Machine-Learning-with-Python | Small scale machine learning projects to understand | Machine Learning library

by devAmoghS Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Machine-Learning-with-Python Summary

Machine-Learning-with-Python is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. Machine-Learning-with-Python has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Small scale machine learning projects to understand the core concepts . Give a Star If it helps you. BONUS: Interview Bank coming up..!

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Machine-Learning-with-Python has a medium active ecosystem.

It has 1147 star(s) with 187 fork(s). There are 46 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 12 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Machine-Learning-with-Python is current.

Quality

Machine-Learning-with-Python has 0 bugs and 0 code smells.

Security

Machine-Learning-with-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Machine-Learning-with-Python code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Machine-Learning-with-Python is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Machine-Learning-with-Python releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Machine-Learning-with-Python saves you 1253 person hours of effort in developing the same functionality from scratch.

It has 2818 lines of code, 273 functions and 55 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Machine-Learning-with-Python and discovered the below as its top functions. This is intended to give you an instant insight into Machine-Learning-with-Python implemented functionality, and help decide if they suit your requirements.

Get shortest paths from from_user to_from_from_from_user
Make a scatterplot matrix
Compute the cdf of a normal distribution
Return the number of rows and columns of A
Build a tree id3
Group rows by group
Return a partition by a given attribute
Train and test the model
Get subject information from a file
Split data into two lists
Minimize a stochastic
Splits a tree
Plot the cities of the languages
Evaluate an algorithm
Minimize an error function
Find the bottom - up cluster of inputs
Classify and plot a grid and plot it
Show the weights of a network
Back - propagation function
Feed forward forward computation
Make graph dot product projection
Make a histogram plot
Perform the feed - forward computation
Classify a decision tree
Plot the derivative
Return a sorted list of suggested suggestions
Returns a list of all words in the article

Get all kandi verified functions for this library.

Machine-Learning-with-Python Key Features

No Key Features are available at this moment for Machine-Learning-with-Python.

Machine-Learning-with-Python Examples and Code Snippets

No Code Snippets are available at this moment for Machine-Learning-with-Python.

Community Discussions

Trending Discussions on Machine-Learning-with-Python

How to visualize categorical frequency difference

QUESTION

How to visualize categorical frequency difference

Asked 2021-Oct-24 at 05:14

Data: Diabetes dataset found here: https://raw.githubusercontent.com/LahiruTjay/Machine-Learning-With-Python/master/datasets/diabetes.csv

Objective: I want to examine how many people under the Age of 30 have diabetes, which is indicated by a 1 or 0 in the "Outcome" column of the dataset and plot it to see if there is a class imbalance (more of 1 or more of 2 or roughly equal?)

Method:

Filter my dataset as such:

...

ANSWER

Answered 2021-Oct-24 at 03:28

The difference in 'Outcome' for each 'Age' can most easily be seen with a bar plot showing the count, which can be done directly with a seaborn.countplot, or calculating the counts in pandas, and plotting with pandas.DataFrmame.plot.
Tested in python 3.8.12, pandas 1.3.3, matplotlib 3.4.3, seaborn 0.11.2

Data and Imports

Source https://stackoverflow.com/questions/69693096

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Machine-Learning-with-Python

MLwP is built using Python 3.5. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run MLwP. (Note: enter cd ~ to go on $HOME , then perform these commands). You will get the following, mlwp-test is the env name used in this example. The environment is successfully created.
Download and install Conda.
Create a Conda environment with Python 3.
Now activate the Conda environment. source activate *your env name* You will get the following (mlwp-test) amogh@hp15X34:~$ Enter conda list to get the list of available packages (mlwp-test) amogh@hp15X34:~$ conda list # packages in environment at /home/amogh/anaconda3/envs/mlwp-test: # # Name Version Build Channel ca-certificates 2018.12.5 0 certifi 2018.8.24 py35_1 libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 8.2.0 hdf63c60_1 libstdcxx-ng 8.2.0 hdf63c60_1 ncurses 6.1 he6710b0_1 openssl 1.0.2p h14c3975_0 pip 10.0.1 py35_0 python 3.5.6 hc3d631a_0 readline 7.0 h7b6447c_5 setuptools 40.2.0 py35_0 sqlite 3.26.0 h7b6447c_0 tk 8.6.8 hbc83047_0 wheel 0.31.1 py35_0 xz 5.2.4 h14c3975_4 zlib 1.2.11 h7b6447c_3
Install the required dependencies. (mlwp-test) amogh@hp15X34:~$ conda install --yes --file *path to requirements.txt*
In case you are not able to install the packages or getting PackagesNotFoundError Use the following command conda install -c conda-forge *list of packages separated by space*. For more info, refer issue #3 Unable to install requirements