Datasets
Models
Downloads: 1
1705 lines (1705 with data), 121.5 kB
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "66XARXevKjGr"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"df = pd.read_csv('/content/insurance.csv')"
],
"metadata": {
"id": "wlhgNMxqYPoK"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "SroxXs47KxuD",
"outputId": "07e01659-b5e3-4551-e33a-4bf15ba1b2c3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" age sex bmi children smoker region charges\n",
"0 19 female 27.900 0 yes southwest 16884.92400\n",
"1 18 male 33.770 1 no southeast 1725.55230\n",
"2 28 male 33.000 3 no southeast 4449.46200\n",
"3 33 male 22.705 0 no northwest 21984.47061\n",
"4 32 male 28.880 0 no northwest 3866.85520"
],
"text/html": [
"\n",
" <div id=\"df-ccb060b1-2620-489d-b7fe-4eadba069eb1\" class=\"colab-df-container\">\n",
" <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>age</th>\n",
" <th>sex</th>\n",
" <th>bmi</th>\n",
" <th>children</th>\n",
" <th>smoker</th>\n",
" <th>region</th>\n",
" <th>charges</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19</td>\n",
" <td>female</td>\n",
" <td>27.900</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>southwest</td>\n",
" <td>16884.92400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18</td>\n",
" <td>male</td>\n",
" <td>33.770</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>southeast</td>\n",
" <td>1725.55230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>male</td>\n",
" <td>33.000</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>southeast</td>\n",
" <td>4449.46200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>33</td>\n",
" <td>male</td>\n",
" <td>22.705</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>northwest</td>\n",
" <td>21984.47061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32</td>\n",
" <td>male</td>\n",
" <td>28.880</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>northwest</td>\n",
" <td>3866.85520</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccb060b1-2620-489d-b7fe-4eadba069eb1')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-ccb060b1-2620-489d-b7fe-4eadba069eb1 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-ccb060b1-2620-489d-b7fe-4eadba069eb1');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-ec0337e3-3d17-483a-94ad-a146df8433a1\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ec0337e3-3d17-483a-94ad-a146df8433a1')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-ec0337e3-3d17-483a-94ad-a146df8433a1 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 1338,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14,\n \"min\": 18,\n \"max\": 64,\n \"num_unique_values\": 47,\n \"samples\": [\n 21,\n 45,\n 36\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"male\",\n \"female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bmi\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.098186911679014,\n \"min\": 15.96,\n \"max\": 53.13,\n \"num_unique_values\": 548,\n \"samples\": [\n 23.18,\n 26.885\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"children\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 5,\n \"num_unique_values\": 6,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"smoker\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"no\",\n \"yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"region\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"southeast\",\n \"northeast\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"charges\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12110.011236694001,\n \"min\": 1121.8739,\n \"max\": 63770.42801,\n \"num_unique_values\": 1337,\n \"samples\": [\n 8688.85885,\n 5708.867\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"df.shape\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2VyX5fcUK0pN",
"outputId": "5bdeaee8-f882-441b-b362-93da3b4353da"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1338, 7)"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"df.info()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ra5-HKaeK30V",
