Some important docs reviewed:
- np.where: https://numpy.org/doc/2.1/reference/generated/numpy.where.html
- np.log: https://www.educative.io/answers/what-is-numpylog-in-python
- sns.displot: https://seaborn.pydata.org/generated/seaborn.displot.html
- sns.boxplot: https://seaborn.pydata.org/generated/seaborn.boxplot.html
- df.quantile: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html
- df.loc: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html
- sns.histplot: https://seaborn.pydata.org/generated/seaborn.histplot.html#seaborn.histplot
- df.map: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.map.html
- plt.figure: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html
- df.corr: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html
- sns.heatmap: https://seaborn.pydata.org/generated/seaborn.heatmap.html
- sc.fit_transform: https://www.educative.io/answers/what-is-the-use-of-fittransform-from-sklearn-in-python
- train_test_split(): https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.train_test_split.html
- sklearn.metrics.accuracy_score: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score
- sklearn.metric.confusion_matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
- sklearn.metrics.classification_report: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
- precision: Of all the instances predicted as positive, what proportion was actually positive?
TP / (TP + FP). - recall: Of all the instances that were actually positive, what proportion was correctly predicted as positive?
TP / (TP + FN). Also known as sensitivity or true positive rate. - f1-score: The harmonic mean of precision and recall. It balances precision and recall and is useful when there's an uneven class distribution.
2 * (precision * recall) / (precision + recall). - LogisticRegression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
- df.drop: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html
- df.shift: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shift.html
- np.array: https://numpy.org/doc/2.1/reference/generated/numpy.array.html
- SVM: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html
- SVR-RBF: https://www.geeksforgeeks.org/support-vector-regression-svr-using-linear-and-non-linear-kernels-in-scikit-learn/
- Linear Regression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
- Cross val score: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html
- Ridge Regression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html
- GridSearchCV: https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html
- Lasso Regression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html
- Pillow: https://pillow.readthedocs.io/en/stable/
- np.concatenate: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
- VGG16: https://keras.io/api/applications/vgg/ , https://builtin.com/machine-learning/vgg16
- GlobalAveragePooling2D: https://keras.io/api/layers/pooling_layers/global_average_pooling2d/
- Conv2D: https://keras.io/api/layers/convolution_layers/convolution2d/
- Dense: https://keras.io/api/layers/core_layers/dense/
- ExponentialDecay: https://keras.io/api/optimizers/learning_rate_schedules/exponential_decay/
- RMSProp: https://keras.io/api/optimizers/rmsprop/
- BinaryAccuracy: https://keras.io/api/metrics/accuracy_metrics/
- tfds.load: https://www.tensorflow.org/datasets/api_docs/python/tfds/load
- gnews-swivel: https://www.kaggle.com/models/google/gnews-swivel/tensorFlow2/tf2-preview-20dim/1?tfhub-redirect=true
- Keras model.fit and model.compile: https://keras.io/api/models/model_training_apis/
- Loss v Accuracy: https://www.geeksforgeeks.org/what-is-the-relationship-between-the-accuracy-and-the-loss-in-deep-learning/
- RandomForest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
- np.linspace: https://numpy.org/doc/2.1/reference/generated/numpy.linspace.html
- RandomizedSearchCV: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
- hyperopt: https://hyperopt.github.io/hyperopt/