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choose-optimal-threshold-for-imbalanced-dataset

ROC curve for finding the optimal threshold: A receiver operating characteristics or known as ROC curve is a two-dimensional plot that illustrates how well a classifier system works as the discrimination cut-off value is changed over the range of the predictor variable. The X-axis or independent variable is the false positive rate for the predictive test. The Y-axis or dependent variable is the true positive rate for the predictive test. It is relevant to note that the nearer to the upper-left side of ROC space, the better a classifier is.

cs229-python

Exercises and solutions to Stanford CS229 Machine Learning in Python

data-analysis-of-coursera-courses

detecting_multicollinearity

docker

face-mask-detection

feature-engineering

feature-engineering-cardinality

feature-selection-using-wrapper-method

Step Forward, Step Backward and Exhaustive Feature Selection_Wrapper Method

feature-selection-with-filtering-method-constant-quasi-constant-and-duplicate-feature-removal

Feature Selection with Filtering Method | Constant, Quasi Constant and Duplicate Feature Removal

handling-imbalanced-data

k_means-_clustering

matplotlib

missing-value-imputation

handling_missing_values_by delateting rows or columns

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

mlflow

mlfundamentals

Notebooks for the "ML from the Fundamentals" series

opencv-tutorial

pca

Here is a detailed explanation of PCA technique which is used for dimesnionality reduction using sklearn and python

plotly-and-cufflink

post_pruning_decisiontree

DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a ccp_alpha based on validation scores.

python

All Algorithms implemented in Python

python_for_image_processing_apeer

https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

seaborn

silhouette-clustering-

stackoverflow