forked from s464914/ium_464914
1259 lines
449 KiB
Plaintext
1259 lines
449 KiB
Plaintext
{
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{
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"text": [
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"Requirement already satisfied: kaggle in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (1.6.6)\n",
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"Requirement already satisfied: six>=1.10 in c:\\software\\python3\\lib\\site-packages (from kaggle) (1.16.0)\n",
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"Requirement already satisfied: certifi in c:\\software\\python3\\lib\\site-packages (from kaggle) (2023.7.22)\n",
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"Requirement already satisfied: python-dateutil in c:\\software\\python3\\lib\\site-packages (from kaggle) (2.8.2)\n",
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"Requirement already satisfied: tqdm in c:\\software\\python3\\lib\\site-packages (from kaggle) (4.66.1)\n",
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"Requirement already satisfied: python-slugify in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from kaggle) (8.0.4)\n",
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"Requirement already satisfied: urllib3 in c:\\software\\python3\\lib\\site-packages (from kaggle) (1.26.16)\n",
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"Requirement already satisfied: bleach in c:\\software\\python3\\lib\\site-packages (from kaggle) (6.0.0)\n",
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"Requirement already satisfied: webencodings in c:\\software\\python3\\lib\\site-packages (from bleach->kaggle) (0.5.1)\n",
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"Requirement already satisfied: text-unidecode>=1.3 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from python-slugify->kaggle) (1.3)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in c:\\software\\python3\\lib\\site-packages (from requests->kaggle) (3.2.0)\n",
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"Requirement already satisfied: idna<4,>=2.5 in c:\\software\\python3\\lib\\site-packages (from requests->kaggle) (3.4)\n",
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"Requirement already satisfied: colorama in c:\\software\\python3\\lib\\site-packages (from tqdm->kaggle) (0.4.6)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
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"[notice] To update, run: python3.exe -m pip install --upgrade pip\n"
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]
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},
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{
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"name": "stdout",
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"text": [
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"Requirement already satisfied: pandas in c:\\software\\python3\\lib\\site-packages (2.0.3)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\software\\python3\\lib\\site-packages (from pandas) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in c:\\software\\python3\\lib\\site-packages (from pandas) (2023.3)\n",
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"Requirement already satisfied: tzdata>=2022.1 in c:\\software\\python3\\lib\\site-packages (from pandas) (2023.3)\n",
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"Requirement already satisfied: numpy>=1.21.0 in c:\\software\\python3\\lib\\site-packages (from pandas) (1.24.3)\n",
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"Requirement already satisfied: six>=1.5 in c:\\software\\python3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
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"[notice] To update, run: python3.exe -m pip install --upgrade pip\n"
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]
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},
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{
