https://github.com/gallo13/neuralnetworks-deeplearning-stats-classification

Descriptive Statistics, Classification and Analysis Using Python & Python Libraries (Assignment 1)
https://github.com/gallo13/neuralnetworks-deeplearning-stats-classification

analysis data datasets deep-learning jupyter-notebook matplotlib neural-networks numpy pandas plotting python seaborn

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Descriptive Statistics, Classification and Analysis Using Python & Python Libraries (Assignment 1)

• Host: GitHub
• URL: https://github.com/gallo13/neuralnetworks-deeplearning-stats-classification
• Owner: Gallo13
• Created: 2020-09-29T00:08:23.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-12-18T02:51:21.000Z (about 4 years ago)
• Last Synced: 2023-08-21T20:23:09.290Z (over 1 year ago)
• Topics: analysis, data, datasets, deep-learning, jupyter-notebook, matplotlib, neural-networks, numpy, pandas, plotting, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 674 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Neural Networks & Deep Learning: Classification

Descriptive Statistics, Classification and Analysis Using Python & Python Libraries

Used the Wine Quality Dataset

Part 1

Plotting using matplotlib only

  Listing 1: 

    a) Load libraries

    b) Load the dataset

  Listing 2:

    a) Print the shape of the dataset

    b) Print the first few rows of the dataset

    c) Print the statistical descriptions of the dataset

    d) Print the class distribution in the dataset

  Listing 3:

    a) Univariate Plot

    b) Visualize the dataset using histogram plots

    c) Visualize the dataset using scatter plots

  Listing 4:

    a) Create validation set

    b) Build models (Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-Nearest 

       Neighbors (KNN), Classifications and Regressions Trees (CART), Gaussian Naive Bayes (NB), 

       Support Vector Machines (SVM) and select the best mdoel.

    c) Compare algorithms

  Listing 5:

    - Make predictions on the validation dataset

Part 2:

Plotting using matplotlib only

  Listing 6:

  - Pairwise Pearson Correlation

  - Skew for Each Attribute

  - Univariate Density Plot

  - Correlation Matrix Plot

  Listing 7:

  - Rescaling Data

  - Standardize Data

  - Normalize Data

  - Binarize Data

  - Analysis after each listing

Part 3

Complete 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.

  Listing 8:

  - (need to continue)