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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "566dfd4d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:50:45.380322Z",
"iopub.status.busy": "2023-02-25T19:50:45.379882Z",
"iopub.status.idle": "2023-02-25T19:50:46.832918Z",
"shell.execute_reply": "2023-02-25T19:50:46.831497Z"
},
"papermill": {
"duration": 1.462794,
"end_time": "2023-02-25T19:50:46.835945",
"exception": false,
"start_time": "2023-02-25T19:50:45.373151",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn import preprocessing \n",
"le=preprocessing.LabelEncoder()\n",
"one=preprocessing.OneHotEncoder()\n",
"local_encoder=preprocessing.LabelEncoder()\n",
"ordinal=preprocessing.OrdinalEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bb5007ec",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:50:46.846836Z",
"iopub.status.busy": "2023-02-25T19:50:46.845515Z",
"iopub.status.idle": "2023-02-25T19:50:46.964134Z",
"shell.execute_reply": "2023-02-25T19:50:46.962244Z"
},
"papermill": {
"duration": 0.126092,
"end_time": "2023-02-25T19:50:46.966229",
"exception": true,
"start_time": "2023-02-25T19:50:46.840137",
"status": "failed"
},
"tags": []
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'insurance.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_19/4081594082.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'insurance.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbmi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m )\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m )\n\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'insurance.csv'"
]
}
],
"source": [
"ds=pd.read_csv('insurance.csv')\n",
"ds.bmi.T"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbe88a6f",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f22cc392",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8686688d",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='age')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70a05ede",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='bmi')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adb90fe8",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.drop(ds[ds.bmi>47].index,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b281046d",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='bmi')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2f6c89b",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='children')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "381b98c7",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sns.histplot(ds.charges,bins=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "339bf6d7",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"#we see what is the change if we scale the data\n",
"price_log=np.log1p(ds.charges)\n",
"price_log\n",
"ds.charges=price_log\n",
"sns.histplot(price_log,bins=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb54a9eb",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"#the data aftr scalling \n",
"sns.boxplot(data=ds,x='charges')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e629e618",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.region.unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08a5f3fb",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"#ds.region=le.fit_transform(ds.region)\n",
"#ds.region.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4141571c",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"f=local_encoder.fit_transform(ds.region)\n",
"ds.region=local_encoder.fit_transform(ds.region)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f822bfff",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.sex=local_encoder.fit_transform(ds.sex)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23c4ada2",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ds.smoker=le.fit_transform(ds.smoker)\n",
"ds"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
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"papermill": {
"default_parameters": {},
"duration": 13.295462,
"end_time": "2023-02-25T19:50:47.695164",
"environment_variables": {},
"exception": true,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
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}
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"nbformat": 4,
"nbformat_minor": 5
}
| 0120/312/120312926.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2d4b3c22",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:50:56.535357Z",
"iopub.status.busy": "2023-02-25T19:50:56.534694Z",
"iopub.status.idle": "2023-02-25T19:50:57.882478Z",
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"start_time": "2023-02-25T19:50:56.525950",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn import preprocessing \n",
"le=preprocessing.LabelEncoder()\n",
"one=preprocessing.OneHotEncoder()\n",
"local_encoder=preprocessing.LabelEncoder()\n",
"ordinal=preprocessing.OrdinalEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6a53065b",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:50:57.897855Z",
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},