"outputId": "215d7da6-c595-402a-96bf-f8f9894e8b35"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1338 entries, 0 to 1337\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 1338 non-null int64 \n",
" 1 sex 1338 non-null object \n",
" 2 bmi 1338 non-null float64\n",
" 3 children 1338 non-null int64 \n",
" 4 smoker 1338 non-null object \n",
" 5 region 1338 non-null object \n",
" 6 charges 1338 non-null float64\n",
"dtypes: float64(2), int64(2), object(3)\n",
"memory usage: 73.3+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "FhuFO33rK6X2",
"outputId": "2375a2e3-ab07-4121-af5f-c3315adcebf3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" age bmi children charges\n",
"count 1338.000000 1338.000000 1338.000000 1338.000000\n",
"mean 39.207025 30.663397 1.094918 13270.422265\n",
"std 14.049960 6.098187 1.205493 12110.011237\n",
"min 18.000000 15.960000 0.000000 1121.873900\n",
"25% 27.000000 26.296250 0.000000 4740.287150\n",
"50% 39.000000 30.400000 1.000000 9382.033000\n",
"75% 51.000000 34.693750 2.000000 16639.912515\n",
"max 64.000000 53.130000 5.000000 63770.428010"
],
"text/html": [
"\n",
" <div id=\"df-679ff5bb-eaf5-4023-965c-74bb18a1e4ea\" class=\"colab-df-container\">\n",
" <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>age</th>\n",
" <th>bmi</th>\n",
" <th>children</th>\n",
" <th>charges</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1338.000000</td>\n",
" <td>1338.000000</td>\n",
" <td>1338.000000</td>\n",
" <td>1338.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>39.207025</td>\n",
" <td>30.663397</td>\n",
" <td>1.094918</td>\n",
" <td>13270.422265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>14.049960</td>\n",
" <td>6.098187</td>\n",
" <td>1.205493</td>\n",
" <td>12110.011237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>18.000000</td>\n",
" <td>15.960000</td>\n",
" <td>0.000000</td>\n",
" <td>1121.873900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>27.000000</td>\n",
" <td>26.296250</td>\n",
" <td>0.000000</td>\n",
" <td>4740.287150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>39.000000</td>\n",
" <td>30.400000</td>\n",
" <td>1.000000</td>\n",
" <td>9382.033000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>51.000000</td>\n",
" <td>34.693750</td>\n",
" <td>2.000000</td>\n",
" <td>16639.912515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>64.000000</td>\n",
" <td>53.130000</td>\n",
" <td>5.000000</td>\n",
" <td>63770.428010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-679ff5bb-eaf5-4023-965c-74bb18a1e4ea')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-679ff5bb-eaf5-4023-965c-74bb18a1e4ea button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-679ff5bb-eaf5-4023-965c-74bb18a1e4ea');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-5590907f-9321-4e89-9dff-7c575b3cdd07\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5590907f-9321-4e89-9dff-7c575b3cdd07')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-5590907f-9321-4e89-9dff-7c575b3cdd07 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"df\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 460.6106090399993,\n \"min\": 14.049960379216154,\n \"max\": 1338.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 39.20702541106129,\n 39.0,\n 1338.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bmi\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 463.29524977918294,\n \"min\": 6.098186911679014,\n \"max\": 1338.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 30.66339686098655,\n 30.4,\n 1338.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"children\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 472.5368318870757,\n \"min\": 0.0,\n \"max\": 1338.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1338.0,\n 1.0949177877429,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"charges\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 20381.922846226596,\n \"min\": 1121.8739,\n \"max\": 63770.42801,\n \"num_unique_values\": 8,\n \"samples\": [\n 13270.422265141257,\n 9382.033,\n 1338.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"Handling Null values\n",
"\n"
],
"metadata": {
"id": "tsebi3UhK9cq"
}
},
{
"cell_type": "code",
"source": [
"\n",
