
|
||||
"y = df['ViolentCrimesPerPop']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"id": "2bdc2e77",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"id": "2bbd2ddb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/tonywesoly/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py:251: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.1533542718655332"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ridgecv = RidgeCV(alphas=np.arange(1, 100, 5), scoring='r2', cv=10)\n",
|
||||
"ridgecv.fit(X, y)\n",
|
||||
"ridge = Ridge(alpha=ridgecv.alpha_)\n",
|
||||
"ridge.fit(X_train, y_train)\n",
|
||||
"ridge_y_predicted = ridge.predict(X_test)\n",
|
||||
"ridge_rmse = np.sqrt(mean_squared_error(y_test, ridge_y_predicted))\n",
|
||||
"ridge_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"id": "dbfe728b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#poly = PolynomialFeatures(degree=11, include_bias=False)\n",
|
||||
"#poly_features = poly.fit_transform(X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"id": "3be15622",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.6511181528162753"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"poly_reg_model = LinearRegression()\n",
|
||||
"poly_reg_model.fit(X_train,y_train)\n",
|
||||
"poly_reg_y_predicted = poly_reg_model.predict(X_test)\n",
|
||||
"poly_reg_rmse = np.sqrt(mean_squared_error(y_test, poly_reg_y_predicted))\n",
|
||||
"poly_reg_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"id": "4ab0949a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.3085495600528652"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
|
||||
"lin_reg_model = LinearRegression()\n",
|
||||
"lin_reg_model.fit(X_train, y_train)\n",
|
||||
"lin_reg_y_predicted = lin_reg_model.predict(X_test)\n",
|
||||
"lin_reg_rmse = np.sqrt(mean_squared_error(y_test, lin_reg_y_predicted))\n",
|
||||
"lin_reg_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "99365180",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0e0b2f8e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
File diff suppressed because it is too large
Load Diff
|
@ -0,0 +1,522 @@
|
|||
Title: Communities and Crime
|
||||
|
||||
Abstract: Communities within the United States. The data combines socio-economic data
|
||||
from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime
|
||||
data from the 1995 FBI UCR.
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
Data Set Characteristics: Multivariate
|
||||
Attribute Characteristics: Real
|
||||
Associated Tasks: Regression
|
||||
Number of Instances: 1994
|
||||
Number of Attributes: 128
|
||||
Missing Values? Yes
|
||||
Area: Social
|
||||
Date Donated: 2009-07-13
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
Source:
|
||||
|
||||
Creator: Michael Redmond (redmond 'at' lasalle.edu); Computer Science; La Salle
|
||||
University; Philadelphia, PA, 19141, USA
|
||||
-- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law
|
||||
Enforcement Management and Administrative Statistics Survey, available from ICPSR at U
|
||||
of Michigan.
|
||||
-- Donor: Michael Redmond (redmond 'at' lasalle.edu); Computer Science; La Salle
|
||||
University; Philadelphia, PA, 19141, USA
|
||||
-- Date: July 2009
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
Data Set Information:
|
||||
|
||||
Many variables are included so that algorithms that select or learn weights for
|
||||
attributes could be tested. However, clearly unrelated attributes were not included;
|
||||
attributes were picked if there was any plausible connection to crime (N=122), plus
|
||||
the attribute to be predicted (Per Capita Violent Crimes). The variables included in
|
||||
the dataset involve the community, such as the percent of the population considered
|
||||
urban, and the median family income, and involving law enforcement, such as per capita
|
||||
number of police officers, and percent of officers assigned to drug units.
|
||||
|
||||
The per capita violent crimes variable was calculated using population and the sum of
|
||||
crime variables considered violent crimes in the United States: murder, rape, robbery,
|
||||
and assault. There was apparently some controversy in some states concerning the
|
||||
counting of rapes. These resulted in missing values for rape, which resulted in
|
||||
incorrect values for per capita violent crime. These cities are not included in the
|
||||
dataset. Many of these omitted communities were from the midwestern USA.
|
||||
|
||||
Data is described below based on original values. All numeric data was normalized into
|
||||
the decimal range 0.00-1.00 using an Unsupervised, equal-interval binning method.
|
||||
Attributes retain their distribution and skew (hence for example the population
|
||||
attribute has a mean value of 0.06 because most communities are small). E.g. An
|
||||
attribute described as 'mean people per household' is actually the normalized (0-1)
|
||||
version of that value.
|
||||
|
||||
The normalization preserves rough ratios of values WITHIN an attribute (e.g. double
|
||||
the value for double the population within the available precision - except for
|
||||
extreme values (all values more than 3 SD above the mean are normalized to 1.00; all
|
||||
values more than 3 SD below the mean are nromalized to 0.00)).
|
||||
|
||||
However, the normalization does not preserve relationships between values BETWEEN
|
||||
attributes (e.g. it would not be meaningful to compare the value for whitePerCap with
|
||||
the value for blackPerCap for a community)
|
||||
|
||||
A limitation was that the LEMAS survey was of the police departments with at least 100
|
||||
officers, plus a random sample of smaller departments. For our purposes, communities
|
||||
not found in both census and crime datasets were omitted. Many communities are missing
|
||||
LEMAS data.
