Implementing Machine Learning steps using Regression Model.

From our previous article we looked at the machine learning steps. Lets now have a look at how to implement a machine learning model using Python.

The dataset used is collected from kaggle.

We will be able to predict the insurance amount for a person.

  • We start by importing necessary modules as shown:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
  • Then import the data.
  • Clean the data by removing duplicate values and transform the columns into numerical values to make the easier to work with.

The final dataset is as shown below;
Screenshot (38)

  • Using the cleaned dataset, now split it into training and test sets.
X=data.drop(['charges'], axis=1)
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
  • After splitting the model choose the suitable algorithm. In this case we will use Linear Regression since we need to predict a numerical value based on some parameters.
  • Now predict the testing dataset and find how accurate your predictions are.
  • Accuracy score is predicted as follows:
  • parameter tuning Lets find the hyperparameters which affect various variables in the dataset.