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"name": "stdout",
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"text": [
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"Requirement already satisfied: scikit-learn in c:\\software\\python3\\lib\\site-packages (1.3.0)\n",
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"Requirement already satisfied: numpy>=1.17.3 in c:\\software\\python3\\lib\\site-packages (from scikit-learn) (1.24.3)\n",
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"Requirement already satisfied: scipy>=1.5.0 in c:\\software\\python3\\lib\\site-packages (from scikit-learn) (1.11.2)\n",
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"Requirement already satisfied: joblib>=1.1.1 in c:\\software\\python3\\lib\\site-packages (from scikit-learn) (1.3.2)\n",
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"Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\software\\python3\\lib\\site-packages (from scikit-learn) (3.2.0)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
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"[notice] To update, run: python3.exe -m pip install --upgrade pip\n"
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]
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},
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"name": "stdout",
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"Requirement already satisfied: matplotlib in c:\\software\\python3\\lib\\site-packages (3.7.2)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (1.1.0)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
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"Requirement already satisfied: numpy>=1.20 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (1.24.3)\n",
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"Requirement already satisfied: packaging>=20.0 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (23.1)\n",
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"Requirement already satisfied: pillow>=6.2.0 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (10.0.0)\n",
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"Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in c:\\software\\python3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
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"Requirement already satisfied: six>=1.5 in c:\\software\\python3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
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"[notice] To update, run: python3.exe -m pip install --upgrade pip\n"
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]
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},
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{
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"name": "stdout",
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"Requirement already satisfied: geopandas in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (0.14.3)\n",
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"Requirement already satisfied: fiona>=1.8.21 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from geopandas) (1.9.6)\n",
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"Requirement already satisfied: packaging in c:\\software\\python3\\lib\\site-packages (from geopandas) (23.1)\n",
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"Requirement already satisfied: pandas>=1.4.0 in c:\\software\\python3\\lib\\site-packages (from geopandas) (2.0.3)\n",
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"Requirement already satisfied: pyproj>=3.3.0 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from geopandas) (3.6.1)\n",
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"Requirement already satisfied: shapely>=1.8.0 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from geopandas) (2.0.3)\n",
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"Requirement already satisfied: attrs>=19.2.0 in c:\\software\\python3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (23.1.0)\n",
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"Requirement already satisfied: certifi in c:\\software\\python3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (2023.7.22)\n",
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"Requirement already satisfied: click~=8.0 in c:\\software\\python3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (8.1.7)\n",
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"Requirement already satisfied: click-plugins>=1.0 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from fiona>=1.8.21->geopandas) (1.1.1)\n",
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"Requirement already satisfied: cligj>=0.5 in \\\\files\\students\\s464914\\.appdata\\python\\python310\\site-packages (from fiona>=1.8.21->geopandas) (0.7.2)\n",
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"Requirement already satisfied: six in c:\\software\\python3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (1.16.0)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\software\\python3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in c:\\software\\python3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n",
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"Requirement already satisfied: tzdata>=2022.1 in c:\\software\\python3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n",
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"Requirement already satisfied: numpy>=1.21.0 in c:\\software\\python3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (1.24.3)\n",
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"Requirement already satisfied: colorama in c:\\software\\python3\\lib\\site-packages (from click~=8.0->fiona>=1.8.21->geopandas) (0.4.6)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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"[notice] A new release of pip is available: 23.0.1 -> 24.0\n",
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||
"[notice] To update, run: python3.exe -m pip install --upgrade pip\n"
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||
]
|
||
}
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||
],
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"source": [
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||
"%pip install --user kaggle \n",
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||
"%pip install --user pandas\n",
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||
"%pip install --user scikit-learn\n",
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||
"%pip install --user matplotlib\n",
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||
"%pip install --user geopandas"
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||
]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"end_time": "2024-03-17T17:38:40.019389600Z",
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"start_time": "2024-03-17T17:38:36.535384600Z"
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}
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||
},
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||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt \n",
|
||
"import pandas as pd"
|
||
]
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||
},
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||
{
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||
"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"pycharm": {
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"is_executing": true
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},
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"ExecuteTime": {
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"end_time": "2024-04-13T16:11:03.249257400Z",
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"start_time": "2024-04-13T16:10:58.745694100Z"
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}
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"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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||
