# Showcase

Hi there, and welcome to my showcase of data science notebooks! As a data scientist, I know firsthand the importance of having a powerful and interactive environment to analyze and visualize data. That's where data science notebooks come in. In this showcase, I've gathered a collection of notebooks that demonstrate how these tools can be used to tackle a wide range of data science challenges. Whether you're interested in data cleaning, data preprocessing, machine learning, or data visualization, there's something here for everyone.

I've put together notebooks that showcase a variety of data science libraries and tools, each designed to tackle a specific problem or explore a particular dataset. So, whether you're a seasoned data scientist or just starting in this field, I invite you to join me on this journey through the exciting world of data science notebooks. Let's dive in and see what insights we can uncover!

## Prediction of Gasoline Prices using a LSTM network

In this notebook a model is implement to predict gasoline prices using a LSTM network (Long Short-Term Memory). LSTM networks are a type of recurrent neural network that can help in predicting gasoline prices based on time series data. LSTM networks are well-suited for handling time-series data because they can capture long-term dependencies and trends in the data.

- Programming language: Python (Tensorflow, matplotlib)
- Dataset: U.S. Energy Information Administration - Gasoline prices from Jun. 13, 2014 to Feb. 17, 2023
- Category: Sequential Regression
- Notebook: click here

## K-nearest neighbors classification

It shows how to implement the k-nearest neighbors algorithm for classification. To classify an observation, this algorithm looks for closer examples using a distance function.

- Programming language: Python
- Dataset: Iris
- Category: Classification
- Notebook: click here

## Multilayer Perceptron

A model is deduced and implemented for the multilayer perceptron (MLP), a feedforward neural network. In the first section, MLP's structure is shown, including the algorithm to transform the input X to the output y^, i.e. the predicted values. In the second section, considerations to optimize MLP's parameters are defined. The backpropagation algorithm indicates how to adjust values for coefficients in each network connection. Finally, a gradient descent algorithm is drawn to integrate forward and backpropagation operations.

- Programming language: Python
- Dataset: Iris
- Category: Classification
- Notebook: click here