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"end_time": "2023-02-25T19:50:58.022891",
"exception": true,
"start_time": "2023-02-25T19:50:57.890529",
"status": "failed"
},
"tags": []
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'BIKE DETAILS.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_19/1757130367.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'BIKE DETAILS.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m )\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 708\u001b[0m )\n\u001b[1;32m 709\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'BIKE DETAILS.csv'"
]
}
],
"source": [
"ds=pd.read_csv('BIKE DETAILS.csv')\n",
"ds"
]
},
{
"cell_type": "code",
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"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
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"source": [
"ds.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca9f3475",
"metadata": {
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"duration": null,
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},
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"source": [
"ds.ex_showroom_price.fillna(0,inplace=True)\n",
"ds.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb05ba65",
"metadata": {
"papermill": {
"duration": null,
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"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='selling_price')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b11a947c",
"metadata": {
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"duration": null,
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},
"tags": []
},
"outputs": [],
"source": [
"sns.histplot(ds.selling_price)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a293dd85",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"outputs": [],
"source": [
"d=np.log1p(ds.selling_price)\n",
"sns.histplot(d)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ece3d87a",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.selling_price=np.log1p(ds.selling_price)\n",
"sns.histplot(ds.selling_price)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82d2354e",
"metadata": {
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},
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"source": [
"sns.boxplot(data=ds,x='selling_price')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5167f50d",
"metadata": {
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"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b34068ba",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"sns.displot(data=ds,x='year')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "191e4ff7",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='km_driven')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebceda50",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.km_driven=np.log1p(ds.km_driven)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0f27427",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
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},
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"source": [
"sns.boxplot(data=ds,x='km_driven')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "809d36c8",
"metadata": {
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"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.drop(ds[ds.km_driven>7.5].index,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76fa8cc5",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='km_driven')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86633e14",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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"start_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='ex_showroom_price')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13408cb9",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.ex_showroom_price=np.log1p(ds.ex_showroom_price)\n",
"ds.ex_showroom_price"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6eef0289",
"metadata": {
"papermill": {
"duration": null,
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},
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},
"outputs": [],
"source": [
"sns.boxplot(data=ds,x='ex_showroom_price')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e911637a",
"metadata": {
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"duration": null,
"end_time": null,
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},
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"source": [
"ds.dtypes"
]
},
{
"cell_type": "markdown",
"id": "78878af8",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"source": [
"the categorical types has an one type of classes then i thank thats don`t need to encode"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17e8da62",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.seller_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7e4f817",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"ds.owner.unique()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
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| 0120/312/120312950.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