"df.isnull().sum()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KJMA1mXeK_GU",
"outputId": "fc621d53-fb7c-4d4f-9a48-d42c040bc9f8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"age 0\n",
"sex 0\n",
"bmi 0\n",
"children 0\n",
"smoker 0\n",
"region 0\n",
"charges 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"sns.distplot(df['age'])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 657
},
"id": "5UB5hc66LCCV",
"outputId": "0234b02c-6faa-4b53-86ce-6d4dc59e3c1a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-8-7452d86f8334>:1: UserWarning: \n",
"\n",
"`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
"\n",
"Please adapt your code to use either `displot` (a figure-level function with\n",
"similar flexibility) or `histplot` (an axes-level function for histograms).\n",
"\n",
"For a guide to updating your code to use the new functions, please see\n",
"https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
"\n",
" sns.distplot(df['age'])\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: xlabel='age', ylabel='Density'>"
]
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"sns.boxplot(df['age'])\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "P6dgXdNTLFh7",
"outputId": "8e2c9b4b-00f5-4eed-9517-ce06633c1dd7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Axes: ylabel='age'>"
]
},
"metadata": {},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Detect Outlies of age column\n",
"1. Calculate first quartile(q1) and third quartile(q3)\n"
],
"metadata": {
"id": "hOTYYhwTLIq7"
}
},
{
"cell_type": "code",
"source": [
"\n",
"q1 = df['age'].quantile(0.25)\n",
"q1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uR0z17xKLMaW",
"outputId": "53e1c8c3-06d1-4cad-efb8-84b35caefb3f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"27.0"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"q3 = df['age'].quantile(0.75)\n",
"q3\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rwrSAYMTLQY5",
"outputId": "3cefd37a-c4cb-496e-c45f-4068ffc869f1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"51.0"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"IQR = q3-q1\n",
"IQR\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JnWefwYALTJG",
"outputId": "d1755255-80c3-4350-dec4-ed61e3cbf862"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"24.0"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "markdown",
"source": [
"Calculate Lower Bound q1 - 1.5IQR Calculate Upper Bound q3 + 1.5IQR\n"
],
"metadata": {
"id": "FWfqaKgfLVsE"
}
},
{
"cell_type": "code",
"source": [
"\n",
"lowerBound = q1-(1.5*IQR)\n",
"upperBound = q3+(1.5*IQR)\n",
"print(lowerBound)\n",
"print(upperBound)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mJDBbR4DLXsd",
"outputId": "c0a8791e-4617-4f04-dc5f-74fb2b6a8327"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-9.0\n",
"87.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df['age']=np.where(df['age']>upperBound,upperBound,df['age'])\n",
"print(df[df['age']>upperBound])\n",
"print(df[df['age']<lowerBound])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UhfmhbkmLb1-",
"outputId": "20665151-0e8b-49ec-fef0-4b5b4229201a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Empty DataFrame\n",
"Columns: [age, sex, bmi, children, smoker, region, charges]\n",
"Index: []\n",
"Empty DataFrame\n",
"Columns: [age, sex, bmi, children, smoker, region, charges]\n",
"Index: []\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Now there are no outliers in age column\n",
"Encoding data using LabelEncoder"
],
"metadata": {
"id": "3yiCP65rLedS"
}
},
{
"cell_type": "code",
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"le = LabelEncoder()\n",
"df['sex']=le.fit_transform(df['sex'])\n",
"df['smoker']=le.fit_transform(df['smoker'])\n",
"df['region']=le.fit_transform(df['region'])\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "58EKsA8qLhrl",
"outputId": "2964b941-f950-4cd4-af95-68bcb419e733"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" age sex bmi children smoker region charges\n",
"0 19.0 0 27.900 0 1 3 16884.92400\n",
"1 18.0 1 33.770 1 0 2 1725.55230\n",
"2 28.0 1 33.000 3 0 2 4449.46200\n",
"3 33.0 1 22.705 0 0 1 21984.47061\n",
"4 32.0 1 28.880 0 0 1 3866.85520"
