{"id":21759095,"url":"https://github.com/gallo13/neuralnetworks-deeplearning-stats-classification","last_synced_at":"2026-04-17T15:07:59.937Z","repository":{"id":189795169,"uuid":"299460540","full_name":"Gallo13/NeuralNetworks-DeepLearning-Stats-Classification","owner":"Gallo13","description":"Descriptive Statistics, Classification and Analysis Using Python \u0026 Python Libraries (Assignment 1)","archived":false,"fork":false,"pushed_at":"2020-12-18T02:51:21.000Z","size":690,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-26T00:41:29.076Z","etag":null,"topics":["analysis","data","datasets","deep-learning","jupyter-notebook","matplotlib","neural-networks","numpy","pandas","plotting","python","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Gallo13.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-09-29T00:08:23.000Z","updated_at":"2022-03-02T00:56:38.000Z","dependencies_parsed_at":"2023-08-21T20:37:32.804Z","dependency_job_id":null,"html_url":"https://github.com/Gallo13/NeuralNetworks-DeepLearning-Stats-Classification","commit_stats":null,"previous_names":["gallo13/neuralnetworks-deeplearning-stats-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gallo13%2FNeuralNetworks-DeepLearning-Stats-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gallo13%2FNeuralNetworks-DeepLearning-Stats-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gallo13%2FNeuralNetworks-DeepLearning-Stats-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gallo13%2FNeuralNetworks-DeepLearning-Stats-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Gallo13","download_url":"https://codeload.github.com/Gallo13/NeuralNetworks-DeepLearning-Stats-Classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244734196,"owners_count":20501018,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data","datasets","deep-learning","jupyter-notebook","matplotlib","neural-networks","numpy","pandas","plotting","python","seaborn"],"created_at":"2024-11-26T11:25:29.445Z","updated_at":"2026-04-17T15:07:54.917Z","avatar_url":"https://github.com/Gallo13.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural Networks \u0026 Deep Learning: Classification\nDescriptive Statistics, Classification and Analysis Using Python \u0026amp; Python Libraries\n\nUsed the Wine Quality Dataset\n\nPart 1\nPlotting using matplotlib only\n  Listing 1: \n    a) Load libraries\n    b) Load the dataset\n  Listing 2:\n    a) Print the shape of the dataset\n    b) Print the first few rows of the dataset\n    c) Print the statistical descriptions of the dataset\n    d) Print the class distribution in the dataset\n  Listing 3:\n    a) Univariate Plot\n    b) Visualize the dataset using histogram plots\n    c) Visualize the dataset using scatter plots\n  Listing 4:\n    a) Create validation set\n    b) Build models (Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-Nearest \n       Neighbors (KNN), Classifications and Regressions Trees (CART), Gaussian Naive Bayes (NB), \n       Support Vector Machines (SVM) and select the best mdoel.\n    c) Compare algorithms\n  Listing 5:\n    - Make predictions on the validation dataset\n\nPart 2:\nPlotting using matplotlib only\n  Listing 6:\n  - Pairwise Pearson Correlation\n  - Skew for Each Attribute\n  - Univariate Density Plot\n  - Correlation Matrix Plot\n\n  Listing 7:\n  - Rescaling Data\n  - Standardize Data\n  - Normalize Data\n  - Binarize Data\n  - Analysis after each listing\n\nPart 3\nComplete any 8 calculations and plotting using seaborn packages which are not included into the previous calculations and plotting with matplotlib. Four of these should be related to PCA.\n  Listing 8:\n  - (need to continue)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgallo13%2Fneuralnetworks-deeplearning-stats-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgallo13%2Fneuralnetworks-deeplearning-stats-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgallo13%2Fneuralnetworks-deeplearning-stats-classification/lists"}