Update README.md

README.md

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 Churn prediction is a classic problem in Machine Learning. A quick survey of Kaggle shows numerous churn prediction competitions being hosted every year by data-rich companies such as online retailers and network providers. Most of these competitions are won by black-box models such as highly tuned Random Forests or the all-time classic XGBoost. Although they achieve state- of-the-art performance in terms of classification accuracy, they leave little room for interpretability. They can make individual predictions with high confidence but fall apart when someone starts probing the whys. The aim of this project is two-fold: (i) to describe a whole suite of models that preserve both global interpretability and local explainability, and (ii) to make the case that interpretable models are the best way to gain actionable insight into the data they are trained on, and why one might accept a slight drop in predictive capability in favor of interpretability.
 
-The complete report can be found in a PDF [here](./Interpretable%20Churn%20Prediction%20Report.pdf).
+The complete report can be found in a PDF [here](./Interpretable_Churn_Prediction_Report.pdf).
 
 ## Data
 
@@ -93,4 +93,4 @@ Although the predictive performance of interpretable models did not match that o
 
 [10.] Wang, Y., Huang, H., Rudin, C., & Shaposhnik, Y. (2021). Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP 
 
-[11.] for Data Visualization. Journal of Machine Learning Research, 22, 1-73.
+[11.] for Data Visualization. Journal of Machine Learning Research, 22, 1-73.