Update project_report.md

After extracting the knowledgepaths, we found that the neural model of @DebjitPaul was not immediately executable without errors. Again there were a lot of version problems, the versions given in the [gitlab]() are outdated and every version change caused a problem with another package. Since we didn't want to invest all our time again in finding out the correct versions of another project and @debjitpaul couldn't help either, we decided to develop our own method for this. Since the knowledgepaths are already returned according to their expressiveness, we used them in *humans_needs_assginer.py* by using and assigning the human needs of the first path we found. 

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#### evaluation of results

#### Evaluation of results

After the human needs have been successfully assigned for all essays, we evaluate the whole thing. In *human_needs_evaluation.py" we have written a code for evaluation. Here we calculate Precision, Accuracy, Recall and F1-Score and with these measurements we analyze the evaluation. Concrete comments and visualisations of the evaluation can be found in the jupyter notebook. 

<p align="center">

  <img src="pictures/Accuracy.png" />

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<p align="center">

  <img src="pictures/Recall_test.png" />

</p>

<p align="center">

  <img src="pictures/Precision_test.png" />

</p>

<p align="center">

  <img src="pictures/Matrix_test_1.png" />

</p>

<p align="center">

  <img src="pictures/Matrix_test_2.png" />

</p>

<p align="center">

  <img src="pictures/Classification_Report_test.png" />

</p>

<p align="center">

  <img src="pictures/F1_test.png" />

</p>

<p align="center">

  <img src="pictures/Matrix_test_3.png" />

</p>

<p align="center">

  <img src="pictures/Matrix_4_test.png" />

</p>

## The Tools and how they worked

### Problems that accured