{
"cells": [
{
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"id": "0cba94af",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"dfFinal = pd.DataFrame()\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" filepath = os.path.join(dirname, filename)\n",
" dfnew = pd.read_csv(filepath)\n",
" \n",
" #assigning the dfFinal as dfnew if the dataframe is empty\n",
" if (dfFinal.empty):\n",
" dfFinal= dfnew\n",
" #merging all the dataframe\n",
" dfFinal= pd.concat([dfFinal,dfnew])\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1a578883",
"metadata": {
"execution": {
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"start_time": "2023-02-25T19:58:04.042958",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"ride_id 0\n",
"rideable_type 0\n",
"started_at 0\n",
"ended_at 0\n",
"start_station_name 788383\n",
"start_station_id 788382\n",
"end_station_name 845164\n",
"end_station_id 845164\n",
"start_lat 0\n",
"start_lng 0\n",
"end_lat 5668\n",
"end_lng 5668\n",
"member_casual 0\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#checing null values\n",
"dfFinal.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "89de696f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:06.577500Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"ride_id 0\n",
"rideable_type 0\n",
"started_at 0\n",
"ended_at 0\n",
"start_station_name 0\n",
"start_station_id 0\n",
"end_station_name 0\n",
"end_station_id 0\n",
"start_lat 0\n",
"start_lng 0\n",
"end_lat 0\n",
"end_lng 0\n",
"member_casual 0\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in above step found that columns start_station_name 532092,start_station_id 532406, \n",
"#end_station_name 577545,end_station_id 577778,end_lat 4924 and end_lng 4924 have null values\n",
"\n",
"#below code delete the values in end_station_name column with null values\n",
"dfFinal = dfFinal.dropna()\n",
"\n",
"#checking if the null values are removed\n",
"dfFinal.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "22c7beeb",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:14.842571Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duplicate Rows :\n",
"The total duplicated row 450994\n",
"The total non duplicated row 4451069\n",
"Total size of the dataframe 4902063\n"
]
}
],
"source": [
"\n",
"# Selecting duplicate rows except first\n",
"# occurrence based on 'ride_id' column\n",
"# duplicate = dfFinal[dfFinal.duplicated('ride_id')]\n",
" \n",
"print(\"Duplicate Rows :\")\n",
" \n",
"# Print the resultant Dataframe\n",
"nonDuplicateVal = (~(dfFinal.duplicated(subset=[\"ride_id\"]))).sum()\n",
"duplicateval = dfFinal.duplicated(subset=[\"ride_id\"]).sum()\n",
"\n",
"print(\"The total duplicated row\", duplicateval)\n",
"print(\"The total non duplicated row\", nonDuplicateVal)\n",
"print(\"Total size of the dataframe\",dfFinal.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "289de6e4",
"metadata": {
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},
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},
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}
],
"metadata": {
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},
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}
},
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"nbformat_minor": 5
}
| 0120/313/120313353.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
{
"cells": [
{
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"execution_count": 1,
"id": "c58539d2",
"metadata": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/pima-indians-diabetes-database/diabetes.csv\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d940bd06",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:06.223850Z",
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"start_time": "2023-02-25T19:58:06.216928",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ef3451a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:06.261397Z",
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},
"outputs": [
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" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"0 6 148 72 35 0 33.6 \n",
"1 1 85 66 29 0 26.6 \n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"0 0.627 50 1 \n",
"1 0.351 31 0 \n",
"2 0.672 32 1 \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "053e4b6d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:06.306010Z",
"iopub.status.busy": "2023-02-25T19:58:06.304803Z",
"iopub.status.idle": "2023-02-25T19:58:07.574825Z",
"shell.execute_reply": "2023-02-25T19:58:07.573551Z"
},
"papermill": {
"duration": 1.279438,
"end_time": "2023-02-25T19:58:07.577683",
"exception": false,
"start_time": "2023-02-25T19:58:06.298245",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<AxesSubplot:title={'center':'Pregnancies'}>,\n",
" <AxesSubplot:title={'center':'Glucose'}>,\n",
" <AxesSubplot:title={'center':'BloodPressure'}>],\n",
" [<AxesSubplot:title={'center':'SkinThickness'}>,\n",
" <AxesSubplot:title={'center':'Insulin'}>,\n",
" <AxesSubplot:title={'center':'BMI'}>],\n",
" [<AxesSubplot:title={'center':'DiabetesPedigreeFunction'}>,\n",
