Add breast cancer data exploration

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Jake Walker 2024-02-22 16:59:37 +00:00 committed by Jake Walker
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commit df28f7edaf

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@ -10,11 +10,651 @@
"\n", "\n",
"From: <https://www.datacamp.com/tutorial/principal-component-analysis-in-python>" "From: <https://www.datacamp.com/tutorial/principal-component-analysis-in-python>"
] ]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Breast Cancer Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_breast_cancer\n",
"\n",
"breast = load_breast_cancer()\n",
"breast_data = breast.data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(569, 30)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"breast_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(569,)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"breast_labels = breast.target\n",
"breast_labels.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"labels = np.reshape(breast_labels, (569, 1))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(569, 31)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_breast_data = np.concatenate([breast_data, labels], axis=1)\n",
"final_breast_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"breast_dataset = pd.DataFrame(final_breast_data)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
" 'mean smoothness', 'mean compactness', 'mean concavity',\n",
" 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
" 'radius error', 'texture error', 'perimeter error', 'area error',\n",
" 'smoothness error', 'compactness error', 'concavity error',\n",
" 'concave points error', 'symmetry error',\n",
" 'fractal dimension error', 'worst radius', 'worst texture',\n",
" 'worst perimeter', 'worst area', 'worst smoothness',\n",
" 'worst compactness', 'worst concavity', 'worst concave points',\n",
" 'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = breast.feature_names\n",
"features"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"features_labels = np.append(features, \"label\")\n",
"breast_dataset.columns = features_labels"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
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" <td>0.4601</td>\n",
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" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.1238</td>\n",
" <td>0.1866</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
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" <td>0.2069</td>\n",
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" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.1444</td>\n",
" <td>0.4245</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.2414</td>\n",
" <td>0.10520</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>...</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.2098</td>\n",
" <td>0.8663</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
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" <th>4</th>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
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" <td>0.1374</td>\n",
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" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
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" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.3001 0.14710 0.2419 \n",
"1 0.07864 0.0869 0.07017 0.1812 \n",
"2 0.15990 0.1974 0.12790 0.2069 \n",
"3 0.28390 0.2414 0.10520 0.2597 \n",
"4 0.13280 0.1980 0.10430 0.1809 \n",
"\n",
" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
"0 0.07871 ... 17.33 184.60 2019.0 \n",
"1 0.05667 ... 23.41 158.80 1956.0 \n",
"2 0.05999 ... 25.53 152.50 1709.0 \n",
"3 0.09744 ... 26.50 98.87 567.7 \n",
"4 0.05883 ... 16.67 152.20 1575.0 \n",
"\n",
" worst smoothness worst compactness worst concavity worst concave points \\\n",
"0 0.1622 0.6656 0.7119 0.2654 \n",
"1 0.1238 0.1866 0.2416 0.1860 \n",
"2 0.1444 0.4245 0.4504 0.2430 \n",
"3 0.2098 0.8663 0.6869 0.2575 \n",
"4 0.1374 0.2050 0.4000 0.1625 \n",
"\n",
" worst symmetry worst fractal dimension label \n",
"0 0.4601 0.11890 0.0 \n",
"1 0.2750 0.08902 0.0 \n",
"2 0.3613 0.08758 0.0 \n",
"3 0.6638 0.17300 0.0 \n",
"4 0.2364 0.07678 0.0 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"breast_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\JakeWalker\\AppData\\Local\\Temp\\ipykernel_17096\\3450579118.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
"The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
"\n",
"For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
"\n",
"\n",
" breast_dataset[\"label\"].replace(0, \"Benign\", inplace=True)\n"
]
}
],
"source": [
"breast_dataset[\"label\"].replace(0, \"Benign\", inplace=True)\n",
"breast_dataset[\"label\"].replace(1, \"Malignant\", inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
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" <th>564</th>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
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" <td>0.4107</td>\n",
" <td>0.2216</td>\n",
" <td>0.2060</td>\n",
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" <td>Benign</td>\n",
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" <td>0.3215</td>\n",
" <td>0.1628</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" <td>Benign</td>\n",
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" <td>16.60</td>\n",
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" <td>108.30</td>\n",
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" <td>0.3403</td>\n",
" <td>0.1418</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" <td>Benign</td>\n",
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" <tr>\n",
" <th>567</th>\n",
" <td>20.60</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
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" <td>0.35140</td>\n",
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" <td>1821.0</td>\n",
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" <td>0.86810</td>\n",
" <td>0.9387</td>\n",
" <td>0.2650</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" <td>Benign</td>\n",
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" <td>7.76</td>\n",
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" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
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" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" <td>Malignant</td>\n",
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],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"564 21.56 22.39 142.00 1479.0 0.11100 \n",
"565 20.13 28.25 131.20 1261.0 0.09780 \n",
"566 16.60 28.08 108.30 858.1 0.08455 \n",
"567 20.60 29.33 140.10 1265.0 0.11780 \n",
"568 7.76 24.54 47.92 181.0 0.05263 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"564 0.11590 0.24390 0.13890 0.1726 \n",
"565 0.10340 0.14400 0.09791 0.1752 \n",
"566 0.10230 0.09251 0.05302 0.1590 \n",
"567 0.27700 0.35140 0.15200 0.2397 \n",
"568 0.04362 0.00000 0.00000 0.1587 \n",
"\n",
" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
"564 0.05623 ... 26.40 166.10 2027.0 \n",
"565 0.05533 ... 38.25 155.00 1731.0 \n",
"566 0.05648 ... 34.12 126.70 1124.0 \n",
"567 0.07016 ... 39.42 184.60 1821.0 \n",
"568 0.05884 ... 30.37 59.16 268.6 \n",
"\n",
" worst smoothness worst compactness worst concavity \\\n",
"564 0.14100 0.21130 0.4107 \n",
"565 0.11660 0.19220 0.3215 \n",
"566 0.11390 0.30940 0.3403 \n",
"567 0.16500 0.86810 0.9387 \n",
"568 0.08996 0.06444 0.0000 \n",
"\n",
" worst concave points worst symmetry worst fractal dimension label \n",
"564 0.2216 0.2060 0.07115 Benign \n",
"565 0.1628 0.2572 0.06637 Benign \n",
"566 0.1418 0.2218 0.07820 Benign \n",
"567 0.2650 0.4087 0.12400 Benign \n",
"568 0.0000 0.2871 0.07039 Malignant \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"breast_dataset.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CIFAR-10 Data Exploration"
]
} }
], ],
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