Travis CI: Test on Python 3.9 release candidate 2

.travis.yml

@@ -1,7 +1,14 @@
 os: linux
 dist: focal
+addons:  # TEMPORARY: Just for Python 3.9-rc2
+  apt:
+    update: true
+    packages:
+      - python3-keras
+      - python3-pandas
+      - python3-sklearn
 language: python
-python: 3.8
+python: 3.9-dev
 cache: pip
 before_install: pip install --upgrade pip setuptools six
 install: pip install black flake8

DIRECTORY.md

@@ -319,6 +319,7 @@
   * [Data Transformations](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/data_transformations.py)
   * [Decision Tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py)
   * [Gaussian Naive Bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gaussian_naive_bayes.py)
+  * [Gradient Boosting Regressor](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_regressor.py)
   * [Gradient Descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py)
   * [K Means Clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_nearest_neighbours.py)

machine_learning/gradient_boosting_regressor.py

@@ -3,11 +3,11 @@
    predict house price.
 """
 
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 from sklearn.datasets import load_boston
-from sklearn.metrics import mean_squared_error, r2_score
 from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import mean_squared_error, r2_score
 from sklearn.model_selection import train_test_split
 
 
@@ -42,10 +42,7 @@ def main():
     training_score = model.score(X_train, y_train).round(3)
     test_score = model.score(X_test, y_test).round(3)
     print("Training score of GradientBoosting is :", training_score)
-    print(
-        "The test score of GradientBoosting is :",
-        test_score
-    )
+    print("The test score of GradientBoosting is :", test_score)
     # Let us evaluation the model by finding the errors
     y_pred = model.predict(X_test)
 
@@ -57,8 +54,7 @@ def main():
     # So let's run the model against the test data
     fig, ax = plt.subplots()
     ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
-    ax.plot([y_test.min(), y_test.max()],
-            [y_test.min(), y_test.max()], "k--", lw=4)
+    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
     ax.set_xlabel("Actual")
     ax.set_ylabel("Predicted")
     ax.set_title("Truth vs Predicted")

requirements.txt

@@ -2,19 +2,19 @@ beautifulsoup4
 black
 fake_useragent
 flake8
-keras
+keras; python_version <= '3.8'
 lxml
 matplotlib
 mypy
 numpy
 opencv-python
-pandas
+pandas; python_version <= '3.8'
 pillow
 pytest
 pytest-cov
 requests
-scikit-fuzzy
-sklearn
+scikit-fuzzy; python_version <= '3.8'
+sklearn; python_version <= '3.8'
 sympy
-tensorflow
-xgboost
+tensorflow; python_version <= '3.8'
+xgboost; python_version <= '3.8'