" <AxesSubplot:title={'center':'Age'}>,\n",
" <AxesSubplot:title={'center':'Outcome'}>]], dtype=object)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.hist() # useful way to look at outliers"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "54c753e7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:07.591493Z",
"iopub.status.busy": "2023-02-25T19:58:07.591090Z",
"iopub.status.idle": "2023-02-25T19:58:07.600822Z",
"shell.execute_reply": "2023-02-25T19:58:07.599717Z"
},
"papermill": {
"duration": 0.019497,
"end_time": "2023-02-25T19:58:07.603321",
"exception": false,
"start_time": "2023-02-25T19:58:07.583824",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Pregnancies 0\n",
"Glucose 0\n",
"BloodPressure 0\n",
"SkinThickness 0\n",
"Insulin 0\n",
"BMI 0\n",
"DiabetesPedigreeFunction 0\n",
"Age 0\n",
"Outcome 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ec38520a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:07.617044Z",
"iopub.status.busy": "2023-02-25T19:58:07.616668Z",
"iopub.status.idle": "2023-02-25T19:58:07.622593Z",
"shell.execute_reply": "2023-02-25T19:58:07.621556Z"
},
"papermill": {
"duration": 0.015721,
"end_time": "2023-02-25T19:58:07.624999",
"exception": false,
"start_time": "2023-02-25T19:58:07.609278",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"x = data.iloc[:, :-1].values\n",
"y = data['Outcome'].values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9f028c51",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:07.639047Z",
"iopub.status.busy": "2023-02-25T19:58:07.638304Z",
"iopub.status.idle": "2023-02-25T19:58:07.646787Z",
"shell.execute_reply": "2023-02-25T19:58:07.645376Z"
},
"papermill": {
"duration": 0.018257,
"end_time": "2023-02-25T19:58:07.649291",
"exception": false,
"start_time": "2023-02-25T19:58:07.631034",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 6. , 148. , 72. , ..., 33.6 , 0.627, 50. ],\n",
" [ 1. , 85. , 66. , ..., 26.6 , 0.351, 31. ],\n",
" [ 8. , 183. , 64. , ..., 23.3 , 0.672, 32. ],\n",
" ...,\n",
" [ 5. , 121. , 72. , ..., 26.2 , 0.245, 30. ],\n",
" [ 1. , 126. , 60. , ..., 30.1 , 0.349, 47. ],\n",
" [ 1. , 93. , 70. , ..., 30.4 , 0.315, 23. ]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e3c18fc8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:07.663682Z",
"iopub.status.busy": "2023-02-25T19:58:07.663248Z",
"iopub.status.idle": "2023-02-25T19:58:08.719072Z",
"shell.execute_reply": "2023-02-25T19:58:08.718096Z"
},
"papermill": {
"duration": 1.066364,
"end_time": "2023-02-25T19:58:08.721881",
"exception": false,
"start_time": "2023-02-25T19:58:07.655517",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "46992ddd",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.740482Z",
"iopub.status.busy": "2023-02-25T19:58:08.739939Z",
"iopub.status.idle": "2023-02-25T19:58:08.756547Z",
"shell.execute_reply": "2023-02-25T19:58:08.755300Z"
},
"papermill": {
"duration": 0.027408,
"end_time": "2023-02-25T19:58:08.759407",
"exception": false,
"start_time": "2023-02-25T19:58:08.731999",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"sc = StandardScaler() # is this good? we don't know if data is following normal distribution\n",
"\n",
"x_train = sc.fit_transform(x_train)\n",
"x_test = sc.transform(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4dfffb54",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.774931Z",
"iopub.status.busy": "2023-02-25T19:58:08.773432Z",
"iopub.status.idle": "2023-02-25T19:58:08.780677Z",
"shell.execute_reply": "2023-02-25T19:58:08.779842Z"
},
"papermill": {
"duration": 0.017059,
"end_time": "2023-02-25T19:58:08.782970",
"exception": false,
"start_time": "2023-02-25T19:58:08.765911",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.90832902, 0.91569367, 0.44912368, ..., 0.37852648,\n",
" 0.67740401, 1.69955804],\n",
" [ 0.03644676, -0.75182191, -0.47230103, ..., -0.50667229,\n",
" -0.07049698, -0.96569189],\n",
" [-1.12606292, 1.38763205, 1.06340683, ..., 2.54094063,\n",
" -0.11855487, -0.88240283],\n",
" ...,\n",
" [ 0.03644676, -0.84620959, -0.21634972, ..., -0.94927168,\n",
" -0.95656442, -1.04898095],\n",
" [ 2.0708387 , -1.12937261, 0.24436264, ..., -0.26640405,\n",
" -0.50001442, 0.11706589],\n",
" [ 0.32707418, 0.47521786, 0.65388473, ..., -4.07275877,\n",
" 0.52121586, 2.94889395]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d8584143",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.798805Z",
"iopub.status.busy": "2023-02-25T19:58:08.797698Z",
"iopub.status.idle": "2023-02-25T19:58:08.803944Z",
"shell.execute_reply": "2023-02-25T19:58:08.803060Z"
},
"papermill": {
"duration": 0.016607,
"end_time": "2023-02-25T19:58:08.806388",
"exception": false,
"start_time": "2023-02-25T19:58:08.789781",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"model = GaussianNB()\n",
"model.fit(x_train, y_train);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "14360dbe",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.821629Z",
"iopub.status.busy": "2023-02-25T19:58:08.820841Z",
"iopub.status.idle": "2023-02-25T19:58:08.826026Z",
"shell.execute_reply": "2023-02-25T19:58:08.824961Z"