|
||||
|
||||
.arff header for Weka:
|
||||
|
||||
@relation crimepredict
|
||||
|
||||
@attribute state numeric
|
||||
@attribute county numeric
|
||||
@attribute community numeric
|
||||
@attribute communityname string
|
||||
@attribute fold numeric
|
||||
@attribute population numeric
|
||||
@attribute householdsize numeric
|
||||
@attribute racepctblack numeric
|
||||
@attribute racePctWhite numeric
|
||||
@attribute racePctAsian numeric
|
||||
@attribute racePctHisp numeric
|
||||
@attribute agePct12t21 numeric
|
||||
@attribute agePct12t29 numeric
|
||||
@attribute agePct16t24 numeric
|
||||
@attribute agePct65up numeric
|
||||
@attribute numbUrban numeric
|
||||
@attribute pctUrban numeric
|
||||
@attribute medIncome numeric
|
||||
@attribute pctWWage numeric
|
||||
@attribute pctWFarmSelf numeric
|
||||
@attribute pctWInvInc numeric
|
||||
@attribute pctWSocSec numeric
|
||||
@attribute pctWPubAsst numeric
|
||||
@attribute pctWRetire numeric
|
||||
@attribute medFamInc numeric
|
||||
@attribute perCapInc numeric
|
||||
@attribute whitePerCap numeric
|
||||
@attribute blackPerCap numeric
|
||||
@attribute indianPerCap numeric
|
||||
@attribute AsianPerCap numeric
|
||||
@attribute OtherPerCap numeric
|
||||
@attribute HispPerCap numeric
|
||||
@attribute NumUnderPov numeric
|
||||
@attribute PctPopUnderPov numeric
|
||||
@attribute PctLess9thGrade numeric
|
||||
@attribute PctNotHSGrad numeric
|
||||
@attribute PctBSorMore numeric
|
||||
@attribute PctUnemployed numeric
|
||||
@attribute PctEmploy numeric
|
||||
@attribute PctEmplManu numeric
|
||||
@attribute PctEmplProfServ numeric
|
||||
@attribute PctOccupManu numeric
|
||||
@attribute PctOccupMgmtProf numeric
|
||||
@attribute MalePctDivorce numeric
|
||||
@attribute MalePctNevMarr numeric
|
||||
@attribute FemalePctDiv numeric
|
||||
@attribute TotalPctDiv numeric
|
||||
@attribute PersPerFam numeric
|
||||
@attribute PctFam2Par numeric
|
||||
@attribute PctKids2Par numeric
|
||||
@attribute PctYoungKids2Par numeric
|
||||
@attribute PctTeen2Par numeric
|
||||
@attribute PctWorkMomYoungKids numeric
|
||||
@attribute PctWorkMom numeric
|
||||
@attribute NumIlleg numeric
|
||||
@attribute PctIlleg numeric
|
||||
@attribute NumImmig numeric
|
||||
@attribute PctImmigRecent numeric
|
||||
@attribute PctImmigRec5 numeric
|
||||
@attribute PctImmigRec8 numeric
|
||||
@attribute PctImmigRec10 numeric
|
||||
@attribute PctRecentImmig numeric
|
||||
@attribute PctRecImmig5 numeric
|
||||
@attribute PctRecImmig8 numeric
|
||||
@attribute PctRecImmig10 numeric
|
||||
@attribute PctSpeakEnglOnly numeric
|
||||
@attribute PctNotSpeakEnglWell numeric
|
||||
@attribute PctLargHouseFam numeric
|
||||
@attribute PctLargHouseOccup numeric
|
||||
@attribute PersPerOccupHous numeric
|
||||
@attribute PersPerOwnOccHous numeric
|
||||
@attribute PersPerRentOccHous numeric
|
||||
@attribute PctPersOwnOccup numeric
|
||||
@attribute PctPersDenseHous numeric
|
||||
@attribute PctHousLess3BR numeric
|
||||
@attribute MedNumBR numeric
|
||||
@attribute HousVacant numeric
|
||||
@attribute PctHousOccup numeric
|
||||
@attribute PctHousOwnOcc numeric
|
||||
@attribute PctVacantBoarded numeric
|
||||
@attribute PctVacMore6Mos numeric
|
||||
@attribute MedYrHousBuilt numeric
|
||||
@attribute PctHousNoPhone numeric
|
||||
@attribute PctWOFullPlumb numeric
|
||||
@attribute OwnOccLowQuart numeric
|
||||
@attribute OwnOccMedVal numeric
|
||||
@attribute OwnOccHiQuart numeric
|
||||
@attribute RentLowQ numeric
|
||||
@attribute RentMedian numeric
|
||||
@attribute RentHighQ numeric
|
||||
@attribute MedRent numeric
|
||||
@attribute MedRentPctHousInc numeric
|
||||
@attribute MedOwnCostPctInc numeric
|
||||
@attribute MedOwnCostPctIncNoMtg numeric
|
||||
@attribute NumInShelters numeric
|
||||
@attribute NumStreet numeric
|
||||
@attribute PctForeignBorn numeric
|
||||
@attribute PctBornSameState numeric
|
||||
@attribute PctSameHouse85 numeric
|
||||
@attribute PctSameCity85 numeric
|
||||
@attribute PctSameState85 numeric
|
||||
@attribute LemasSwornFT numeric
|
||||
@attribute LemasSwFTPerPop numeric
|
||||
@attribute LemasSwFTFieldOps numeric
|
||||
@attribute LemasSwFTFieldPerPop numeric
|
||||
@attribute LemasTotalReq numeric
|
||||
@attribute LemasTotReqPerPop numeric
|
||||
@attribute PolicReqPerOffic numeric
|
||||
@attribute PolicPerPop numeric
|
||||
@attribute RacialMatchCommPol numeric
|
||||
@attribute PctPolicWhite numeric
|
||||
@attribute PctPolicBlack numeric
|
||||
@attribute PctPolicHisp numeric
|
||||
@attribute PctPolicAsian numeric
|
||||
@attribute PctPolicMinor numeric
|
||||
@attribute OfficAssgnDrugUnits numeric
|
||||
@attribute NumKindsDrugsSeiz numeric
|
||||
@attribute PolicAveOTWorked numeric
|
||||
@attribute LandArea numeric
|
||||
@attribute PopDens numeric
|
||||
@attribute PctUsePubTrans numeric
|
||||
@attribute PolicCars numeric
|
||||
@attribute PolicOperBudg numeric
|
||||
@attribute LemasPctPolicOnPatr numeric
|
||||
@attribute LemasGangUnitDeploy numeric
|
||||
@attribute LemasPctOfficDrugUn numeric
|
||||
@attribute PolicBudgPerPop numeric
|
||||
@attribute ViolentCrimesPerPop numeric
|
||||
|
||||
@data
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
Attribute Information:
|
||||
|
||||
Attribute Information: (122 predictive, 5 non-predictive, 1 goal)
|
||||
-- state: US state (by number) - not counted as predictive above, but if considered, should be consided nominal (nominal)
|
||||
-- county: numeric code for county - not predictive, and many missing values (numeric)
|
||||
-- community: numeric code for community - not predictive and many missing values (numeric)
|
||||
-- communityname: community name - not predictive - for information only (string)
|
||||
-- fold: fold number for non-random 10 fold cross validation, potentially useful for debugging, paired tests - not predictive (numeric)
|
||||
-- population: population for community: (numeric - decimal)
|
||||
-- householdsize: mean people per household (numeric - decimal)
|
||||
-- racepctblack: percentage of population that is african american (numeric - decimal)
|
||||
-- racePctWhite: percentage of population that is caucasian (numeric - decimal)