],
"text/html": [
"\n",
" <div id=\"df-9dace5e2-3bab-4eef-ad47-e29610adca77\" class=\"colab-df-container\">\n",
" <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>age</th>\n",
" <th>sex</th>\n",
" <th>bmi</th>\n",
" <th>children</th>\n",
" <th>smoker</th>\n",
" <th>region</th>\n",
" <th>charges</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>27.900</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>16884.92400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18.0</td>\n",
" <td>1</td>\n",
" <td>33.770</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1725.55230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>33.000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>4449.46200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>33.0</td>\n",
" <td>1</td>\n",
" <td>22.705</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>21984.47061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32.0</td>\n",
" <td>1</td>\n",
" <td>28.880</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3866.85520</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9dace5e2-3bab-4eef-ad47-e29610adca77')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-9dace5e2-3bab-4eef-ad47-e29610adca77 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-9dace5e2-3bab-4eef-ad47-e29610adca77');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-ff5e768e-5512-4fe3-b6fb-151125595a32\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff5e768e-5512-4fe3-b6fb-151125595a32')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-ff5e768e-5512-4fe3-b6fb-151125595a32 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 1338,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.049960379216154,\n \"min\": 18.0,\n \"max\": 64.0,\n \"num_unique_values\": 47,\n \"samples\": [\n 21.0,\n 45.0,\n 36.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sex\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bmi\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.098186911679014,\n \"min\": 15.96,\n \"max\": 53.13,\n \"num_unique_values\": 548,\n \"samples\": [\n 23.18,\n 26.885\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"children\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 5,\n \"num_unique_values\": 6,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"smoker\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"region\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 2,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"charges\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12110.011236694001,\n \"min\": 1121.8739,\n \"max\": 63770.42801,\n \"num_unique_values\": 1337,\n \"samples\": [\n 8688.85885,\n 5708.867\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"source": [
"Seperating data into input and ouput/target\n"
],
"metadata": {
"id": "HzR6jd5RLk3H"
}
},
{
"cell_type": "code",
"source": [
"x = df.drop(columns=['charges'], axis=1)\n",
"y = df['charges']\n",
"print(x)\n",
"print(y)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "csci6c_LLoGN",
"outputId": "2399cfdc-e00b-4a8e-fc08-3107364fa5b2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" age sex bmi children smoker region\n",
"0 19.0 0 27.900 0 1 3\n",
"1 18.0 1 33.770 1 0 2\n",
"2 28.0 1 33.000 3 0 2\n",
"3 33.0 1 22.705 0 0 1\n",
"4 32.0 1 28.880 0 0 1\n",
"... ... ... ... ... ... ...\n",
"1333 50.0 1 30.970 3 0 1\n",
"1334 18.0 0 31.920 0 0 0\n",
"1335 18.0 0 36.850 0 0 2\n",
"1336 21.0 0 25.800 0 0 3\n",
"1337 61.0 0 29.070 0 1 1\n",
"\n",
"[1338 rows x 6 columns]\n",
"0 16884.92400\n",
"1 1725.55230\n",
"2 4449.46200\n",
"3 21984.47061\n",
"4 3866.85520\n",
" ... \n",
"1333 10600.54830\n",
"1334 2205.98080\n",
"1335 1629.83350\n",
"1336 2007.94500\n",
"1337 29141.36030\n",
"Name: charges, Length: 1338, dtype: float64\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Scaling\n",
"\n"
],
"metadata": {
"id": "dBEapXyVLsRK"
}
},
{
"cell_type": "code",
"source": [
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"Scaler = StandardScaler()\n",
"X = Scaler.fit_transform(x)\n",
"X"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-faW-TKmLuFG",
