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Dive into the heart of data science with a project that combines healthcare insights and predictive analytics. As a Data Scientist at a top Health Insurance company, you have the opportunity to predict customer healthcare costs using the power of machine learning. Your insights will help tailor services and guide customers in planning their healthcare expenses more effectively.

Dataset Summary

Meet your primary tool: the insurance.csv dataset. Packed with information on health insurance customers, this dataset is your key to unlocking patterns in healthcare costs. Here's what you need to know about the data you'll be working with:

insurance.csv

ColumnData TypeDescription
ageintAge of the primary beneficiary.
sexobjectGender of the insurance contractor (male or female).
bmifloatBody mass index, a key indicator of body fat based on height and weight.
childrenintNumber of dependents covered by the insurance plan.
smokerobjectIndicates whether the beneficiary smokes (yes or no).
regionobjectThe beneficiary's residential area in the US, divided into four regions.
chargesfloatIndividual medical costs billed by health insurance.

A bit of data cleaning is key to ensure the dataset is ready for modeling. Once your model is built using the insurance.csv dataset, the next step is to apply it to the validation_dataset.csv. This new dataset, similar to your training data minus the charges column, tests your model's accuracy and real-world utility by predicting costs for new customers.

Let's Get Started!

This project is your playground for applying data science in a meaningful way, offering insights that have real-world applications. Ready to explore the data and uncover insights that could revolutionize healthcare planning? Let's begin this exciting journey!

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score

# Load the insurance dataset
insurance_data_path = 'insurance.csv'
insurance = pd.read_csv(insurance_data_path)
insurance.head()

Task Overview:

Develop a regression model using the insurance.csv dataset to predict charges. Evaluate the model's accuracy using the R-Squared Score. Then, apply the model to estimate predicted_charges for unseen data in validation_dataset.csv.

Task 1:

Build a regression model to predict charges using the insurance.csv dataset. Evaluate the R-Squared Score of your trained model and save it as a variable named r2_score. The model's success will be assessed based on its R-Squared Score, which must exceed a threshold of 0.65.

Task 2:

Use the trained model to predict charges for the data in validation_dataset.csv. Store the predictions in a new column named predicted_charges within the validation dataset, and save it as a pandas DataFrame called validation_data. Ensure a minimum basic charge of 1000.

⚠️ Note: If you encounter errors during model training, make sure the insurance DataFrame is properly cleaned and ready for modeling.

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# Implement model creation and training here
# Use as many cells as you need
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# Check for missing values
print(insurance.isnull().sum())

# No missing values found, so we can proceed

# Separate features and target variable
X = insurance.drop('charges', axis=1)
y = insurance['charges']

# Define categorical and numerical features
categorical_features = ['sex', 'smoker', 'region']
numerical_features = ['age', 'bmi', 'children']

# Create a column transformer for preprocessing
preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), numerical_features),
        ('cat', OneHotEncoder(), categorical_features)
    ])

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

# Example preprocessing steps
numeric_features = ['feature1', 'feature2']
numeric_transformer = StandardScaler()

categorical_features = ['feature3']
categorical_transformer = OneHotEncoder(handle_unknown='ignore')

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ])