devAmoghS/Machine-Learning-with-Python

Small scale machine learning projects to understand the core concepts


repo name	devAmoghS/Machine-Learning-with-Python
repo link	https://github.com/devAmoghS/Machine-Learning-with-Python
homepage
language	Python
size (curr.)	755 kB
stars (curr.)	555
created	2018-03-02
license	MIT License

Machine-Learning-with-Python

alt text

Small scale machine learning projects to understand the core concepts (order: oldest to newest)

Topic Modelling using Latent Dirichlet Allocation with newsgroups20 dataset, implemented with Python and Scikit-Learn
Implemented a simple neural network built with Keras on MNIST dataset
Stock Price Forecasting on Google using Linear Regression
Implemented a simple a social network to learn basics of Python
Implemented Naives Bayes Classifier to filter spam messages on SpamAssasin Public Corpus
Churn Prediction Model for banking dataset using Keras and Scikit-Learn
Implemented Random Forest from scratch and built a classifier on Sonar dataset from UCI repository
Simple Linear Regression in Python on sample dataset
Multiple Regression in Python on sample dataset
PCA and scaling sample stock data in Python [working_with_data]
Decision Trees in Python on sample dataset
Logistic Regression in Python on sample dataset
Built a neural network in Python to defeat a captcha system
Helper methods include commom operations used in Statistics, Probability, Linear Algebra and Data Analysis
K-means clustering with example data; clustering colors with k-means; Bottom-up Hierarchical Clustering
Generating Word Clouds
Sentence generation using n-grams
Sentence generation using Grammars and Automata Theory; Gibbs Sampling
Topic Modelling using Latent Dirichlet Analysis (LDA)
Wrapper for using Scikit-Learn’s GridSearchCV for a Keras Neural Network
Recommender system using cosine similarity, recommending new interests to users as well as matching users as per common interests
Implementing different methods for network analysis such as PageRank, Betweeness Centrality, Closeness Centrality, EigenVector Centrality
Implementing methods used for Hypothesis Inference such as P-hacking, A/B Testing, Bayesian Inference
Implemented K-nearest neigbors for next presedential election and prediciting voting behavior based on nearest neigbors.

Installation notes

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.

Download and install Conda.
Create a Conda environment with Python 3.

(Note: enter cd ~ to go on $HOME , then perform these commands)

```
conda create --name *your env name* python=3.5
```

You will get the following, mlwp-test is the env name used in this example

Solving environment: done

## Package Plan ##

environment location: /home/user/anaconda3/envs/mlwp-test

added / updated specs: 
 - python=3.5


The following NEW packages will be INSTALLED:

 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      

Proceed ([y]/n)?  *Press y*

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate mlwp-test
#
# To deactivate an active environment, use:
# > source deactivate
#

The environment is successfully created.

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

How good is the code ?

It is well tested
It passes style checks (PEP8 compliant)
It can compile in its current state (and there are relatively no issues)

How much support is available?

FAQs (coming soon)
Documentation (coming soon)

Issues

Feel free to submit issues and enhancement requests.

Contributing

Please refer to each project’s style guidelines and guidelines for submitting patches and additions. In general, we follow the “fork-and-pull” Git workflow.