|
||||
-- racePctAsian: percentage of population that is of asian heritage (numeric - decimal)
|
||||
-- racePctHisp: percentage of population that is of hispanic heritage (numeric - decimal)
|
||||
-- agePct12t21: percentage of population that is 12-21 in age (numeric - decimal)
|
||||
-- agePct12t29: percentage of population that is 12-29 in age (numeric - decimal)
|
||||
-- agePct16t24: percentage of population that is 16-24 in age (numeric - decimal)
|
||||
-- agePct65up: percentage of population that is 65 and over in age (numeric - decimal)
|
||||
-- numbUrban: number of people living in areas classified as urban (numeric - decimal)
|
||||
-- pctUrban: percentage of people living in areas classified as urban (numeric - decimal)
|
||||
-- medIncome: median household income (numeric - decimal)
|
||||
-- pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal)
|
||||
-- pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal)
|
||||
-- pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal)
|
||||
-- pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal)
|
||||
-- pctWPubAsst: percentage of households with public assistance income in 1989 (numeric - decimal)
|
||||
-- pctWRetire: percentage of households with retirement income in 1989 (numeric - decimal)
|
||||
-- medFamInc: median family income (differs from household income for non-family households) (numeric - decimal)
|
||||
-- perCapInc: per capita income (numeric - decimal)
|
||||
-- whitePerCap: per capita income for caucasians (numeric - decimal)
|
||||
-- blackPerCap: per capita income for african americans (numeric - decimal)
|
||||
-- indianPerCap: per capita income for native americans (numeric - decimal)
|
||||
-- AsianPerCap: per capita income for people with asian heritage (numeric - decimal)
|
||||
-- OtherPerCap: per capita income for people with 'other' heritage (numeric - decimal)
|
||||
-- HispPerCap: per capita income for people with hispanic heritage (numeric - decimal)
|
||||
-- NumUnderPov: number of people under the poverty level (numeric - decimal)
|
||||
-- PctPopUnderPov: percentage of people under the poverty level (numeric - decimal)
|
||||
-- PctLess9thGrade: percentage of people 25 and over with less than a 9th grade education (numeric - decimal)
|
||||
-- PctNotHSGrad: percentage of people 25 and over that are not high school graduates (numeric - decimal)
|
||||
-- PctBSorMore: percentage of people 25 and over with a bachelors degree or higher education (numeric - decimal)
|
||||
-- PctUnemployed: percentage of people 16 and over, in the labor force, and unemployed (numeric - decimal)
|
||||
-- PctEmploy: percentage of people 16 and over who are employed (numeric - decimal)
|
||||
-- PctEmplManu: percentage of people 16 and over who are employed in manufacturing (numeric - decimal)
|
||||
-- PctEmplProfServ: percentage of people 16 and over who are employed in professional services (numeric - decimal)
|
||||
-- PctOccupManu: percentage of people 16 and over who are employed in manufacturing (numeric - decimal) ########
|
||||
-- PctOccupMgmtProf: percentage of people 16 and over who are employed in management or professional occupations (numeric - decimal)
|
||||
-- MalePctDivorce: percentage of males who are divorced (numeric - decimal)
|
||||
-- MalePctNevMarr: percentage of males who have never married (numeric - decimal)
|
||||
-- FemalePctDiv: percentage of females who are divorced (numeric - decimal)
|
||||
-- TotalPctDiv: percentage of population who are divorced (numeric - decimal)
|
||||
-- PersPerFam: mean number of people per family (numeric - decimal)
|
||||
-- PctFam2Par: percentage of families (with kids) that are headed by two parents (numeric - decimal)
|
||||
-- PctKids2Par: percentage of kids in family housing with two parents (numeric - decimal)
|
||||
-- PctYoungKids2Par: percent of kids 4 and under in two parent households (numeric - decimal)
|
||||
-- PctTeen2Par: percent of kids age 12-17 in two parent households (numeric - decimal)
|
||||
-- PctWorkMomYoungKids: percentage of moms of kids 6 and under in labor force (numeric - decimal)
|
||||
-- PctWorkMom: percentage of moms of kids under 18 in labor force (numeric - decimal)
|
||||
-- NumIlleg: number of kids born to never married (numeric - decimal)
|
||||
-- PctIlleg: percentage of kids born to never married (numeric - decimal)
|
||||
-- NumImmig: total number of people known to be foreign born (numeric - decimal)
|
||||
-- PctImmigRecent: percentage of _immigrants_ who immigated within last 3 years (numeric - decimal)
|
||||
-- PctImmigRec5: percentage of _immigrants_ who immigated within last 5 years (numeric - decimal)
|
||||
-- PctImmigRec8: percentage of _immigrants_ who immigated within last 8 years (numeric - decimal)
|
||||
-- PctImmigRec10: percentage of _immigrants_ who immigated within last 10 years (numeric - decimal)
|
||||
-- PctRecentImmig: percent of _population_ who have immigrated within the last 3 years (numeric - decimal)
|
||||
-- PctRecImmig5: percent of _population_ who have immigrated within the last 5 years (numeric - decimal)
|
||||
-- PctRecImmig8: percent of _population_ who have immigrated within the last 8 years (numeric - decimal)
|
||||
-- PctRecImmig10: percent of _population_ who have immigrated within the last 10 years (numeric - decimal)
|
||||
-- PctSpeakEnglOnly: percent of people who speak only English (numeric - decimal)
|
||||
-- PctNotSpeakEnglWell: percent of people who do not speak English well (numeric - decimal)
|
||||
-- PctLargHouseFam: percent of family households that are large (6 or more) (numeric - decimal)
|
||||
-- PctLargHouseOccup: percent of all occupied households that are large (6 or more people) (numeric - decimal)
|
||||
-- PersPerOccupHous: mean persons per household (numeric - decimal)
|
||||
-- PersPerOwnOccHous: mean persons per owner occupied household (numeric - decimal)
|
||||
-- PersPerRentOccHous: mean persons per rental household (numeric - decimal)
|
||||
-- PctPersOwnOccup: percent of people in owner occupied households (numeric - decimal)
|
||||
-- PctPersDenseHous: percent of persons in dense housing (more than 1 person per room) (numeric - decimal)
