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• Topic Modelling usingLatent Dirichlet Allocation with newsgroups20 dataset, implemented with Python and Scikit-Learn
• Implemented a simpleneural network built with Keras on MNIST dataset
• Stock Price Forecasting on Google usingLinear Regression
• Implemented a simple asocial network to learn basics of Python
• ImplementedNaives Bayes Classifier to filter spam messages on SpamAssasin Public Corpus
• Churn Prediction Model for banking dataset using Keras and Scikit-Learn
• ImplementedRandom 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 inStatistics, 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 usingGrammars and Automata Theory; Gibbs Sampling
• Topic Modelling using Latent Dirichlet Analysis (LDA)
• Wrapper for using Scikit-Learn'sGridSearchCV for aKeras Neural Network
• Recommender system usingcosine similarity, recommending new interests to users as well as matching users as per common interests
• Implementing different methods fornetwork analysis such asPageRank, Betweeness Centrality, Closeness Centrality, EigenVector Centrality
• Implementing methods used forHypothesis Inference such asP-hacking, A/B Testing, Bayesian Inference
• ImplementedK-nearest neigbors for next presedential election and prediciting voting behavior based on nearest neigbors.

MLwP is built using Python 3.5. The easiest way to set up a compatibleenvironment is to useConda. This will set up a virtualenvironment with the exact version of Python used for development along with all thedependencies needed to run MLwP.

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

(Note: entercd ~ 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-testadded / updated specs:  - python=3.5The 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: doneVerifying transaction: doneExecuting transaction: done## To activate this environment, use:# > source activate mlwp-test## To deactivate an active environment, use:# > source deactivate#

The environment is successfully created.

3. Now activate the Conda environment.

   source activate *your env name*

   You will get the following

   (mlwp-test) amogh@hp15X34:~$

   Enterconda 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  Channelca-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

4. Install the required dependencies.

   (mlwp-test) amogh@hp15X34:~$ conda install --yes --file *path to requirements.txt*

5. In case you are not able to install the packages or gettingPackagesNotFoundErrorUse the following command conda install -c conda-forge *list of packages separated by space*. For more info, refer issue#3Unable to install requirements

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

• FAQs (coming soon)
• Documentation (coming soon)

Feel free to submit issues and enhancement requests.

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.

1. Fork the repo on GitHub
2. Clone the project to your own machine
3. Commit changes to your own branch
4. Push your work back up to your fork
5. Submit aPull request so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!