Scikit-learn is a popular open-source machine learning library for Python. It provides a range of supervised and unsupervised learning algorithms.
1. Install scikit-learn:
To install scikit-learn, use one of the following commands:
pip install scikit-learn
conda install scikit-learn
2. Import the library:
To use scikit-learn, you need to import it in your Python code. This is done using the following command:
3. Load the data:
The next step is to load the data into your Python environment. This can be done using the pandas library, which is a popular data manipulation library.
import pandas as pd
data = pd.read_csv(‘data.csv’)
4. Pre-process the data:
Before you can use the data for machine learning, you need to pre-process it. This involves cleaning the data, handling missing values, and transforming the data into a format that is suitable for machine learning.
5. Split the data:
Once the data is pre-processed, it needs to be split into training and test datasets. This is done using the train_test_split() function from scikit-learn.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
6. Build the model:
Once the data is split, you can build the machine learning model. Scikit-learn provides a range of supervised and unsupervised learning algorithms.
from sklearn.linear_model import LinearRegression
model = LinearRegression()
7. Train the model:
Once the model is built, it needs to be trained on the training data. This is done using the fit() function.
8. Evaluate the model:
Once the model is trained, it needs to be evaluated on the test data. This is done using the score() function.
score = model.score(X_test, y_test)
9. Make predictions:
Finally, the model can be used to make predictions on new data. This is done using the predict() function.
predictions = model.predict(X_new)