},
"papermill": {
"duration": 0.015697,
"end_time": "2023-02-25T19:58:08.828539",
"exception": false,
"start_time": "2023-02-25T19:58:08.812842",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"y_pred = model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b5c02f99",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.843744Z",
"iopub.status.busy": "2023-02-25T19:58:08.842888Z",
"iopub.status.idle": "2023-02-25T19:58:08.850135Z",
"shell.execute_reply": "2023-02-25T19:58:08.849181Z"
},
"papermill": {
"duration": 0.017358,
"end_time": "2023-02-25T19:58:08.852355",
"exception": false,
"start_time": "2023-02-25T19:58:08.834997",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
" 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
" 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0307b7bc",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.868759Z",
"iopub.status.busy": "2023-02-25T19:58:08.867944Z",
"iopub.status.idle": "2023-02-25T19:58:08.881537Z",
"shell.execute_reply": "2023-02-25T19:58:08.880048Z"
},
"papermill": {
"duration": 0.024947,
"end_time": "2023-02-25T19:58:08.884973",
"exception": false,
"start_time": "2023-02-25T19:58:08.860026",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion matrix:\n",
" [[93 14]\n",
" [18 29]]\n",
"\n",
"Classification report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.84 0.87 0.85 107\n",
" 1 0.67 0.62 0.64 47\n",
"\n",
" accuracy 0.79 154\n",
" macro avg 0.76 0.74 0.75 154\n",
"weighted avg 0.79 0.79 0.79 154\n",
"\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"\n",
"# Confusion matrix\n",
"cm = metrics.confusion_matrix (y_test, y_pred)\n",
"print('Confusion matrix:\\n', cm)\n",
"\n",
"# Precision, recall, F1-score\n",
"report = metrics.classification_report(y_test, y_pred)\n",
"print('\\nClassification report:\\n', report) \n",
"\n",
"# note: accuracy isn't a good metric here because the target variable classes are not balanced (107 no vs. 47 yes)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "06067d7d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.900801Z",
"iopub.status.busy": "2023-02-25T19:58:08.900232Z",
"iopub.status.idle": "2023-02-25T19:58:08.910018Z",
"shell.execute_reply": "2023-02-25T19:58:08.908729Z"
},
"papermill": {
"duration": 0.020532,
"end_time": "2023-02-25T19:58:08.912548",
"exception": false,
"start_time": "2023-02-25T19:58:08.892016",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0.6444444444444444"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# F1 score\n",
"metrics.f1_score(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "27d43d14",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-25T19:58:08.928032Z",
"iopub.status.busy": "2023-02-25T19:58:08.927344Z",
"iopub.status.idle": "2023-02-25T19:58:09.168888Z",
"shell.execute_reply": "2023-02-25T19:58:09.167883Z"
},
"papermill": {
"duration": 0.251886,
"end_time": "2023-02-25T19:58:09.171168",
"exception": false,
"start_time": "2023-02-25T19:58:08.919282",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"names = ['negative (0)','positive (1)']\n",
"\n",
"# visualizing confusion matrix\n",
"plt.figure()\n",
"plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Reds)\n",
"plt.title(\"NB Confusion Matrix\\n 0= no diabetes, 1= diabetes\")\n",
"plt.colorbar()\n",
"tick_marks = np.arange(len(names))\n",
"plt.xticks(tick_marks, names, rotation=90)\n",
"plt.yticks(tick_marks, names)\n",
"plt.tight_layout()\n",
"plt.ylabel('True label')\n",
"plt.xlabel('Predicted label')\n",
"for i,j in ((x,y) for x in range(len(cm))\n",
" for y in range(len(cm[0]))):\n",
" plt.annotate(str(cm[i][j]),xy=(i,j))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0daefacb",
"metadata": {
"papermill": {
"duration": 0.006957,
"end_time": "2023-02-25T19:58:09.185527",
"exception": false,
"start_time": "2023-02-25T19:58:09.178570",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2196432",
"metadata": {
"papermill": {
"duration": 0.006845,
"end_time": "2023-02-25T19:58:09.199545",
"exception": false,
"start_time": "2023-02-25T19:58:09.192700",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 14.144431,
"end_time": "2023-02-25T19:58:10.029626",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2023-02-25T19:57:55.885195",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0120/313/120313409.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0120/313/120313839.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"736acc(...TRUNCATED) | 0120/314/120314416.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3267e9ef\",\n \"metadata\": (...TRUNCATED) | 0120/314/120314598.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\n \"cells\": [\n {\n \"attachments\": {\n \"5f7f70fa-89c2-4932-b4a4-3cb66f976adf.png\": {\n(...TRUNCATED) | 0120/314/120314670.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"186be7(...TRUNCATED) | 0120/315/120315145.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"55c4f1(...TRUNCATED) | 0120/315/120315374.ipynb | s3://data-agents/kaggle-outputs/sharded/009_00120.jsonl.gz |
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