Position: Machine Learning Engineer / Data Scientist
Category: Data Science
English level: upper
Years of experience: 2

Completed **Data Science Certification at DataCamp**.
 
It consists of 3 steps: 

 1. Pass 4 tests:
    - Python Coding
    - Statistics
    - PostgreSQL
    - Model Development

 2. Coding Challenge
 The task was to transform raw data into a specific form in order to fit the shown example.

 3. Case study
  I was given a project and had 24 hours to finish it and then present findings to a person who has no technical data science background. Then my project has been reviewed and graded by data science experts.
Trainee Data Scientist (7 months) 
- Write SQL queries in order to get required data.
- Explore DataScience pipeline (from Business Understanding to Deployment). 
- Project 'Analysis of open data': Web-Scraping, Data analysis, Data cleaning/transformation,Feature Engineering.
- Lead ML Engineer role for NLP Model Development (Task Definition and Description + Development).

Projects:

| NLP with Disaster Tweets
Made feature engineering and interesting plots. 
Used Spell Correction and Stemming techniques on text.
Wrote own classes which split data into numerical and category; transform text using built-in pipeline with CountVectorizer, TfidfTransformer and PCA; transform ordinal features using OneHotEncoder.
Finally wrote LSTM nn using PyTorch and put everything in Pipeline. 

| Digit Recognizer
The aim was to correctly identify digits from handwritten images.
Made CNN model with final accuracy 98.5% using Keras.

|  Bank Clients
- The main idea is to predict if client subscribed a term deposit (Binary).
- I wrote own classes in order to process data. Finally made pipeline with Random Forest and used GridSearchCV to find the best parameters. 
- As a result, got a model which can receive raw data, prepare it by itself and return predictions.

|  Used Cars Sales
 Predict car price which should be in range +- 1500£ from competitors price. Made own metrics to evaluate model output which finds the percent of cars which would be sold and percent of cars which would be sold for a good price - not too low (means lose money) and not too high (if price is too high customers will prefer our competitors).

| HousePrice 
Created visualisations using Plotly.
Then prepared dataset and wrote function which finds MSE, RMSE and R^2 for train and test and returns scores dataset with a new model performance observation. 
Finally used Keras to create a Sequential Neural Network.

| FakeNewsDetection
University project, which will help people to avoid fake news.
Created NLP model in order to classify news.

|  Other projects
- Motorbike Ambulance Calls: How many ambulance calls will be per day depending on weather conditions, precipitation, time and season.
- Mall Customers: Cluster customers by their age , income and spendings.
- Ukrainian Coffee Shop Market: Analyse and cluster existing coffee shop market.
- Iris Clustering: Clustered data using KMeans and created own metrics to evaluate model (also prepared a picture of how it works).