Building a Machine Learning System to Predict Medical Insurance Costs

Introduction

• Presenter: Siddharthan
• Project Video: 11th project in machine learning series
• Objective: Build a system to predict medical insurance costs using machine learning
• Programming Language: Python
• Platform Used: Google Collaboratory
• Data Source: Kaggle

Workflow Overview

1. Problem Statement
2. Workflow Explanation
3. Data Collection
4. Data Analysis
5. Data Preprocessing
6. Data Splitting
7. Model Training
8. Model Evaluation
9. Building Predictive System

Problem Statement

• Task: Predict medical insurance cost for individuals using provided data.
• Dataset Requirements: Insurance cost data with parameters like health issues, gender, etc.
• Role: Data Scientist/Machine Learning Expert to build the predictive system.

Workflow Details

1. Data Collection

• Step: Collect insurance cost data.
• Data Includes: Age, sex, BMI, children, smoker status, region, insurance charges.

2. Data Analysis

• Purpose: Understand data and its meaningful insights.
• Steps:
  • Analyze data structure.
  • Use plots to visualize data.

3. Data Preprocessing

• Purpose: Prepare data for machine learning model.
• Steps:
  • Handle missing values.
  • Encode categorical features.

4. Data Splitting

• Purpose: Split data into training and testing datasets.
• Steps:
  • Use train_test_split function from sklearn to split data.
  • Typical split: 80% training, 20% testing.

5. Model Training

• Model: Linear Regression
• Steps:
  • Initialize the model.
  • Train using training data (X_train, y_train).

6. Model Evaluation

• Purpose: Verify performance of the model.
• Steps:
  • Predict on training and testing data.
  • Calculate R-squared value for both sets to measure performance.

7. Building Predictive System

• Objective: Predict insurance cost with new input data.
• Steps:
  • Input data transformation.
  • Use trained model to predict costs.

Detailed Implementation

Dependencies

• Libraries: numpy, pandas, matplotlib, seaborn, sklearn.
• **Load in Python: import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics

Data Collection and Analysis

1. Load Data

2. Print First 5 Rows

3. Data Info

4. Check for Missing Values

5. Statistical Measures

6. Data Visualization

• Age Distribution plt.figure(figsize=(6, 6)) sns.histplot(insurance_dataset['age'], kde=True) plt.title('Age Distribution') plt.show()
  • Other Visualizations: Sex Distribution, BMI Distribution, Children Count, Smoker Count, Region Distribution, Charges Distribution

Data Preprocessing

• Encoding Categorical Features insurance_dataset.replace({'sex': {'male': 0, 'female': 1}, 'smoker': {'yes': 0, 'no': 1}, 'region': {'southeast': 0, 'southwest': 1, 'northeast': 2, 'northwest': 3}}, inplace=True)

Splitting Features and Target

• Features and Target Variables X = insurance_dataset.drop(columns='charges', axis=1) y = insurance_dataset['charges']
• Split Data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)

Model Training

• Initialize and Train Model regressor = LinearRegression() regressor.fit(X_train, y_train)

Model Evaluation

• Predict & Evaluate Training Data train_data_prediction = regressor.predict(X_train) r2_train = metrics.r2_score(y_train, train_data_prediction) print(r2_train)
• Predict & Evaluate Testing Data test_data_prediction = regressor.predict(X_test) r2_test = metrics.r2_score(y_test, test_data_prediction) print(r2_test)

Building Predictive System

• Prediction Example input_data = (40, 1, 25.8, 2, 1, 0) # Sample Input Data (age, sex, bmi, children, smoker, region) input_data_numpy = np.asarray(input_data) input_data_reshaped = input_data_numpy.reshape(1, -1) prediction = regressor.predict(input_data_reshaped) print('The insurance cost is USD', prediction[0])

Conclusion

• Summary: Successfully built and evaluated a linear regression model to predict medical insurance costs.
• Next Steps: Practice the codes, try other regression models for improved accuracy, and engage with the community for feedback and questions.

Note: Review machine learning basics, model evaluation techniques, and Python data structures for deeper understanding. Recommended modules include Python basics and model training and evaluation techniques from the course.