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DarioDell/README.md

Data Scientist • Data Explorer • Problem Solver • Data Analyst • Data Visualization • Python Developer • SQL • Machine Learning

  • 🎓 Graduating in 2024 from IMF Smart Education --> Data Science & Business Analytics
  • 👨‍💻 My main tools are Python and SQL
  • 📚 libraries: Numpy, Pandas, SciPy, Matplotlib, Seaborn, Keras, TensorFlow, OpenCv, Scikit-learn, Statsmodels
  • 📐 Good in Algorithms and Data Structures
  • 💻 Supervised and unsupervised learning models | reinforcement learning | Deep learning models
  • 🛢Databases: MySQL, Microsoft SQL Server, MongoDB, Neo4j
  • 📊 Databricks (Scala, PySpark)
  • 📈 PowerBi, Tableau
  • 🌱 I’m currently continuous learning --> Cloud servicies and DevOps
  • 📌 Hobbies: Cars🚗 - Football⚽️ - Work out🏋️ - Coding⌨️
  • 📫 How to reach me: dariodellagostino@gmail.com

🛠️ My proyects:

  • Univariate Time Series

    NYC's public transportation system Analysis. A quick and effective way to obtain conclusions when working with univariate time series.

  • Multivariate Time Series

    Implementation of the VAR statistical model to predict a set of temporal variables. Interesting project, with an exhaustive and detailed analysis, which has been presented as a final master's project.

    • EDA
    • Split the series into training and test sets
    • Stationarity test
    • Transformation of the training series
    • Construction of a VAR model
    • Granger Causality
    • Model diagnosis
    • The forecast
    • Inverse transformation of the forecast
    • The forecast evaluation

  • Classification Problem

    A binary classification of a bank churn analysis. A very useful model for any company that provides services, capable of predicting potential clients who will leave the company:

    • EDA
    • Visualizations
    • Logistic Regression
    • Metrics: ROC - AUC. Correlation matrix

  • Recommendation System

    It is an effective model applicable to any company. Normally it would be a good idea to present this type of reports to the marketing area, but the objective of this project is to demonstrate that you do not have to be an expert to be able to convert data into relevant information. The technologies used are:

    • SQL Server: to extract the data.
    • Python: to develop the script, all coded in python.
    • PowerBi: technology used for the final report, DAX queries are implemented.

  • Regression Problem

    Price prediction is one of the most common regression problems in data analysis. Through an exhaustive step-by-step analysis, good and detailed results are obtained:

    • EDA
    • Visualizations
    • Linear Regression: Simple linear regression and Multiple linear regression
    • Regularization techniques: Ridge regularization and Lasso regularization
    • Metrics: Mean square error (MSE), Root Mean Square Error (RMSE), Determination coefficient (R2)

  • MySQL Queries

    A deep analysis of a store with intermediate level queries to a more advanced and detailed level.

    Seeking to know the level of sales, relationship between products-customers, profitability, supplies and more relevant information of interest

    • MySQL
    • Python
    • Jupyter Notebook

  • Convolutional Neural Networks(CNN)

    Convolutional neural networks (CNN) specialize in dealing with images and videos. They enable the detection of objects, identification of people, autonomous cars, etc.

    • Object localization
python django pandas numpy seaborn matplot sickit tensorflow keras mysql sqlserver mongo neo4j aws googlecolab jupyter databricks pyspark powerbi tableau

Pinned Loading

  1. Binary_classification_problem Binary_classification_problem Public

    In this exercise I try to predict which bank customers will commit churn. Applying the LOGISTIC REGRESSION technique of machine learning. As a metric of the model I use ROC AUC curves.

    Jupyter Notebook

  2. Regression_problem Regression_problem Public

    This project deals with a regression problem in which the aim is to estimate the price of diamonds.

    Jupyter Notebook

  3. Univariate_time_series Univariate_time_series Public

    Contains the analysis, processing, visualizations and conclusions about a univariate time series.

    Jupyter Notebook

  4. MySQL_commerce MySQL_commerce Public

    Interesting queries to deeply analyze the behavior of a small business

    Jupyter Notebook

  5. Multivariate_time_series Multivariate_time_series Public

    Set of time series of the economic field analyzed simultaneously, through the implementation of a VAR model

    Jupyter Notebook

  6. convolutional_neural_networks-CNN- convolutional_neural_networks-CNN- Public

    Convolutional neural networks (CNN) specialize in dealing with images and videos. They enable the detection of objects, identification of people, autonomous cars, etc.

    Jupyter Notebook