update

README.md

@@ -108,10 +108,12 @@ Through this process, Emma was able to leverage our project to generate meaningf
 
 ## Progress
 - **Data was searched and found at : (https://doi.org/10.13026/wgex-er52, last visit: 15.05.2024)**
-- **[Data was cleaned](#Datacleaning)**
-- **[Demographic data was plotted](#Demographicplots)**
-- **[Hypotheses put forward](#Hypotheses)**
-- **[Noise reduction](#Noisereduction)**
+- **[Data was cleaned](#data-cleaning)**
+- **[Demographic data was plotted](#demographic-plots)**
+- **[Hypotheses put forward](#hypotheses)**
+- **[Noise reduction](#noise-reduction)**
+- **[Features](#features)**
+- **[ML-models](#ml-models)**
 
 
 ### Data cleaning
@@ -188,11 +190,15 @@ With those Classifiers, the hypothesis can be proven, that a classifier is able
     Besides that, older people are more likely to receive medical support such as medication and pacemakers which can prevent sinus bradycardia or at least lower its effect. 
     The sample size in the study conducted may also play a role in the significance of the frequency.
 
-## Noise reduction
+### Noise reduction
 Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise.
 
 How the noise reduction was performed in detail can be seen in the following notebook: [noise_reduction.ipynb](notebooks/noise_reduction.ipynb)
 
+### Features
+
+### ML-models
+
 
 ## Contributing
 Thank you for your interest in contributing to our project! As an open-source project, we welcome contributions from everyone. Here are some ways you can contribute: