To predict links between records using trained models in the context of the provided project, several options are available. The key technologies and dependencies for this task include Jupyter Notebook and JupyterLab, scikit-learn, and splink.
- Scikit-learn: This machine learning library for Python provides various algorithms for classification, regression, and clustering. It can be used to train models on a dataset and then make predictions on new data.
Example:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Assume X is the feature matrix and y is the target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
(Source: Scikit-learn tutorial: How to implement linear regression)
- splink: This is a Python library for linking and deduplicating records in datasets. It can be used to predict links between records based on their features.
Example:
from splink.scoring import JaroWinkler
from splink.models import CompoundPermutationModel
# Assume df is the DataFrame containing the records
model = CompoundPermutationModel(df,
blocking_rule='block1',
training_data_ratio=0.7,
em_iterations=10,
link_type='soft',
similarity_function=JaroWinkler())
predictions = model.predict()
(Source: tutorials/05_Predicting_results.ipynb)
These are just a few examples of how to predict links between records using trained models in the context of the provided project. The specific method to use will depend on the problem at hand and the nature of the data.
