Supervised Learning

Supervised Learning is a machine learning technique where the algorithm learns from labeled training data to make predictions or decisions. In this type of learning, the dataset used for training consists of input features and corresponding target values.

To train a supervised learning model, the data is divided into two parts - training data and testing data. The training data is used to train the model, while the testing data is used to evaluate the performance of the trained model.

The following steps are generally followed in a supervised learning workflow:

1. Data Collection: Gather the labeled training data that consists of input features and corresponding target values.

2. Data Preprocessing: Clean the data, handle missing values, handle categorical variables, and scale the data if required.

3. Feature Selection/Extraction: Select relevant features that have a significant impact on the target variable. You can also perform feature extraction techniques to create new features from existing ones.

4. Model Selection: Choose an appropriate supervised learning algorithm based on the problem at hand, the type of data, and the available computational resources.

5. Model Training: Train the selected model using the training data.

6. Model Evaluation: Evaluate the performance of the trained model using the testing data. Common evaluation metrics for regression problems include R-squared score, mean squared error (MSE), and mean absolute error (MAE).

7. Model Tuning: Fine-tune the hyperparameters of the model to optimize its performance.

8. Model Deployment: Deploy the trained model to make predictions on new unseen data.

Here's an example of training a simple linear regression model using scikit-learn library in Python:

1import pandas as pd
2from sklearn.model_selection import train_test_split
3from sklearn.linear_model import LinearRegression
4
5# Load the dataset
6dataset_url = 'https://raw.githubusercontent.com/algo-daily/python-tutorial/main/datasets/insurance.csv'
7df = pd.read_csv(dataset_url)
8
9# Separate the features and target variable
10X = df[['age', 'bmi']]
11y = df['charges']
12
13# Split the data into training and testing sets
14data_train, data_test, target_train, target_test = train_test_split(X, y, test_size=0.2, random_state=42)
15
16# Create and train the model
17model = LinearRegression()
18model.fit(data_train, target_train)
19
20# Evaluate the model
21score = model.score(data_test, target_test)
22
23print('R-squared score:', score)

xxxxxxxxxx

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

​

# Load the dataset

dataset_url = 'https://raw.githubusercontent.com/algo-daily/python-tutorial/main/datasets/insurance.csv'

df = pd.read_csv(dataset_url)

​

# Separate the features and target variable

X = df[['age', 'bmi']]

y = df['charges']

​

# Split the data into training and testing sets

data_train, data_test, target_train, target_test = train_test_split(X, y, test_size=0.2, random_state=42)

​

# Create and train the model

model = LinearRegression()

model.fit(data_train, target_train)

​

# Evaluate the model

score = model.score(data_test, target_test)

​

print('R-squared score:', score)