|
||||
-- PctHousLess3BR: percent of housing units with less than 3 bedrooms (numeric - decimal)
|
||||
-- MedNumBR: median number of bedrooms (numeric - decimal)
|
||||
-- HousVacant: number of vacant households (numeric - decimal)
|
||||
-- PctHousOccup: percent of housing occupied (numeric - decimal)
|
||||
-- PctHousOwnOcc: percent of households owner occupied (numeric - decimal)
|
||||
-- PctVacantBoarded: percent of vacant housing that is boarded up (numeric - decimal)
|
||||
-- PctVacMore6Mos: percent of vacant housing that has been vacant more than 6 months (numeric - decimal)
|
||||
-- MedYrHousBuilt: median year housing units built (numeric - decimal)
|
||||
-- PctHousNoPhone: percent of occupied housing units without phone (in 1990, this was rare!) (numeric - decimal)
|
||||
-- PctWOFullPlumb: percent of housing without complete plumbing facilities (numeric - decimal)
|
||||
-- OwnOccLowQuart: owner occupied housing - lower quartile value (numeric - decimal)
|
||||
-- OwnOccMedVal: owner occupied housing - median value (numeric - decimal)
|
||||
-- OwnOccHiQuart: owner occupied housing - upper quartile value (numeric - decimal)
|
||||
-- RentLowQ: rental housing - lower quartile rent (numeric - decimal)
|
||||
-- RentMedian: rental housing - median rent (Census variable H32B from file STF1A) (numeric - decimal)
|
||||
-- RentHighQ: rental housing - upper quartile rent (numeric - decimal)
|
||||
-- MedRent: median gross rent (Census variable H43A from file STF3A - includes utilities) (numeric - decimal)
|
||||
-- MedRentPctHousInc: median gross rent as a percentage of household income (numeric - decimal)
|
||||
-- MedOwnCostPctInc: median owners cost as a percentage of household income - for owners with a mortgage (numeric - decimal)
|
||||
-- MedOwnCostPctIncNoMtg: median owners cost as a percentage of household income - for owners without a mortgage (numeric - decimal)
|
||||
-- NumInShelters: number of people in homeless shelters (numeric - decimal)
|
||||
-- NumStreet: number of homeless people counted in the street (numeric - decimal)
|
||||
-- PctForeignBorn: percent of people foreign born (numeric - decimal)
|
||||
-- PctBornSameState: percent of people born in the same state as currently living (numeric - decimal)
|
||||
-- PctSameHouse85: percent of people living in the same house as in 1985 (5 years before) (numeric - decimal)
|
||||
-- PctSameCity85: percent of people living in the same city as in 1985 (5 years before) (numeric - decimal)
|
||||
-- PctSameState85: percent of people living in the same state as in 1985 (5 years before) (numeric - decimal)
|
||||
-- LemasSwornFT: number of sworn full time police officers (numeric - decimal)
|
||||
-- LemasSwFTPerPop: sworn full time police officers per 100K population (numeric - decimal)
|
||||
-- LemasSwFTFieldOps: number of sworn full time police officers in field operations (on the street as opposed to administrative etc) (numeric - decimal)
|
||||
-- LemasSwFTFieldPerPop: sworn full time police officers in field operations (on the street as opposed to administrative etc) per 100K population (numeric - decimal)
|
||||
-- LemasTotalReq: total requests for police (numeric - decimal)
|
||||
-- LemasTotReqPerPop: total requests for police per 100K popuation (numeric - decimal)
|
||||
-- PolicReqPerOffic: total requests for police per police officer (numeric - decimal)
|
||||
-- PolicPerPop: police officers per 100K population (numeric - decimal)
|
||||
-- RacialMatchCommPol: a measure of the racial match between the community and the police force. High values indicate proportions in community and police force are similar (numeric - decimal)
|
||||
-- PctPolicWhite: percent of police that are caucasian (numeric - decimal)
|
||||
-- PctPolicBlack: percent of police that are african american (numeric - decimal)
|
||||
-- PctPolicHisp: percent of police that are hispanic (numeric - decimal)
|
||||
-- PctPolicAsian: percent of police that are asian (numeric - decimal)
|
||||
-- PctPolicMinor: percent of police that are minority of any kind (numeric - decimal)
|
||||
-- OfficAssgnDrugUnits: number of officers assigned to special drug units (numeric - decimal)
|
||||
-- NumKindsDrugsSeiz: number of different kinds of drugs seized (numeric - decimal)
|
||||
-- PolicAveOTWorked: police average overtime worked (numeric - decimal)
|
||||
-- LandArea: land area in square miles (numeric - decimal)
|
||||
-- PopDens: population density in persons per square mile (numeric - decimal)
|
||||
-- PctUsePubTrans: percent of people using public transit for commuting (numeric - decimal)
|
||||
-- PolicCars: number of police cars (numeric - decimal)
|
||||
-- PolicOperBudg: police operating budget (numeric - decimal)
|
||||
-- LemasPctPolicOnPatr: percent of sworn full time police officers on patrol (numeric - decimal)
|
||||
-- LemasGangUnitDeploy: gang unit deployed (numeric - decimal - but really ordinal - 0 means NO, 1 means YES, 0.5 means Part Time)
|
||||
-- LemasPctOfficDrugUn: percent of officers assigned to drug units (numeric - decimal)
|
||||
-- PolicBudgPerPop: police operating budget per population (numeric - decimal)
|
||||
-- ViolentCrimesPerPop: total number of violent crimes per 100K popuation (numeric - decimal) GOAL attribute (to be predicted)
|
||||
|
||||
Summary Statistics:
|
||||
Min Max Mean SD Correl Median Mode Missing
|
||||
population 0 1 0.06 0.13 0.37 0.02 0.01 0
|
||||
householdsize 0 1 0.46 0.16 -0.03 0.44 0.41 0
|
||||
racepctblack 0 1 0.18 0.25 0.63 0.06 0.01 0
|
||||
racePctWhite 0 1 0.75 0.24 -0.68 0.85 0.98 0
|
||||
racePctAsian 0 1 0.15 0.21 0.04 0.07 0.02 0
|
||||
racePctHisp 0 1 0.14 0.23 0.29 0.04 0.01 0
|
||||
agePct12t21 0 1 0.42 0.16 0.06 0.4 0.38 0
|
||||
agePct12t29 0 1 0.49 0.14 0.15 0.48 0.49 0
|
||||
agePct16t24 0 1 0.34 0.17 0.10 0.29 0.29 0
|
||||
agePct65up 0 1 0.42 0.18 0.07 0.42 0.47 0
|
||||
numbUrban 0 1 0.06 0.13 0.36 0.03 0 0
|
||||
pctUrban 0 1 0.70 0.44 0.08 1 1 0
|
||||
medIncome 0 1 0.36 0.21 -0.42 0.32 0.23 0
|
||||
pctWWage 0 1 0.56 0.18 -0.31 0.56 0.58 0
|
||||
pctWFarmSelf 0 1 0.29 0.20 -0.15 0.23 0.16 0
|
||||
pctWInvInc 0 1 0.50 0.18 -0.58 0.48 0.41 0