"Downloading forest-cover-type-dataset.zip to J:\\PycharmProjects\\ium_464914\n"
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||
]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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" 0%| | 0.00/11.2M [00:00<?, ?B/s]\n",
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" 9%|8 | 1.00M/11.2M [00:00<00:06, 1.56MB/s]\n",
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" 18%|#7 | 2.00M/11.2M [00:00<00:03, 3.10MB/s]\n",
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" 36%|###5 | 4.00M/11.2M [00:00<00:01, 6.25MB/s]\n",
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" 54%|#####3 | 6.00M/11.2M [00:01<00:00, 9.19MB/s]\n",
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" 81%|######## | 9.00M/11.2M [00:01<00:00, 13.0MB/s]\n",
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"100%|##########| 11.2M/11.2M [00:01<00:00, 9.30MB/s]\n"
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]
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}
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],
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"source": [
|
||
"!kaggle datasets download -d uciml/forest-cover-type-dataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 5,
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2024-04-13T16:11:41.214712500Z",
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"start_time": "2024-04-13T16:11:37.462860300Z"
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||
}
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||
},
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||
"outputs": [
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||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"Archive: forest-cover-type-dataset.zip\n",
|
||
" inflating: covtype.csv \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!unzip -o forest-cover-type-dataset.zip "
|
||
]
|
||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"<h4>Zbiór</h4>"
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]
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||
},
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||
{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"ExecuteTime": {
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" <th>Soil_Type39</th>\n",
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" <th>Soil_Type40</th>\n",
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" <th>Cover_Type</th>\n",
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|
||
" <td>738</td>\n",
|
||
" <td>177</td>\n",
|
||
" <td>216</td>\n",
|
||
" <td>178</td>\n",
|
||
" <td>510</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131114</th>\n",
|
||
" <td>2962</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>95</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>4270</td>\n",
|
||
" <td>202</td>\n",
|
||
" <td>214</td>\n",
|
||
" <td>148</td>\n",
|
||
" <td>1999</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>385769</th>\n",
|
||
" <td>3181</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>170</td>\n",
|
||
" <td>-1</td>\n",
|
||
" <td>2416</td>\n",
|
||
" <td>228</td>\n",
|
||
" <td>235</td>\n",
|
||
" <td>141</td>\n",
|
||
" <td>999</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>161626</th>\n",
|
||
" <td>2950</td>\n",
|
||
" <td>270</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>108</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>2053</td>\n",
|
||
" <td>210</td>\n",
|
||
" <td>241</td>\n",
|
||
" <td>170</td>\n",
|
||
" <td>2037</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>394880</th>\n",
|
||
" <td>3051</td>\n",
|
||
" <td>155</td>\n",
|
||
" <td>22</td>\n",
|
||
" <td>390</td>\n",
|
||
" <td>70</td>\n",
|
||
" <td>1871</td>\n",
|
||
" <td>239</td>\n",
|
||
" <td>236</td>\n",
|
||
" <td>114</td>\n",
|
||
" <td>1510</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>389492</th>\n",
|
||
" <td>3024</td>\n",
|
||
