Welcome!!! to my Data Science and Machine Learning Portfolio, where I share projects and analysis that I have done using Python and a variety of libraries in this area. Below you'll find a list of projects using Exploratory Data Analysis and Machine Learning Models, along with brief descriptions and links to each.
LINEAR REGRESSION MODELS
- Ecommerce Customers: A New York-based fashion company, dedicated to selling clothing online and in physical stores, wants to know if it should focus its resources on optimizing the user experience on its mobile app or website to boost its earnings. Join me in exploring and investigating whether there were other influential factors in this decision-making process.
- Libraries Used: Pandas, Matplotlib, Seaborn, Numpy, Scikit-learn
- Ver Proyecto
- Ver Data_set
LOGISTIC REGRESSION MODELS
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Advertisement: In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user.
- Libraries Used: Pandas, Matplotlib, Seaborn, Numpy, Scikit-learn
- Ver Proyecto
- Ver Data_set
DECISION TREES AND RANDOM FOREST MODELS
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Lending Club: For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model (both models were implemented) that will help predict this.
- Libraries Used: Pandas, Matplotlib, Seaborn, Numpy, Scikit-learn, Graphviz
- Ver Proyecto
- Ver Data_set
SUPPORT VECTOR MACHINES AND GRIDSEARCHCV
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Iris flower: In this project we will create a model to predict the type of flower, based on the dimensions of the petals and sepals. The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher in the 1936 as an example of discriminant analysis. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.
- Libraries Used: Pandas, Matplotlib, Seaborn, Scikit-learn
- Ver Proyecto
- Iris flower data set
- Programming Languages: Python
- Libraries & Frameworks: Pandas, NumPy, scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn, Statsmodels
- Development Tools: Jupyter Notebooks, Git, GitHub, VS Code
For inquiries or to discuss these projects further, please reach out through the following channels:
- Email: javieralfonsoseijas@gmail.com
- LinkedIn: LinkedIn
- Kaggle: Kaggle
This portfolio is a work in progress and will be updated regularly with new projects and analysis. Thanks for stopping by, and I hope my work not only informs but also inspires.