|
||||
pctWSocSec 0 1 0.47 0.17 0.12 0.475 0.56 0
|
||||
pctWPubAsst 0 1 0.32 0.22 0.57 0.26 0.1 0
|
||||
pctWRetire 0 1 0.48 0.17 -0.10 0.47 0.44 0
|
||||
medFamInc 0 1 0.38 0.20 -0.44 0.33 0.25 0
|
||||
perCapInc 0 1 0.35 0.19 -0.35 0.3 0.23 0
|
||||
whitePerCap 0 1 0.37 0.19 -0.21 0.32 0.3 0
|
||||
blackPerCap 0 1 0.29 0.17 -0.28 0.25 0.18 0
|
||||
indianPerCap 0 1 0.20 0.16 -0.09 0.17 0 0
|
||||
AsianPerCap 0 1 0.32 0.20 -0.16 0.28 0.18 0
|
||||
OtherPerCap 0 1 0.28 0.19 -0.13 0.25 0 1
|
||||
HispPerCap 0 1 0.39 0.18 -0.24 0.345 0.3 0
|
||||
NumUnderPov 0 1 0.06 0.13 0.45 0.02 0.01 0
|
||||
PctPopUnderPov 0 1 0.30 0.23 0.52 0.25 0.08 0
|
||||
PctLess9thGrade 0 1 0.32 0.21 0.41 0.27 0.19 0
|
||||
PctNotHSGrad 0 1 0.38 0.20 0.48 0.36 0.39 0
|
||||
PctBSorMore 0 1 0.36 0.21 -0.31 0.31 0.18 0
|
||||
PctUnemployed 0 1 0.36 0.20 0.50 0.32 0.24 0
|
||||
PctEmploy 0 1 0.50 0.17 -0.33 0.51 0.56 0
|
||||
PctEmplManu 0 1 0.40 0.20 -0.04 0.37 0.26 0
|
||||
PctEmplProfServ 0 1 0.44 0.18 -0.07 0.41 0.36 0
|
||||
PctOccupManu 0 1 0.39 0.20 0.30 0.37 0.32 0
|
||||
PctOccupMgmtProf 0 1 0.44 0.19 -0.34 0.4 0.36 0
|
||||
MalePctDivorce 0 1 0.46 0.18 0.53 0.47 0.56 0
|
||||
MalePctNevMarr 0 1 0.43 0.18 0.30 0.4 0.38 0
|
||||
FemalePctDiv 0 1 0.49 0.18 0.56 0.5 0.54 0
|
||||
TotalPctDiv 0 1 0.49 0.18 0.55 0.5 0.57 0
|
||||
PersPerFam 0 1 0.49 0.15 0.14 0.47 0.44 0
|
||||
PctFam2Par 0 1 0.61 0.20 -0.71 0.63 0.7 0
|
||||
PctKids2Par 0 1 0.62 0.21 -0.74 0.64 0.72 0
|
||||
PctYoungKids2Par 0 1 0.66 0.22 -0.67 0.7 0.91 0
|
||||
PctTeen2Par 0 1 0.58 0.19 -0.66 0.61 0.6 0
|
||||
PctWorkMomYoungKids 0 1 0.50 0.17 -0.02 0.51 0.51 0
|
||||
PctWorkMom 0 1 0.53 0.18 -0.15 0.54 0.57 0
|
||||
NumIlleg 0 1 0.04 0.11 0.47 0.01 0 0
|
||||
PctIlleg 0 1 0.25 0.23 0.74 0.17 0.09 0
|
||||
NumImmig 0 1 0.03 0.09 0.29 0.01 0 0
|
||||
PctImmigRecent 0 1 0.32 0.22 0.17 0.29 0 0
|
||||
PctImmigRec5 0 1 0.36 0.21 0.22 0.34 0 0
|
||||
PctImmigRec8 0 1 0.40 0.20 0.25 0.39 0.26 0
|
||||
PctImmigRec10 0 1 0.43 0.19 0.29 0.43 0.43 0
|
||||
PctRecentImmig 0 1 0.18 0.24 0.23 0.09 0.01 0
|
||||
PctRecImmig5 0 1 0.18 0.24 0.25 0.08 0.02 0
|
||||
PctRecImmig8 0 1 0.18 0.24 0.25 0.09 0.02 0
|
||||
PctRecImmig10 0 1 0.18 0.23 0.26 0.09 0.02 0
|
||||
PctSpeakEnglOnly 0 1 0.79 0.23 -0.24 0.87 0.96 0
|
||||
PctNotSpeakEnglWell 0 1 0.15 0.22 0.30 0.06 0.03 0
|
||||
PctLargHouseFam 0 1 0.27 0.20 0.38 0.2 0.17 0
|
||||
PctLargHouseOccup 0 1 0.25 0.19 0.29 0.19 0.19 0
|
||||
PersPerOccupHous 0 1 0.46 0.17 -0.04 0.44 0.37 0
|
||||
PersPerOwnOccHous 0 1 0.49 0.16 -0.12 0.48 0.45 0
|
||||
PersPerRentOccHous 0 1 0.40 0.19 0.25 0.36 0.32 0
|
||||
PctPersOwnOccup 0 1 0.56 0.20 -0.53 0.56 0.54 0
|
||||
PctPersDenseHous 0 1 0.19 0.21 0.45 0.11 0.06 0
|
||||
PctHousLess3BR 0 1 0.50 0.17 0.47 0.51 0.53 0
|
||||
MedNumBR 0 1 0.31 0.26 -0.36 0.5 0.5 0
|
||||
HousVacant 0 1 0.08 0.15 0.42 0.03 0.01 0
|
||||
PctHousOccup 0 1 0.72 0.19 -0.32 0.77 0.88 0
|
||||
PctHousOwnOcc 0 1 0.55 0.19 -0.47 0.54 0.52 0
|
||||
PctVacantBoarded 0 1 0.20 0.22 0.48 0.13 0 0
|
||||
PctVacMore6Mos 0 1 0.43 0.19 0.02 0.42 0.44 0
|
||||
MedYrHousBuilt 0 1 0.49 0.23 -0.11 0.52 0 0
|
||||
PctHousNoPhone 0 1 0.26 0.24 0.49 0.185 0.01 0
|
||||
PctWOFullPlumb 0 1 0.24 0.21 0.36 0.19 0 0
|
||||
OwnOccLowQuart 0 1 0.26 0.22 -0.21 0.18 0.09 0
|
||||
OwnOccMedVal 0 1 0.26 0.23 -0.19 0.17 0.08 0
|
||||
OwnOccHiQuart 0 1 0.27 0.24 -0.17 0.18 0.08 0
|
||||
RentLowQ 0 1 0.35 0.22 -0.25 0.31 0.13 0
|
||||
RentMedian 0 1 0.37 0.21 -0.24 0.33 0.19 0
|
||||
RentHighQ 0 1 0.42 0.25 -0.23 0.37 1 0
|
||||
MedRent 0 1 0.38 0.21 -0.24 0.34 0.17 0
|
||||
MedRentPctHousInc 0 1 0.49 0.17 0.33 0.48 0.4 0
|
||||
MedOwnCostPctInc 0 1 0.45 0.19 0.06 0.45 0.41 0
|
||||
MedOwnCostPctIncNoMtg 0 1 0.40 0.19 0.05 0.37 0.24 0
|
||||
NumInShelters 0 1 0.03 0.10 0.38 0 0 0
|
||||
NumStreet 0 1 0.02 0.10 0.34 0 0 0
|
||||
PctForeignBorn 0 1 0.22 0.23 0.19 0.13 0.03 0
|
||||
PctBornSameState 0 1 0.61 0.20 -0.08 0.63 0.78 0
|
||||
PctSameHouse85 0 1 0.54 0.18 -0.16 0.54 0.59 0
|
||||
PctSameCity85 0 1 0.63 0.20 0.08 0.67 0.74 0
|
||||
PctSameState85 0 1 0.65 0.20 -0.02 0.7 0.79 0
|
||||
LemasSwornFT 0 1 0.07 0.14 0.34 0.02 0.02 1675
|
||||
LemasSwFTPerPop 0 1 0.22 0.16 0.15 0.18 0.2 1675
|
||||
LemasSwFTFieldOps 0 1 0.92 0.13 -0.33 0.97 0.98 1675
|
||||
LemasSwFTFieldPerPop 0 1 0.25 0.16 0.16 0.21 0.19 1675
|
||||
LemasTotalReq 0 1 0.10 0.16 0.35 0.04 0.02 1675
|
||||
LemasTotReqPerPop 0 1 0.22 0.16 0.27 0.17 0.14 1675
|
||||
PolicReqPerOffic 0 1 0.34 0.20 0.17 0.29 0.23 1675
|
||||
PolicPerPop 0 1 0.22 0.16 0.15 0.18 0.2 1675
|
||||
RacialMatchCommPol 0 1 0.69 0.23 -0.46 0.74 0.78 1675
|
||||
PctPolicWhite 0 1 0.73 0.22 -0.44 0.78 0.72 1675
|
||||
PctPolicBlack 0 1 0.22 0.24 0.54 0.12 0 1675
|
||||
PctPolicHisp 0 1 0.13 0.20 0.12 0.06 0 1675
|
||||
PctPolicAsian 0 1 0.11 0.23 0.10 0 0 1675
|
||||
PctPolicMinor 0 1 0.26 0.23 0.49 0.2 0.07 1675
|
||||
OfficAssgnDrugUnits 0 1 0.08 0.12 0.34 0.04 0.03 1675
|
||||
NumKindsDrugsSeiz 0 1 0.56 0.20 0.13 0.57 0.57 1675
|
||||
PolicAveOTWorked 0 1 0.31 0.23 0.03 0.26 0.19 1675
|
||||
LandArea 0 1 0.07 0.11 0.20 0.04 0.01 0
|
||||
PopDens 0 1 0.23 0.20 0.28 0.17 0.09 0
|
||||
PctUsePubTrans 0 1 0.16 0.23 0.15 0.07 0.01 0
|
||||
PolicCars 0 1 0.16 0.21 0.38 0.08 0.02 1675
|
||||
PolicOperBudg 0 1 0.08 0.14 0.34 0.03 0.02 1675
|
||||
LemasPctPolicOnPatr 0 1 0.70 0.21 -0.08 0.75 0.74 1675
|
||||
LemasGangUnitDeploy 0 1 0.44 0.41 0.12 0.5 0 1675
|
||||
LemasPctOfficDrugUn 0 1 0.09 0.24 0.35 0 0 0
|
||||
PolicBudgPerPop 0 1 0.20 0.16 0.10 0.15 0.12 1675
|
||||
ViolentCrimesPerPop 0 1 0.24 0.23 1.00 0.15 0.03 0
|
||||
|
||||
Distribution of the Goal Variable (Violent Crimes per Population):
|
||||
Range Frequency
|
||||
0.000-0.067 484
|
||||
0.067-0.133 420
|
||||
0.133-0.200 284
|
||||
0.200-0.267 177
|
||||
0.267-0.333 142
|
||||
0.333-0.400 113
|
||||
0.400-0.467 59
|
||||
0.467-0.533 76
|
||||
0.533-0.600 57
|
||||
0.600-0.667 38
|
||||
0.667-0.733 37
|
||||
0.733-0.800 20
|
||||
0.800-0.867 23
|
||||
0.867-0.933 14
|
||||
0.933-1.000 50
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
Relevant Papers:
|
||||
|
||||
No published results using this specific dataset.