" <td>191</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>785</td>\n",
|
||
" <td>110</td>\n",
|
||
" <td>3000</td>\n",
|
||
" <td>218</td>\n",
|
||
" <td>251</td>\n",
|
||
" <td>162</td>\n",
|
||
" <td>1961</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>52507</th>\n",
|
||
" <td>2714</td>\n",
|
||
" <td>349</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>1599</td>\n",
|
||
" <td>184</td>\n",
|
||
" <td>207</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>3234</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>10 rows × 55 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Elevation Aspect Slope Horizontal_Distance_To_Hydrology \\\n",
|
||
"318054 2517 271 12 272 \n",
|
||
"30504 2959 0 1 180 \n",
|
||
"349520 3093 54 19 42 \n",
|
||
"365645 2502 330 17 150 \n",
|
||
"131114 2962 4 13 95 \n",
|
||
"385769 3181 119 5 170 \n",
|
||
"161626 2950 270 4 108 \n",
|
||
"394880 3051 155 22 390 \n",
|
||
"389492 3024 191 16 785 \n",
|
||
"52507 2714 349 18 67 \n",
|
||
"\n",
|
||
" Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways \\\n",
|
||
"318054 84 484 \n",
|
||
"30504 20 5960 \n",
|
||
"349520 -3 797 \n",
|
||
"365645 52 738 \n",
|
||
"131114 7 4270 \n",
|
||
"385769 -1 2416 \n",
|
||
"161626 15 2053 \n",
|
||
"394880 70 1871 \n",
|
||
"389492 110 3000 \n",
|
||
"52507 20 1599 \n",
|
||
"\n",
|
||
" Hillshade_9am Hillshade_Noon Hillshade_3pm \\\n",
|
||
"318054 189 244 193 \n",
|
||
"30504 217 236 156 \n",
|
||
"349520 227 196 94 \n",
|
||
"365645 177 216 178 \n",
|
||
"131114 202 214 148 \n",
|
||
"385769 228 235 141 \n",
|
||
"161626 210 241 170 \n",
|
||
"394880 239 236 114 \n",
|
||
"389492 218 251 162 \n",
|
||
"52507 184 207 160 \n",
|
||
"\n",
|
||
" Horizontal_Distance_To_Fire_Points ... Soil_Type32 Soil_Type33 \\\n",
|
||
"318054 162 ... 0 0 \n",
|
||
"30504 3960 ... 0 0 \n",
|
||
"349520 1318 ... 0 0 \n",
|
||
"365645 510 ... 0 0 \n",
|
||
"131114 1999 ... 0 0 \n",
|
||
"385769 999 ... 0 0 \n",
|
||
"161626 2037 ... 0 0 \n",
|
||
"394880 1510 ... 0 0 \n",
|
||
"389492 1961 ... 0 1 \n",
|
||
"52507 3234 ... 0 0 \n",
|
||
"\n",
|
||
" Soil_Type34 Soil_Type35 Soil_Type36 Soil_Type37 Soil_Type38 \\\n",
|
||
"318054 0 0 0 0 0 \n",
|
||
"30504 0 0 0 0 0 \n",
|
||
"349520 0 0 0 0 0 \n",
|
||
"365645 0 0 0 0 0 \n",
|
||
"131114 0 0 0 0 0 \n",
|
||
"385769 0 0 0 0 0 \n",
|
||
"161626 0 0 0 0 0 \n",
|
||
"394880 0 0 0 0 0 \n",
|
||
"389492 0 0 0 0 0 \n",
|
||
"52507 0 0 0 0 0 \n",
|
||
"\n",
|
||
" Soil_Type39 Soil_Type40 Cover_Type \n",
|
||
"318054 0 0 2 \n",
|
||
"30504 0 0 2 \n",
|
||
"349520 0 0 1 \n",
|
||
"365645 0 0 6 \n",
|
||
"131114 0 0 2 \n",
|
||
"385769 0 0 1 \n",
|
||
"161626 0 0 2 \n",
|
||
"394880 0 0 1 \n",
|
||
"389492 0 0 2 \n",
|
||
"52507 0 0 2 \n",
|
||
"\n",
|
||
"[10 rows x 55 columns]"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data = pd.read_csv(\"covtype.csv\")\n",
|
||
"data = data.sample(frac = 1)\n",
|
||
"data.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Podział na podzbiory"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-17T17:38:53.929532900Z",
|
||
"start_time": "2024-03-17T17:38:51.607851900Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"forest_train, forest_test = train_test_split(data, test_size=0.2, random_state=1)\n",
|
||
"forest_train, forest_val = train_test_split(forest_train, test_size=0.25, random_state=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<h4>Statystyki</h4>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wielkości zbiorów"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-17T17:38:58.202546800Z",
|
||
"start_time": "2024-03-17T17:38:58.143643600Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"wielkość zbioru: (581012, 55)\n",
|
||
"wielkość zbioru treningowego: (348606, 55)\n",
|
||
"wielkość zbioru testującego: (116203, 55)\n",
|
||
"wielkość zbioru walidacyjnego: (116203, 55)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f'wielkość zbioru: {data.shape}')\n",
|
||
"print(f'wielkość zbioru treningowego: {forest_train.shape}')\n",
|
||
