Update project for 0.0.3

Author: frederikhoengaard

README.md

@@ -5,6 +5,18 @@
 
 The aim of lazy-learn is exactly that. Given a dataset, easy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference. 
 
+## Upcoming features
+
+Current stable version is 0.0.3. The upcoming updates will support:
+- Abstract construction of model architectures
+- XGBoost, LightGBM, Adaboost and Catboost architectures
+- Time partitioning of datasets
+- Automated Hyperparameter Optimisation (HPO)
+- Text features
+- An interface to AutoGluon
+- Outlier detection and handling
+- Automated suggestions of performance metrics
+
 ## Usage
 
 Using lazy-learn revolves around the `LazyLearner` class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn.
@@ -17,6 +29,7 @@ lazy-learn requires:
 
 - pandas
 - scikit-learn
+- xgboost
 
 ### User Installation 
 ```

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "lazylearn"
-version = "0.0.2"
+version = "0.0.3"
 authors = [
   { name="Frederik P. Høngaard", email="mail@frederikhoengaard.com" },
 ]