"outputId": "194399f7-7d8c-4498-f140-9e577caa762c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-1.43876426, -1.0105187 , -0.45332 , -0.90861367, 1.97058663,\n",
" 1.34390459],\n",
" [-1.50996545, 0.98959079, 0.5096211 , -0.07876719, -0.5074631 ,\n",
" 0.43849455],\n",
" [-0.79795355, 0.98959079, 0.38330685, 1.58092576, -0.5074631 ,\n",
" 0.43849455],\n",
" ...,\n",
" [-1.50996545, -1.0105187 , 1.0148781 , -0.90861367, -0.5074631 ,\n",
" 0.43849455],\n",
" [-1.29636188, -1.0105187 , -0.79781341, -0.90861367, -0.5074631 ,\n",
" 1.34390459],\n",
" [ 1.55168573, -1.0105187 , -0.26138796, -0.90861367, 1.97058663,\n",
" -0.46691549]])"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"source": [
"Splitting Data\n"
],
"metadata": {
"id": "A2AEqLOaLzKG"
}
},
{
"cell_type": "code",
"source": [
"\n",
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=0)"
],
"metadata": {
"id": "SI2K63DWL0l7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.linear_model import LinearRegression\n",
"lr = LinearRegression()\n",
"lr.fit(x_train,y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
},
"id": "Dy2KTIyxL30T",
"outputId": "0416443e-18bd-4b6b-e20e-1e34fcfadd0a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
],
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"pred = lr.predict(x_test)\n",
"pred"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QcJur_rJL6wm",
"outputId": "7b42d53c-ea30-4106-c80f-fc5f61209e86"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([11051.54909755, 9821.28110689, 37867.57220923, 16125.70579228,\n",
" 6920.27132517, 3879.38549816, 1448.91928088, 14390.17797974,\n",
" 9022.95151353, 7458.83371884, 4584.60125463, 10309.9886336 ,\n",
" 8693.03891958, 4085.25393494, 27551.60737718, 11151.0640722 ,\n",
" 11243.0536825 , 5962.9521121 , 8181.9015666 , 26750.7993431 ,\n",
" 33448.59842228, 14350.03320383, 11672.89478465, 32235.7832204 ,\n",
" 4326.07702625, 9096.53607025, 1045.25196369, 10177.76672094,\n",
" 4042.60346751, 10384.28681219, 9035.98983755, 40123.71002379,\n",
" 15507.20819083, 13678.264976 , 24481.97362849, 5059.21988589,\n",
" 12889.80609711, 30333.92625689, 33301.25091403, 3431.35787088,\n",
" 3941.81614597, 4203.90901434, 30219.19050725, 39245.56885373,\n",
" 27762.83744249, 4994.74188765, 11042.48621304, 7760.15047885,\n",
" 3569.09734756, 10613.61535955, 5544.95921408, 3397.80923785,\n",
" 32701.67144343, 38285.57836702, 16290.50463759, 6965.99677468,\n",
" 5895.27536963, 9364.94083823, 9395.1780384 , 11722.13868077,\n",
" 1611.87873326, 38750.4981005 , 15296.11225478, 11708.42958487,\n",
" 14076.39653066, 13904.28564489, 25798.46519738, 31953.12169371,\n",
" 1168.25915489, 10184.5995492 , 12273.00414884, 11867.15734569,\n",
" 24808.10644113, 15908.53043993, 11198.67421883, 12631.50869281,\n",
" 6433.71238434, 9915.55343233, 29953.19794316, 38768.07351788,\n",
" 12011.54405754, 37253.64166612, 4056.21325429, 9255.50826428,\n",
" 34537.73817683, 28976.62623495, 8444.02316285, 4738.69241453,\n",
" 11959.22562859, 30006.0695852 , 10041.58386562, 11243.48874027,\n",
" 8183.6075869 , 9280.51490529, 8255.40224617, 7239.23538241,\n",
" 35731.00350944, 32878.29978853, 7591.7717691 , 14921.91368481,\n",
" 4184.53547122, 8690.01064385, 6619.75457992, 31535.59819898,\n",
" 32775.00677547, 1887.67848916, 8933.68024017, 6520.27249906,\n",
" 14475.77105663, 36880.82790297, 10252.51955517, 10775.16399139,\n",
" 10192.95246113, 26581.47470665, 39936.28907748, 8453.03671416,\n",
" 143.08142864, 8874.82383918, 15117.85425873, 9557.08594807,\n",
" 35275.59070316, 7270.62037452, 16826.50981439, 9572.8088055 ,\n",
" 8159.95902395, 2952.65859719, 32706.51413703, 31283.9896012 ,\n",
" 39216.89699401, 5362.49911669, 9675.40479836, 3778.85297694,\n",
" 7946.39718647, 8585.02883773, 31341.17050506, 29551.7714624 ,\n",
" 29853.91861524, 9151.88904567, 32625.66390263, 3229.01239018,\n",
" 3529.93652932, 11054.17156002, 13442.38216447, 12761.80223436,\n",
" 5363.70249634, 15875.56674406, 15252.72853146, 2382.17016287,\n",
" -120.56014234, 10834.07802124, 7372.12214193, 31759.88622234,\n",
" 12314.86913452, 2548.30390645, 6284.28252705, 8170.0107525 ,\n",
" 4285.24015268, 2331.14818812, 11414.21888159, 12551.18010753,\n",
