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‎Medical_insurance_codealong/data/insurance.csv‎

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‎Medical_insurance_codealong/notebook/medical_insurance_codealong.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Medical Costs : How your profile affects your medical charges?\n",
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"\n",
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"\n",
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"Today we will explore a data set dedicated to the cost of treatment of different patients. The cost of treatment depends on many factors: diagnosis, type of clinic, city of residence, age and so on. We have no data on the diagnosis of patients. But we have other information that can help us to make a conclusion about the health of patients and practice regression analysis. In any case, I wish you to be healthy! Let's look at our data."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Columns\n",
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"\n",
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" - age: age of primary beneficiary\n",
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"\n",
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" - sex: insurance contractor gender, female, male\n",
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"\n",
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" - bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9\n",
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"\n",
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" - children: Number of children covered by health insurance / Number of dependents\n",
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"\n",
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" - smoker: Smoking\n",
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"\n",
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" - region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.\n",
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"\n",
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" - charges: Individual medical costs billed by health insurance\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import Libraries\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn import metrics\n",
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"from sklearn.metrics import mean_squared_error\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1. Exploratory Data Analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.2 Examining the Relationship of Charges to the Categorical Features\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"#### 1.3 Charges Between Gender\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.4 Charges between Smokers and non-Smokers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.5 Charges Among Regions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.6 In Relation to Other Features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.7 Smokers vs Non- Smokers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Pre-Processing the Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. Quantifying the effect of the features to the medical charges"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"### 4. Basic Machine Learning: Comparison Between Selected Regression Models\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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