link checks

lecture17-basic-spatial-viz/README.md

@@ -45,6 +45,11 @@ After successfully completing [`02_spatial_datavisualisation.ipynb`](https://git
   - Use nice color maps with unique palettes
   - Task for Vienna: replicate the same as for London
 
+After successfully completing [`02_spatial_datavisualisation_plotly.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture17-basic-spatial-viz/02_spatial_datavisualisation_plotly.ipynb) students should be able:
+
+  - Use`plotly`'s 'choropleth_mapbox' function to create interactive maps. 
+
+
 ## Lecture Time
 
 Ideal overall time: **40-60 mins**.

lecture19-lasso/README.md

@@ -15,7 +15,7 @@ Case study:
   - [Chapter 14, B: Predicting AirBnB apartment prices: selecting a regression model](https://gabors-data-analysis.com/casestudies/#ch14b-predicting-airbnb-apartment-prices-selecting-a-regression-model)
 
 ## Learning outcomes
-After successfully completing [`lasso_aribnb.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture19-lasso/codes/lasso_aribnb.ipynb), students should be able:
+After successfully completing [`02_lasso_airbnb_prediction.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture19-lasso/02_lasso_airbnb_prediction.ipynb), students should be able:
 
   - Data cleaning and refactoring to prepare for LASSO type modelling
   - Basic feature engineering for LASSO

lecture21-random-forest/README.md

@@ -21,7 +21,7 @@ Case study:
 
 Lecturer/students should be aware that there is a separate file: [`airbnb_prepare.R`](https://github.com/gabors-data-analysis/da-coding-rstats/blob/main/lecture24-random-forest/codes/airbnb_prepare.R) for this seminar, overviewing only the data cleaning and feature engineering process. This is extremely important and powerful to understand how to prepare the data for these methods, as without it data analysts do garbage-in garbage-out analysis... Usually, due to time constraints, this part is not covered in the seminar but asked students to cover it before the seminar.
 
-After successfully completing [`randomforest_airbnb.R`](https://github.com/gabors-data-analysis/da-coding-rstats/blob/main/lecture24-random-forest/codes/randomforest_airbnb.R), students should be able:
+After successfully completing [`02_random_forest_airbnb.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture21-random-forest/02_random_forest_airbnb.ipynb), students should be able:
 
   - Estimate random forest models via `sklearn`
     - unsderstand `max_features` and `min_samples_split` parameters

lecture22-classification/README.md

@@ -19,7 +19,7 @@ Case study:
 
 Lecturer/students should be aware that there is a separate file at the official case studies repository: [`ch17-firm-exit-data-prep.ipynb`](https://github.com/gabors-data-analysis/da_case_studies/blob/master/ch17-predicting-firm-exit/ch17-firm-exit-data-prep.ipynb) for this seminar, overviewing only the data cleaning and feature engineering process for binary outcomes. This is extremely important and powerful to understand how to prepare the data for these methods, as without it data analysts do garbage-in garbage-out analysis... Usually, due to time constraints, this part is not covered in the seminar but asked students to cover it before the seminar.
 
-After successfully completing [`firm_exit_classification.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture22-classification/codes/firm_exit_classification.ipynb), students should be able:
+After successfully completing [`firm_exit_classification.ipynb`](https://github.com/gabors-data-analysis/da-coding-python/blob/main/lecture22-classification/firm_exit_classification.ipynb), students should be able:
 
   - What is winsorizing and how it helps
   - Basic linear models for predicting probabilities