|
||||
|
||||
Related dataset used in Redmond and Baveja 'A data-driven software tool for enabling
|
||||
cooperative information sharing among police departments' in European Journal of
|
||||
Operational Research 141 (2002) 660-678;
|
||||
That article includes a description of the integration of the three sources of data,
|
||||
however, this data is normalized differently and more/different attributes are
|
||||
included.
|
||||
|
||||
-----------------------------------------------------------------------------------------
|
||||
|
||||
Citation Request:
|
||||
|
||||
Please cite the UCI Machine Learning Repository, my sources and my related paper:
|
||||
|
||||
U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing
|
||||
1990 United States: Summary Tape File 1a & 3a (Computer Files),
|
||||
|
||||
U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and
|
||||
Inter-university Consortium for Political and Social Research Ann Arbor, Michigan.
|
||||
(1992)
|
||||
|
||||
U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management
|
||||
And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of
|
||||
The Census Producer, Washington, DC and Inter-university Consortium for Political and
|
||||
Social Research Ann Arbor, Michigan. (1992)
|
||||
|
||||
U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United
|
||||
States (Computer File) (1995)
|
||||
|
||||
Redmond, M. A. and A. Baveja: A Data-Driven Software Tool for Enabling Cooperative
|
||||
Information Sharing Among Police Departments. European Journal of Operational Research
|
||||
141 (2002) 660-678.
|
|
@ -0,0 +1,703 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"id": "ffd08cc9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
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|
||||
"\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>state</th>\n",
|
||||
" <th>county</th>\n",
|
||||
" <th>community</th>\n",
|
||||
" <th>communityname</th>\n",
|
||||
" <th>fold</th>\n",
|
||||
" <th>population</th>\n",
|
||||
" <th>householdsize</th>\n",
|
||||
" <th>racepctblack</th>\n",
|
||||
" <th>racePctWhite</th>\n",
|
||||
" <th>racePctAsian</th>\n",
|
||||
" <th>...</th>\n",
|
||||
" <th>LandArea</th>\n",
|
||||
" <th>PopDens</th>\n",
|
||||
" <th>PctUsePubTrans</th>\n",
|
||||
" <th>PolicCars</th>\n",
|
||||
" <th>PolicOperBudg</th>\n",
|
||||
" <th>LemasPctPolicOnPatr</th>\n",
|
||||
" <th>LemasGangUnitDeploy</th>\n",
|
||||
" <th>LemasPctOfficDrugUn</th>\n",
|
||||
" <th>PolicBudgPerPop</th>\n",
|
||||
" <th>ViolentCrimesPerPop</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>16</th>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1000</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.15</td>\n",
|
||||
" <td>0.31</td>\n",
|
||||
" <td>0.40</td>\n",
|
||||
" <td>0.63</td>\n",
|
||||
" <td>0.14</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.84</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.91</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>0.88</td>\n",
|
||||
" <td>0.26</td>\n",
|
||||
" <td>0.49</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>193</td>\n",
|
||||
" <td>93926</td>\n",
|
||||
" <td>94</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.43</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.89</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.16</td>\n",
|
||||
" <td>0.12</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.81</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.56</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.63</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>33</th>\n",
|
||||
" <td>51</td>\n",
|
||||
" <td>680</td>\n",
|
||||
" <td>47672</td>\n",
|
||||
" <td>52</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.43</td>\n",
|
||||
" <td>0.51</td>\n",
|
||||
" <td>0.58</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.14</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.75</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.60</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>0.31</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>68</th>\n",
|
||||
" <td>34</td>\n",
|
||||
" <td>23</td>\n",
|
||||
" <td>58200</td>\n",
|
||||
" <td>79</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>0.59</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.73</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.64</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1.00</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" <td>0.50</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>74</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>46520</td>\n",
|
||||
" <td>58</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.85</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.21</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.7</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.44</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>0.14</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1880</th>\n",
|
||||
" <td>34</td>\n",
|
||||
" <td>39</td>\n",
|
||||
" <td>40350</td>\n",
|
||||
" <td>50</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.39</td>\n",
|
||||
" <td>0.65</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.85</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.99</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1963</th>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>27</td>\n",
|
||||
" <td>59641</td>\n",
|
||||
" <td>85</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.61</td>\n",
|
||||
" <td>0.47</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.47</td>\n",
|
||||