"print(f'wielkość zbioru testującego: {forest_test.shape}')\n",
|
||
"print(f'wielkość zbioru walidacyjnego: {forest_val.shape}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-17T17:39:01.338802400Z",
|
||
"start_time": "2024-03-17T17:39:01.252677700Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 581012 entries, 0 to 581011\n",
|
||
"Data columns (total 55 columns):\n",
|
||
" # Column Non-Null Count Dtype\n",
|
||
"--- ------ -------------- -----\n",
|
||
" 0 Elevation 581012 non-null int64\n",
|
||
" 1 Aspect 581012 non-null int64\n",
|
||
" 2 Slope 581012 non-null int64\n",
|
||
" 3 Horizontal_Distance_To_Hydrology 581012 non-null int64\n",
|
||
" 4 Vertical_Distance_To_Hydrology 581012 non-null int64\n",
|
||
" 5 Horizontal_Distance_To_Roadways 581012 non-null int64\n",
|
||
" 6 Hillshade_9am 581012 non-null int64\n",
|
||
" 7 Hillshade_Noon 581012 non-null int64\n",
|
||
" 8 Hillshade_3pm 581012 non-null int64\n",
|
||
" 9 Horizontal_Distance_To_Fire_Points 581012 non-null int64\n",
|
||
" 10 Wilderness_Area1 581012 non-null int64\n",
|
||
" 11 Wilderness_Area2 581012 non-null int64\n",
|
||
" 12 Wilderness_Area3 581012 non-null int64\n",
|
||
" 13 Wilderness_Area4 581012 non-null int64\n",
|
||
" 14 Soil_Type1 581012 non-null int64\n",
|
||
" 15 Soil_Type2 581012 non-null int64\n",
|
||
" 16 Soil_Type3 581012 non-null int64\n",
|
||
" 17 Soil_Type4 581012 non-null int64\n",
|
||
" 18 Soil_Type5 581012 non-null int64\n",
|
||
" 19 Soil_Type6 581012 non-null int64\n",
|
||
" 20 Soil_Type7 581012 non-null int64\n",
|
||
" 21 Soil_Type8 581012 non-null int64\n",
|
||
" 22 Soil_Type9 581012 non-null int64\n",
|
||
" 23 Soil_Type10 581012 non-null int64\n",
|
||
" 24 Soil_Type11 581012 non-null int64\n",
|
||
" 25 Soil_Type12 581012 non-null int64\n",
|
||
" 26 Soil_Type13 581012 non-null int64\n",
|
||
" 27 Soil_Type14 581012 non-null int64\n",
|
||
" 28 Soil_Type15 581012 non-null int64\n",
|
||
" 29 Soil_Type16 581012 non-null int64\n",
|
||
" 30 Soil_Type17 581012 non-null int64\n",
|
||
" 31 Soil_Type18 581012 non-null int64\n",
|
||
" 32 Soil_Type19 581012 non-null int64\n",
|
||
" 33 Soil_Type20 581012 non-null int64\n",
|
||
" 34 Soil_Type21 581012 non-null int64\n",
|
||
" 35 Soil_Type22 581012 non-null int64\n",
|
||
" 36 Soil_Type23 581012 non-null int64\n",
|
||
" 37 Soil_Type24 581012 non-null int64\n",
|
||
" 38 Soil_Type25 581012 non-null int64\n",
|
||
" 39 Soil_Type26 581012 non-null int64\n",
|
||
" 40 Soil_Type27 581012 non-null int64\n",
|
||
" 41 Soil_Type28 581012 non-null int64\n",
|
||
" 42 Soil_Type29 581012 non-null int64\n",
|
||
" 43 Soil_Type30 581012 non-null int64\n",
|
||
" 44 Soil_Type31 581012 non-null int64\n",
|
||
" 45 Soil_Type32 581012 non-null int64\n",
|
||
" 46 Soil_Type33 581012 non-null int64\n",
|
||
" 47 Soil_Type34 581012 non-null int64\n",
|
||
" 48 Soil_Type35 581012 non-null int64\n",
|
||
" 49 Soil_Type36 581012 non-null int64\n",
|
||
" 50 Soil_Type37 581012 non-null int64\n",
|
||
" 51 Soil_Type38 581012 non-null int64\n",
|
||
" 52 Soil_Type39 581012 non-null int64\n",
|
||
" 53 Soil_Type40 581012 non-null int64\n",
|
||
" 54 Cover_Type 581012 non-null int64\n",
|
||
"dtypes: int64(55)\n",
|
||
"memory usage: 243.8 MB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Nachylenie\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-17T17:39:04.561664800Z",
|
||
"start_time": "2024-03-17T17:39:04.530325200Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Średnie nachylenie: 14.103703537964792\n",
|
||
"Maksymalne nachylenie: 66\n",
|
||
"Minimalne nachylenie: 0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f'Średnie nachylenie: {data[\"Slope\"].mean()}')\n",
|
||
"print(f'Maksymalne nachylenie: {data[\"Slope\"].max()}')\n",
|
||
"print(f'Minimalne nachylenie: {data[\"Slope\"].min()}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 3000x5000 with 10 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"features = data.loc[:,'Elevation':'Horizontal_Distance_To_Fire_Points']\n",
|
||
"\n",
|
||
