Scikit-learn Developers For Hire

Visualization using d3.js and DataMaps to visualize attack vector within given network. Application takes data from IBM QRadar and with help of RESTful API sent query into d3.js, where it geocodes to lat-ling and places  into the map. Lines are colorized and filtered on front-end for the different level of attack points.

Task:

The company asked to do Data Analysis, data cleaning, data transforming and decision making by using MySQL database.

Solution:

• Data cleaning
• Tables transforming
• Data Frames joining
• Analysis apllying.

Task:

Build a classifier that can find the localization site of a protein in yeast, based on 8 attributes (features).

Solution:

For performing classification there was constructed a 3-layer artifical neural network (ANN) and specially a feed-forward multilayer perception. We have used a stochastic gradient descent with back-propagation to train our ANN.

Task:

Create a reference implementation of Spark MLLib in churn modeling project.

Solution:

We used Mahout Random Forest Classification and Spark MLLib Random Forest Regressions to predict the probability of churn of customers for online-shop.

Task:

Develop web app for predicting phenotypic and environmental characteristics of gram-negative bacterium Escherichia coli (dataset contains 4502 features, the first 6 corresponding to gene ID, strain, medium, environmental and genetic perturbation, and information about the growth rate; the last entries correspond to the expression of all genes in the bacterium).

Solution:

In this project we use a set of 223 transcriptional profiling samples from the gram-negative bacterium Escherichiacoli, which is the well-studied organism with great importance to human healthand biotechnology. We created a predictor of the bacterial growth attribute by using only the expression of the genes as attributes and use a regularized regression technique lasso. Program reports the confidence interval of the prediction by using the bootstrapping method. We created four separate SVM classifiers to categorize the strain type, medium type, environmental and gene perturbation,given all the gene transcriptional profiles and create one composite SVM classifier to simultaneously predict medium and environmental perturbations. This classifier performs worse that the two individual classifiers together for these predictions. And finally the program performs of Principal Component Analysis,keeping only 3 Principal Components as features for the SVM classifier.

Task:

Develop Prediction Model for webspam and hyperlink analysis designed and trained (with provided data) to achieve certain prediction goals.

Solution:

We have built model for Spam\No-spam prediction for links analysis company. We have used Big Data methods for data size of 70+ Gb. There were a lot of text features, which were preprocessed by using TF-IDF, Word2Vec and Features Selecting methods. Various data cleaning and ETL methods were applied. As result we have built classifier model, which was deployed as a RESTful web service

Task:

We had to visualize data from sensors data, stored in MongoDB with help of D3.js visualization library in accordance to user choise made on the dashboard.

Solution:

We took data from database, applied filters on it and presented it in the web UI dashboard.

Task:

To solve the image captchas (recognize numbers and letters from image)

Solution:

We used Machine learning methods in order to solve the captcha images, during the process we wrote program which dissect letters and numbers from the image file and then machine learning classifier applied. We had dataset for training, which contained images with captchas and correct answers. We have used it for training the model. Then we applied the model to test dataset (only images) and have got the results with answers.