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Learning every day
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Learning every day
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KonovalovaDS/README.md

Good Day to Everyone

My name is Natalia - passionate Data Scientist and Photography Enthusiast

As a Data Science Specialist, I am passionate about working with (big) data and applying machine learning techniques to solve real-world problems. Currently I am in the process to complete a Data Science program. I am confidently using the following libraries: Numpy, Pandas, Matplotlib, SciPy & Scikit-learn, Seaborn, Plotly and others. I know data structures, principles of OOP and modular programming; understand and use ML algorithms such as Linear & Logistics Regression, Decision Tree algorithm, Random Forest Classifier, Gradient Boosting, Clustering (k-means, DBSCAN).

The projects I’ve been working on can be reviewed below.

I am currently open for new opportunities in the data science field, where I can leverage my analytical skills, communication qualities, and domain expertise to create value and impact for the organization. I am eager to learn new technologies, collaborate with diverse teams, and contribute to the ML-developing area.

So far have learned and made use of:

  • Python, SQL;
  • EDA, Feature Engineering and Data wrangling;
  • Machine Learning;
  • Mathematical statistics;
  • Linear Algebra in the context of Linear Methods;
  • Mathematical Analysis in the context of an optimization problem;
  • ... to be continued - learning every day...

If interested, please have look at

Portfolio Description
PROJECTS Projects I was exploring out of curiousity, gaining experience and enjoying learning new tools & techniques.
GAMES Click to have some fun with simple and primitive codes.
TRAINING PROJECTS Few case-studies performed following the ML-modeling learning path...
GISTS I really like this tool to store and share some useful codes and functions. Have a look, maybe you will like it too.

Feel free to contact

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  1. TRAINING_PROJECTS TRAINING_PROJECTS Public

    Data Science & Machine Learning Course

    HTML 1

  2. PROJECTS PROJECTS Public

    A sight on my work

    HTML 1

  3. Outliers functions Outliers functions
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    # Outliers: method Tukey
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    def outliers_iqr(data, feature, left = 2, right = 2, log_scale = False):
    
              
    
              
    
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        if log_scale:
    
              
    
              
    
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            x = np.log(data[feature]+1)
    
              
    
          
    
    
    
  
    

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          Linear Regression_Metrics
Linear Regression_Metrics      
    

          
    
              
    
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    from sklearn import linear_model 
    
              
    
              
    
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    from sklearn import metrics 
    
              
    
              
    
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    def print_metrics(y_train, y_train_predict, y_test, y_test_predict):
    
              
    
              
    
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          Statistical Tests
Statistical Tests      
    

          
    
              
    
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    # Confidence Interval Function
    
              
    
              
    
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    def proportions_conf_interval(n, x_p, gamma):
    
              
    
              
    
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        alpha = 1 - gamma
    
              
    
              
    
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        z_crit = -norm.ppf(alpha / 2)
    
              
    
          
    
    
    
  
    

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          Encoding categorical features
Encoding categorical features      
    

          
    
              
    
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    import category_encoders as ce
    
              
    
              
    
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    #Ordinal Encoding
    
              
    
              
    
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    ord_enc = ce.OrdinalEncoder()
    
              
    
              
    
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    df[['XXX', 'YYY', 'ZZZ']] = ord_enc.fit_transform(
    
              
    
          
    
    
    
  
    

    7.