Fixed with correct link

README.md

@@ -45,7 +45,7 @@
 NannyML is an open-source python library that allows you to **estimate post-deployment model performance** (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and **regression**.
 
 The core contributors of NannyML have researched and developed multiple novel algorithms for estimating model performance: [confidence-based performance estimation (CBPE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#confidence-based-performance-estimation-cbpe) and [direct loss estimation (DLE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#direct-loss-estimation-dle).
-The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html) using PCA-based data reconstruction.
+The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca) using PCA-based data reconstruction.
 
 If you like what we are working on, be sure to become a Nanster yourself, join our [community slack](https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ) <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/slack.png" height='15'> and support us with a GitHub <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/github.png" height='15'> star ⭐.