" 7208.95663304, 16615.95420641, 11792.56220606, 13920.69808423,\n",
" 3134.30793579, 7262.13973297, 22758.38813544, 7596.99822972,\n",
" 5401.65993492, 5339.75438707, 6641.09944767, 5142.27041 ,\n",
" 9983.03913716, 5526.89132472, 5628.18992827, 6975.95618531,\n",
" 3673.17907317, 5521.32735633, 37913.25218948, 1337.01243212,\n",
" 12636.06438156, 8935.78276524, 13661.56267036, 5572.770716 ,\n",
" 5181.38538205, 36214.23931831, 4207.49996636, 1896.75580314,\n",
" 15163.16594007, 12674.02182014, 34823.20434979, 5093.20670396,\n",
" 5580.90282376, 31320.99694717, 5982.46375195, 1940.59597738,\n",
" 8389.18364163, 10016.84576515, 8238.45168712, 5687.97489766,\n",
" 13133.993244 , 38538.79843345, 13749.62605459, 28607.07797491,\n",
" 6685.39503417, 35610.2777963 , 3716.13611211, 12131.97274228,\n",
" 9356.80352592, 6339.94803517, 11268.82158683, 14519.98276598,\n",
" 5175.63113265, 4233.99203814, 7768.5658748 , 1150.93881984,\n",
" 7861.51707835, 4401.34365822, 13351.6815701 , 4312.6173173 ,\n",
" 10007.10646576, 7274.06437597, 9167.81641515, 2307.24223958,\n",
" 13115.48905979, 16739.15126554, 15287.81532153, 10516.20091624,\n",
" 5706.41694356, 2453.74757235, 2105.41152789, 13376.27751148,\n",
" 14339.33209654, 5021.89418573, 4090.605953 , 9366.33184625,\n",
" 9969.15202337, 27902.13744062, 7572.78791802, 10543.34735666,\n",
" 6174.31283586, 29537.11622517, 11027.94086667, 7457.00641596,\n",
" 10225.18823673, 12176.62074648, 2939.88516441, 10785.5019008 ,\n",
" 1443.73866643, 6998.45741682, 28534.01786505, 38460.70771961,\n",
" 6225.11287288, 8374.92523275, 2386.63715545, 305.52277009,\n",
" 10407.09161901, 4362.55365462, 4960.62107139, 2662.94916464,\n",
" 7249.10943879, 33132.60876475, 37964.64601751, 14795.4925882 ,\n",
" 8191.23299164, 16000.85775643, 33002.87197448, 9520.82908522,\n",
" 33323.6218328 , 3516.92369698, 30497.30098573, 8044.20793611,\n",
" 14191.55955407, 4083.10066112, 32268.03153529, 8315.02063095,\n",
" 11483.73451116, 9510.21374527, 4115.91551624, 12660.02379304,\n",
" 11717.00935707, 8354.17363399, 13269.77614371, 2699.20978031,\n",
" 10542.22785417, 5391.53803822, 11313.67089224, 31398.88972707,\n",
" 10029.81495966, 1118.21159472, 462.52270438, 39674.38015203,\n",
" 9676.2207617 , 7081.42950834, 13999.68415448, 13421.82776838,\n",
" 26976.22819473, 7100.7475948 , 6788.44279001, 12033.45246932,\n",
" 2786.36526883, 3902.46962572, 24859.29396183, 26090.84463032,\n",
" 13371.81813565, 3164.31052787, 5117.58945791, 9325.3139586 ,\n",
" 12404.934023 , 23317.59720695, 30625.80462982, 10046.65063794,\n",
" 23804.49406862, 2804.50033014, 11550.33636023, 7549.77464787,\n",
" 8233.25736365, 312.94242569, 7720.18671702, 35401.11202663,\n",
" 6152.78016128, 6237.66443531, 178.89028134, 10901.70768044,\n",
" 6750.59209883, 9992.98880624, 38764.63808826, 27466.58793525,\n",
" 11489.14198588, 35484.16547957, 15039.21254017, 6810.42408411,\n",
" 11015.353549 , 6839.24282704, 36512.17018034, 6045.00741789,\n",
" 11075.16473053, 741.96608741, 24186.22593789, 1881.15932715,\n",
" 34245.135946 , 11379.34404185, 1787.47141685, 32018.68078654,\n",
" 6644.72832713, 5200.52497017, 37952.77965645, 2171.21921984,\n",
" 9595.04338665, 2650.53107838, 12806.89011534, 1008.0849294 ,\n",
" 11113.81326429, 7092.09772183, 36343.11043514, 7160.77113975,\n",
" 30438.75625726, 29472.1875026 , 7100.76722883, 10790.23315907,\n",
" 1879.90465675, 2200.37960027, 3793.92758918, 12706.65566117,\n",
" 37039.77367021, 9716.69560709, 345.17579449, 11387.82123255,\n",
" 5036.80429065, 9779.98465515, 5576.16518582, 7260.90814745,\n",
" 4311.38104385, 28280.88564325, 4363.51594298, -921.56882455,\n",
" 33139.88333244, 12910.28546987, 35733.85862246, 9841.66334273,\n",
" 7663.70159627, -94.4834943 , 2560.96139226, 11527.50523772,\n",
" 5669.76181684, 3293.09776754, 12120.72252044, 7743.17735694,\n",
" 6920.9128887 , 5459.93977214, 3049.37781558, 31786.79912085,\n",
" 3518.32063003, 8595.19234733, 4796.86509127, 13024.5060374 ,\n",
" 14691.59701387, 7163.95913996, 26454.5855911 , 14277.02407734,\n",
" 17354.84539043, 11476.70520069])"
]
},
"metadata": {},
"execution_count": 20
}
]
}
]
}