"plt.figure(figsize=(30, 50))\n",
|
||
"for i,col in enumerate(features.columns.values):\n",
|
||
" plt.subplot(5,2,i+1)\n",
|
||
" sns.boxplot(x=data['Cover_Type'], y=col, data=data)\n",
|
||
" plt.title(col, fontsize=20)\n",
|
||
" \n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Normalizacja"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"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>Elevation</th>\n",
|
||
" <th>Aspect</th>\n",
|
||
" <th>Slope</th>\n",
|
||
" <th>Horizontal_Distance_To_Hydrology</th>\n",
|
||
" <th>Vertical_Distance_To_Hydrology</th>\n",
|
||
" <th>Horizontal_Distance_To_Roadways</th>\n",
|
||
" <th>Hillshade_9am</th>\n",
|
||
" <th>Hillshade_Noon</th>\n",
|
||
" <th>Hillshade_3pm</th>\n",
|
||
" <th>Horizontal_Distance_To_Fire_Points</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>Soil_Type32</th>\n",
|
||
" <th>Soil_Type33</th>\n",
|
||
" <th>Soil_Type34</th>\n",
|
||
" <th>Soil_Type35</th>\n",
|
||
" <th>Soil_Type36</th>\n",
|
||
" <th>Soil_Type37</th>\n",
|
||
" <th>Soil_Type38</th>\n",
|
||
" <th>Soil_Type39</th>\n",
|
||
" <th>Soil_Type40</th>\n",
|
||
" <th>Cover_Type</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>318054</th>\n",
|
||
" <td>-1.579964</td>\n",
|
||
" <td>1.030645</td>\n",
|
||
" <td>-0.280934</td>\n",
|
||
" <td>0.012100</td>\n",
|
||
" <td>0.644670</td>\n",
|
||
" <td>-1.196821</td>\n",
|
||
" <td>-0.864631</td>\n",
|
||
" <td>1.046164</td>\n",
|
||
" <td>1.318678</td>\n",
|
||
" <td>-1.373130</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>30504</th>\n",
|
||
" <td>-0.001305</td>\n",
|
||
" <td>-1.390866</td>\n",
|
||
" <td>-1.749905</td>\n",
|
||
" <td>-0.420741</td>\n",
|
||
" <td>-0.453191</td>\n",
|
||
" <td>2.315116</td>\n",
|
||
" <td>0.181321</td>\n",
|
||
" <td>0.641484</td>\n",
|
||
" <td>0.351977</td>\n",
|
||
" <td>1.495029</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>349520</th>\n",
|
||
" <td>0.477293</td>\n",
|
||
" <td>-0.908351</td>\n",
|
||
" <td>0.653865</td>\n",
|
||
" <td>-1.070003</td>\n",
|
||
" <td>-0.847735</td>\n",
|
||
" <td>-0.996083</td>\n",
|
||
" <td>0.554876</td>\n",
|
||
" <td>-1.381919</td>\n",
|
||
" <td>-1.267901</td>\n",
|
||
" <td>-0.500147</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>365645</th>\n",
|
||
" <td>-1.633538</td>\n",
|
||
" <td>1.557837</td>\n",
|
||
" <td>0.386780</td>\n",
|
||
" <td>-0.561885</td>\n",
|
||
" <td>0.095739</td>\n",
|
||
" <td>-1.033922</td>\n",
|
||
" <td>-1.312896</td>\n",
|
||
" <td>-0.370218</td>\n",
|
||
" <td>0.926772</td>\n",
|
||
" <td>-1.110329</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131114</th>\n",
|
||
" <td>0.009410</td>\n",
|
||
" <td>-1.355124</td>\n",
|
||
" <td>-0.147392</td>\n",
|
||
" <td>-0.820649</td>\n",
|
||
" <td>-0.676194</td>\n",
|
||
" <td>1.231264</td>\n",
|
||
" <td>-0.379010</td>\n",
|
||
" <td>-0.471388</td>\n",
|
||
" <td>0.142960</td>\n",
|
||
" <td>0.014128</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>385769</th>\n",
|
||
" <td>0.791596</td>\n",
|
||
" <td>-0.327546</td>\n",
|
||
" <td>-1.215734</td>\n",
|
||
" <td>-0.467789</td>\n",
|
||
" <td>-0.813427</td>\n",
|
||
" <td>0.042234</td>\n",
|
||
" <td>0.592231</td>\n",
|
||
" <td>0.590899</td>\n",
|
||
" <td>-0.039929</td>\n",
|
||
" <td>-0.741048</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>161626</th>\n",
|
||
" <td>-0.033449</td>\n",
|
||
" <td>1.021709</td>\n",
|
||
" <td>-1.349277</td>\n",
|
||
" <td>-0.759486</td>\n",
|
||
" <td>-0.538961</td>\n",
|
||
" <td>-0.190570</td>\n",
|
||
" <td>-0.080167</td>\n",
|
||
" <td>0.894409</td>\n",
|
||
" <td>0.717756</td>\n",
|
||
" <td>0.042825</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>394880</th>\n",
|
||
" <td>0.327285</td>\n",
|
||
" <td>-0.005869</td>\n",
|
||
" <td>1.054494</td>\n",
|
||
" <td>0.567265</td>\n",
|
||
" <td>0.404513</td>\n",
|
||
" <td>-0.307292</td>\n",
|
||
" <td>1.003141</td>\n",
|
||
" <td>0.641484</td>\n",
|
||
" <td>-0.745360</td>\n",
|
||
" <td>-0.355153</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>389492</th>\n",