" <td>0.42</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.49</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.37</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0.45</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1981</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>35650</td>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" <td>0.38</td>\n",
|
||||
" <td>0.17</td>\n",
|
||||
" <td>0.84</td>\n",
|
||||
" <td>0.11</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.09</td>\n",
|
||||
" <td>0.13</td>\n",
|
||||
" <td>0.17</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.72</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.62</td>\n",
|
||||
" <td>0.15</td>\n",
|
||||
" <td>0.07</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1991</th>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>80070</td>\n",
|
||||
" <td>110</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.16</td>\n",
|
||||
" <td>0.37</td>\n",
|
||||
" <td>0.25</td>\n",
|
||||
" <td>0.69</td>\n",
|
||||
" <td>0.04</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.32</td>\n",
|
||||
" <td>0.18</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.78</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.91</td>\n",
|
||||
" <td>0.28</td>\n",
|
||||
" <td>0.23</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1992</th>\n",
|
||||
" <td>25</td>\n",
|
||||
" <td>17</td>\n",
|
||||
" <td>72600</td>\n",
|
||||
" <td>107</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.08</td>\n",
|
||||
" <td>0.51</td>\n",
|
||||
" <td>0.06</td>\n",
|
||||
" <td>0.87</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.38</td>\n",
|
||||
" <td>0.33</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.79</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0.22</td>\n",
|
||||
" <td>0.18</td>\n",
|
||||
" <td>0.19</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>123 rows × 128 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" state county community communityname fold population householdsize \\\n",
|
||||
"16 36 1 1000 0 1 0.15 0.31 \n",
|
||||
"23 19 193 93926 94 1 0.11 0.43 \n",
|
||||
"33 51 680 47672 52 1 0.09 0.43 \n",
|
||||
"68 34 23 58200 79 1 0.05 0.59 \n",
|
||||
"74 9 9 46520 58 1 0.08 0.39 \n",
|
||||
"... ... ... ... ... ... ... ... \n",
|
||||
"1880 34 39 40350 50 10 0.04 0.39 \n",
|
||||
"1963 36 27 59641 85 10 0.03 0.32 \n",
|
||||
"1981 9 9 35650 36 10 0.07 0.38 \n",
|
||||
"1991 9 9 80070 110 10 0.16 0.37 \n",
|
||||
"1992 25 17 72600 107 10 0.08 0.51 \n",
|
||||
"\n",
|
||||
" racepctblack racePctWhite racePctAsian ... LandArea PopDens \\\n",
|
||||
"16 0.40 0.63 0.14 ... 0.06 0.39 \n",
|
||||
"23 0.04 0.89 0.09 ... 0.16 0.12 \n",
|
||||
"33 0.51 0.58 0.04 ... 0.14 0.11 \n",
|
||||
"68 0.23 0.39 0.09 ... 0.01 0.73 \n",
|
||||
"74 0.08 0.85 0.04 ... 0.07 0.21 \n",
|
||||
"... ... ... ... ... ... ... \n",
|
||||
"1880 0.39 0.65 0.09 ... 0.03 0.28 \n",
|
||||
"1963 0.61 0.47 0.09 ... 0.01 0.47 \n",
|
||||
"1981 0.17 0.84 0.11 ... 0.09 0.13 \n",
|
||||
"1991 0.25 0.69 0.04 ... 0.08 0.32 \n",
|
||||
"1992 0.06 0.87 0.22 ... 0.03 0.38 \n",
|
||||
"\n",
|
||||
" PctUsePubTrans PolicCars PolicOperBudg LemasPctPolicOnPatr \\\n",
|
||||
"16 0.84 0.06 0.06 0.91 \n",
|
||||
"23 0.07 0.04 0.01 0.81 \n",
|
||||
"33 0.19 0.05 0.01 0.75 \n",
|
||||
"68 0.28 0 0.02 0.64 \n",
|
||||
"74 0.04 0.02 0.01 0.7 \n",
|
||||
"... ... ... ... ... \n",
|
||||
"1880 0.32 0.02 0.01 0.85 \n",
|
||||
"1963 0.42 0.07 0.08 0.49 \n",
|
||||
"1981 0.17 0.02 0.01 0.72 \n",
|
||||
"1991 0.18 0.08 0.06 0.78 \n",
|
||||
"1992 0.33 0.02 0.02 0.79 \n",
|
||||
"\n",
|
||||
" LemasGangUnitDeploy LemasPctOfficDrugUn PolicBudgPerPop \\\n",
|
||||
"16 0.5 0.88 0.26 \n",
|
||||
"23 1 0.56 0.09 \n",
|
||||
"33 0 0.60 0.1 \n",
|
||||
"68 0 1.00 0.23 \n",
|
||||
"74 1 0.44 0.11 \n",
|
||||
"... ... ... ... \n",
|
||||
"1880 0 0.99 0.19 \n",
|
||||
"1963 0 0.37 1 \n",
|
||||
"1981 0 0.62 0.15 \n",
|
||||
"1991 0 0.91 0.28 \n",
|
||||
"1992 0 0.22 0.18 \n",
|
||||
"\n",
|
||||
" ViolentCrimesPerPop \n",
|
||||
"16 0.49 \n",
|
||||
"23 0.63 \n",
|
||||
"33 0.31 \n",
|
||||
"68 0.50 \n",
|
||||
"74 0.14 \n",
|
||||
"... ... \n",
|
||||
"1880 0.22 \n",
|
||||
"1963 0.45 \n",
|
||||
"1981 0.07 \n",
|
||||
"1991 0.23 \n",
|
||||
"1992 0.19 \n",
|
||||
"\n",
|
||||
"[123 rows x 128 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import sklearn\n",
|
||||
"from sklearn.preprocessing import PolynomialFeatures, LabelEncoder\n",
|
||||
"from sklearn.linear_model import LinearRegression, Ridge, RidgeCV\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"\n",
|
||||
"col_names = [\n",
|
||||
"\"state\",\n",
|
||||
"\"county\",\n",
|
||||
"\"community\",\n",
|
||||
"\"communityname\",\n",
|
||||
"\"fold\",\n",
|
||||
"\"population\",\n",
|
||||
"\"householdsize\",\n",
|
||||
"\"racepctblack\",\n",
|
||||
"\"racePctWhite\",\n",
|
||||
"\"racePctAsian\",\n",
|
||||
"\"racePctHisp\",\n",
|
||||
"\"agePct12t21\",\n",
|
||||
"\"agePct12t29\",\n",
|
||||
"\"agePct16t24\",\n",
|
||||
"\"agePct65up\",\n",
|
||||
"\"numbUrban\",\n",
|
||||
"\"pctUrban\",\n",
|
||||
"\"medIncome\",\n",
|
||||
"\"pctWWage\",\n",
|
||||
"\"pctWFarmSelf\",\n",
|
||||
"\"pctWInvInc\",\n",
|
||||
"\"pctWSocSec\",\n",
|
||||
"\"pctWPubAsst\",\n",
|
||||
"\"pctWRetire\",\n",
|
||||
"\"medFamInc\",\n",
|
||||
"\"perCapInc\",\n",
|
||||
"\"whitePerCap\",\n",
|
||||
"\"blackPerCap\",\n",
|
||||
"\"indianPerCap\",\n",
|
||||
"\"AsianPerCap\",\n",
|
||||
"\"OtherPerCap\",\n",
|
||||
"\"HispPerCap\",\n",
|
||||
"\"NumUnderPov\",\n",
|
||||
"\"PctPopUnderPov\",\n",
|
||||
"\"PctLess9thGrade\",\n",
|
||||
