Predict Insurance Charges using different Linear Regression Models and compare results.
Here I will discuss how Linear Regression works and how can we implement it in different ways to achieve best accuracy.
Data set overview:
I have taken health insurance data set for analysis. It contains 1338 samples and 7 features.
Here we want to predict insurance charges using given features like age, sex, bmi, children, smoker and region.
You will be able to download data from here.
How will you deal with unknown categories which were not part of your training set?
Answer: set handle_unknown=’ignore’ in OneHotEncoder
Example:
Let’s consider below as training data set:
How will you evaluate your regression model?
What is Model Evaluation in machine learning?
- Model Evaluation is the process to check how accurate or inaccurate our model is.
- Scikit-learn library provides different types of evaluation metrics which we can use to check model performance.
- We can implement our own evaluation metrics also based on our data set and domain requirement.
Common Regression Evaluation Metrics:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared
1. Mean Absolute Error (MAE):
- Used to check how inaccurate our model is.
- MAE = Sum of absolute error/number of samples
- Absolute Error = Absolute difference between actual and predicted data.
- Below it shows predicted values from 2…
Classification Model
Road safety should be a prior interest for governments, local authorities and private companies investing in technologies that can help reduce accidents and improve overall driver safety.
Here we will analyze historic collision data and prepare classification model to predict future events.
Data:
Download data set from: https://s3.us.cloud-object-storage.appdomain.cloud/cf-courses-data/CognitiveClass/DP0701EN/version-2/Data-Collisions.csv
Shape of a data set: 194673 samples, 38 features
Priyanka Dave
Data Science Enthusiast