|
||
" <td>0.230851</td>\n",
|
||
" <td>0.315808</td>\n",
|
||
" <td>0.253237</td>\n",
|
||
" <td>2.425659</td>\n",
|
||
" <td>1.090676</td>\n",
|
||
" <td>0.416772</td>\n",
|
||
" <td>0.218677</td>\n",
|
||
" <td>1.400260</td>\n",
|
||
" <td>0.508739</td>\n",
|
||
" <td>-0.014568</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>52507</th>\n",
|
||
" <td>-0.876353</td>\n",
|
||
" <td>1.727611</td>\n",
|
||
" <td>0.520322</td>\n",
|
||
" <td>-0.952383</td>\n",
|
||
" <td>-0.453191</td>\n",
|
||
" <td>-0.481735</td>\n",
|
||
" <td>-1.051408</td>\n",
|
||
" <td>-0.825483</td>\n",
|
||
" <td>0.456485</td>\n",
|
||
" <td>0.946771</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>10 rows × 55 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Elevation Aspect Slope Horizontal_Distance_To_Hydrology \\\n",
|
||
"318054 -1.579964 1.030645 -0.280934 0.012100 \n",
|
||
"30504 -0.001305 -1.390866 -1.749905 -0.420741 \n",
|
||
"349520 0.477293 -0.908351 0.653865 -1.070003 \n",
|
||
"365645 -1.633538 1.557837 0.386780 -0.561885 \n",
|
||
"131114 0.009410 -1.355124 -0.147392 -0.820649 \n",
|
||
"385769 0.791596 -0.327546 -1.215734 -0.467789 \n",
|
||
"161626 -0.033449 1.021709 -1.349277 -0.759486 \n",
|
||
"394880 0.327285 -0.005869 1.054494 0.567265 \n",
|
||
"389492 0.230851 0.315808 0.253237 2.425659 \n",
|
||
"52507 -0.876353 1.727611 0.520322 -0.952383 \n",
|
||
"\n",
|
||
" Vertical_Distance_To_Hydrology Horizontal_Distance_To_Roadways \\\n",
|
||
"318054 0.644670 -1.196821 \n",
|
||
"30504 -0.453191 2.315116 \n",
|
||
"349520 -0.847735 -0.996083 \n",
|
||
"365645 0.095739 -1.033922 \n",
|
||
"131114 -0.676194 1.231264 \n",
|
||
"385769 -0.813427 0.042234 \n",
|
||
"161626 -0.538961 -0.190570 \n",
|
||
"394880 0.404513 -0.307292 \n",
|
||
"389492 1.090676 0.416772 \n",
|
||
"52507 -0.453191 -0.481735 \n",
|
||
"\n",
|
||
" Hillshade_9am Hillshade_Noon Hillshade_3pm \\\n",
|
||
"318054 -0.864631 1.046164 1.318678 \n",
|
||
"30504 0.181321 0.641484 0.351977 \n",
|
||
"349520 0.554876 -1.381919 -1.267901 \n",
|
||
"365645 -1.312896 -0.370218 0.926772 \n",
|
||
"131114 -0.379010 -0.471388 0.142960 \n",
|
||
"385769 0.592231 0.590899 -0.039929 \n",
|
||
"161626 -0.080167 0.894409 0.717756 \n",
|
||
"394880 1.003141 0.641484 -0.745360 \n",
|
||
"389492 0.218677 1.400260 0.508739 \n",
|
||
"52507 -1.051408 -0.825483 0.456485 \n",
|
||
"\n",
|
||
" Horizontal_Distance_To_Fire_Points ... Soil_Type32 Soil_Type33 \\\n",
|
||
"318054 -1.373130 ... 0 0 \n",
|
||
"30504 1.495029 ... 0 0 \n",
|
||
"349520 -0.500147 ... 0 0 \n",
|
||
"365645 -1.110329 ... 0 0 \n",
|
||
"131114 0.014128 ... 0 0 \n",
|
||
"385769 -0.741048 ... 0 0 \n",
|
||
"161626 0.042825 ... 0 0 \n",
|
||
"394880 -0.355153 ... 0 0 \n",
|
||
"389492 -0.014568 ... 0 1 \n",
|
||
"52507 0.946771 ... 0 0 \n",
|
||
"\n",
|
||
" Soil_Type34 Soil_Type35 Soil_Type36 Soil_Type37 Soil_Type38 \\\n",
|
||
"318054 0 0 0 0 0 \n",
|
||
"30504 0 0 0 0 0 \n",
|
||
"349520 0 0 0 0 0 \n",
|
||
"365645 0 0 0 0 0 \n",
|
||
"131114 0 0 0 0 0 \n",
|
||
"385769 0 0 0 0 0 \n",
|
||
"161626 0 0 0 0 0 \n",
|
||
"394880 0 0 0 0 0 \n",
|
||
"389492 0 0 0 0 0 \n",
|
||
"52507 0 0 0 0 0 \n",
|
||
"\n",
|
||
" Soil_Type39 Soil_Type40 Cover_Type \n",
|
||
"318054 0 0 2 \n",
|
||
"30504 0 0 2 \n",
|
||
"349520 0 0 1 \n",
|
||
"365645 0 0 6 \n",
|
||
"131114 0 0 2 \n",
|
||
"385769 0 0 1 \n",
|
||
"161626 0 0 2 \n",
|
||
"394880 0 0 1 \n",
|
||
"389492 0 0 2 \n",
|
||
"52507 0 0 2 \n",
|
||
"\n",
|
||
"[10 rows x 55 columns]"
|
||
]
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"\n",
|
||
"columns_to_normalize = data.columns[~data.columns.str.startswith('Soil_Type')]\n",
|
||
"columns_to_normalize = columns_to_normalize.to_list()\n",
|
||
"columns_to_normalize.remove('Cover_Type')\n",
|
||
"data[columns_to_normalize] = scaler.fit_transform(data[columns_to_normalize])\n",
|
||
"\n",
|
||
"data.head(10)"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.11.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|