"\"PctNotHSGrad\",\n",
|
||||
"\"PctBSorMore\",\n",
|
||||
"\"PctUnemployed\",\n",
|
||||
"\"PctEmploy\",\n",
|
||||
"\"PctEmplManu\",\n",
|
||||
"\"PctEmplProfServ\",\n",
|
||||
"\"PctOccupManu\",\n",
|
||||
"\"PctOccupMgmtProf\",\n",
|
||||
"\"MalePctDivorce\",\n",
|
||||
"\"MalePctNevMarr\",\n",
|
||||
"\"FemalePctDiv\",\n",
|
||||
"\"TotalPctDiv\",\n",
|
||||
"\"PersPerFam\",\n",
|
||||
"\"PctFam2Par\",\n",
|
||||
"\"PctKids2Par\",\n",
|
||||
"\"PctYoungKids2Par\",\n",
|
||||
"\"PctTeen2Par\",\n",
|
||||
"\"PctWorkMomYoungKids\",\n",
|
||||
"\"PctWorkMom\",\n",
|
||||
"\"NumIlleg\",\n",
|
||||
"\"PctIlleg\",\n",
|
||||
"\"NumImmig\",\n",
|
||||
"\"PctImmigRecent\",\n",
|
||||
"\"PctImmigRec5\",\n",
|
||||
"\"PctImmigRec8\",\n",
|
||||
"\"PctImmigRec10\",\n",
|
||||
"\"PctRecentImmig\",\n",
|
||||
"\"PctRecImmig5\",\n",
|
||||
"\"PctRecImmig8\",\n",
|
||||
"\"PctRecImmig10\",\n",
|
||||
"\"PctSpeakEnglOnly\",\n",
|
||||
"\"PctNotSpeakEnglWell\",\n",
|
||||
"\"PctLargHouseFam\",\n",
|
||||
"\"PctLargHouseOccup\",\n",
|
||||
"\"PersPerOccupHous\",\n",
|
||||
"\"PersPerOwnOccHous\",\n",
|
||||
"\"PersPerRentOccHous\",\n",
|
||||
"\"PctPersOwnOccup\",\n",
|
||||
"\"PctPersDenseHous\",\n",
|
||||
"\"PctHousLess3BR\",\n",
|
||||
"\"MedNumBR\",\n",
|
||||
"\"HousVacant\",\n",
|
||||
"\"PctHousOccup\",\n",
|
||||
"\"PctHousOwnOcc\",\n",
|
||||
"\"PctVacantBoarded\",\n",
|
||||
"\"PctVacMore6Mos\",\n",
|
||||
"\"MedYrHousBuilt\",\n",
|
||||
"\"PctHousNoPhone\",\n",
|
||||
"\"PctWOFullPlumb\",\n",
|
||||
"\"OwnOccLowQuart\",\n",
|
||||
"\"OwnOccMedVal\",\n",
|
||||
"\"OwnOccHiQuart\",\n",
|
||||
"\"RentLowQ\",\n",
|
||||
"\"RentMedian\",\n",
|
||||
"\"RentHighQ\",\n",
|
||||
"\"MedRent\",\n",
|
||||
"\"MedRentPctHousInc\",\n",
|
||||
"\"MedOwnCostPctInc\",\n",
|
||||
"\"MedOwnCostPctIncNoMtg\",\n",
|
||||
"\"NumInShelters\",\n",
|
||||
"\"NumStreet\",\n",
|
||||
"\"PctForeignBorn\",\n",
|
||||
"\"PctBornSameState\",\n",
|
||||
"\"PctSameHouse85\",\n",
|
||||
"\"PctSameCity85\",\n",
|
||||
"\"PctSameState85\",\n",
|
||||
"\"LemasSwornFT\",\n",
|
||||
"\"LemasSwFTPerPop\",\n",
|
||||
"\"LemasSwFTFieldOps\",\n",
|
||||
"\"LemasSwFTFieldPerPop\",\n",
|
||||
"\"LemasTotalReq\",\n",
|
||||
"\"LemasTotReqPerPop\",\n",
|
||||
"\"PolicReqPerOffic\",\n",
|
||||
"\"PolicPerPop\",\n",
|
||||
"\"RacialMatchCommPol\",\n",
|
||||
"\"PctPolicWhite\",\n",
|
||||
"\"PctPolicBlack\",\n",
|
||||
"\"PctPolicHisp\",\n",
|
||||
"\"PctPolicAsian\",\n",
|
||||
"\"PctPolicMinor\",\n",
|
||||
"\"OfficAssgnDrugUnits\",\n",
|
||||
"\"NumKindsDrugsSeiz\",\n",
|
||||
"\"PolicAveOTWorked\",\n",
|
||||
"\"LandArea\",\n",
|
||||
"\"PopDens\",\n",
|
||||
"\"PctUsePubTrans\",\n",
|
||||
"\"PolicCars\",\n",
|
||||
"\"PolicOperBudg\",\n",
|
||||
"\"LemasPctPolicOnPatr\",\n",
|
||||
"\"LemasGangUnitDeploy\",\n",
|
||||
"\"LemasPctOfficDrugUn\",\n",
|
||||
"\"PolicBudgPerPop\",\n",
|
||||
"\"ViolentCrimesPerPop\"]\n",
|
||||
"\n",
|
||||
"df = pd.read_csv('communities.data',names=col_names)\n",
|
||||
"df = df.replace('?',None)\n",
|
||||
"df = df.dropna(axis='rows')\n",
|
||||
"\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"le.fit(df['communityname'].unique())\n",
|
||||
"df['communityname'] = le.transform(df['communityname'])\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"id": "9c0f6f6d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = df.loc[:,df.columns != 'ViolentCrimesPerPop']\n",
|
||||
"y = df['ViolentCrimesPerPop']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"id": "2bdc2e77",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"id": "2bbd2ddb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/tonywesoly/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py:251: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.1533542718655332"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ridgecv = RidgeCV(alphas=np.arange(1, 100, 5), scoring='r2', cv=10)\n",
|
||||
"ridgecv.fit(X, y)\n",
|
||||
"ridge = Ridge(alpha=ridgecv.alpha_)\n",
|
||||
"ridge.fit(X_train, y_train)\n",
|
||||
"ridge_y_predicted = ridge.predict(X_test)\n",
|
||||
"ridge_rmse = np.sqrt(mean_squared_error(y_test, ridge_y_predicted))\n",
|
||||
"ridge_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"id": "dbfe728b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#poly = PolynomialFeatures(degree=11, include_bias=False)\n",
|
||||
"#poly_features = poly.fit_transform(X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"id": "3be15622",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.6511181528162753"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"poly_reg_model = LinearRegression()\n",
|
||||
"poly_reg_model.fit(X_train,y_train)\n",
|
||||
"poly_reg_y_predicted = poly_reg_model.predict(X_test)\n",
|
||||
"poly_reg_rmse = np.sqrt(mean_squared_error(y_test, poly_reg_y_predicted))\n",
|
||||
"poly_reg_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"id": "4ab0949a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.3085495600528652"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
|
||||
"lin_reg_model = LinearRegression()\n",
|
||||
"lin_reg_model.fit(X_train, y_train)\n",
|
||||
"lin_reg_y_predicted = lin_reg_model.predict(X_test)\n",
|
||||
"lin_reg_rmse = np.sqrt(mean_squared_error(y_test, lin_reg_y_predicted))\n",
|
||||
"lin_reg_rmse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "99365180",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0e0b2f8e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
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|
@ -0,0 +1 @@
|
|||
Subproject commit 6fd4e1856052cb8ce29e81462e3ebef26e232e89
|
Loading…
Reference in New Issue