diff --git a/README.md b/README.md index de9e3d0..50cac79 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@

- +

@@ -59,7 +59,7 @@ ad = AnalyticsDataframe(1000, 6) ad.predictor_matrix.head() ``` -![Initialized Predictor Matrix](https://github.com/Faye-yufan/analytics-dataset/blob/edit-readme/docs/images/initialized-predictor-matrix.png) +![Initialized Predictor Matrix](https://github.com/Faye-yufan/analytics-dataset/blob/main/docs/images/initialized-predictor-matrix.png) The predictor matrix is initialized with all null values. Now let's update the predictors with some distributions: @@ -70,7 +70,7 @@ for var in ['X1', 'X2', 'X3', 'X4', 'X5']: ad.update_predictor_categorical('X6', ["Red", "Yellow", "Blue"], [0.3, 0.4, 0.3]) ``` -![Updated Predictor Matrix](https://github.com/Faye-yufan/analytics-dataset/blob/edit-readme/docs/images/updated-predictor-matrix.png) +![Updated Predictor Matrix](https://github.com/Faye-yufan/analytics-dataset/blob/main/docs/images/updated-predictor-matrix.png) Once we have a dataframe desired and would like to visualize it, we can do: @@ -78,7 +78,7 @@ Once we have a dataframe desired and would like to visualize it, we can do: df_visualization_bi(ad) ``` -![Bivariate Visualization Chart](https://github.com/Faye-yufan/analytics-dataset/blob/edit-readme/docs/images/bivariate-vis.png) +![Bivariate Visualization Chart](https://github.com/Faye-yufan/analytics-dataset/blob/main/docs/images/bivariate-vis.png) # Next Steps @@ -86,7 +86,7 @@ We plan to integrate an user interface to the library, aiming to let users confi ## Code Contributors -![Contributors](https://github.com/Faye-yufan/analytics-dataset/blob/edit-readme/docs/images/contributors.png) +![Contributors](https://github.com/Faye-yufan/analytics-dataset/blob/main/docs/images/contributors.png) ## License AutoGen is released under the MIT License.