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# Data Science Portfolio
---
layout: default
---
## Machine learning

### Fraud Detection

Text can be **bold**, _italic_, or ~~strikethrough~~.
Fraud detection is a set of processes and analyses that allow businesses to identify and prevent unauthorized financial activity. This can include fraudulent credit card transactions, identify theft, cyber hacking, insurance scams, and more.

[Link to another page](./another-page.html).
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/fraud_detection)

There should be whitespace between paragraphs.
<center><img src="images/fraud_detection.jpg"/></center>

There should be whitespace between paragraphs. We recommend including a README, or a file with information about your project.
---
### Financial-Models-Numerical-Methods

# Header 1
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/Financial-Models-Numerical-Methods)

This is a normal paragraph following a header. GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
This is just a collection of topics and algorithms that in my opinion are interesting.

## Header 2
It contains several topics that are not so popular nowadays, but that can be very powerful. Usually, topics such as PDE methods, Lévy processes, Fourier methods or Kalman filter are not very popular among practitioners, who prefers to work with more standard tools.
The aim of these notebooks is to present these interesting topics, by showing their practical application through an interactive python implementation.

> This is a blockquote following a header.
>
> When something is important enough, you do it even if the odds are not in your favor.
<center><img src="images/financial_modeling.jpg"/></center>

### Header 3
---
### LSTM Neural Network for Time Series Prediction

```js
// Javascript code with syntax highlighting.
var fun = function lang(l) {
dateformat.i18n = require('./lang/' + l)
return true;
}
```
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/LSTM-Neural-Network-for-Time-Series-Prediction)

```ruby
# Ruby code with syntax highlighting
GitHubPages::Dependencies.gems.each do |gem, version|
s.add_dependency(gem, "= #{version}")
end
```
The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep neural networks.

#### Header 4
<center><img src="https://camo.githubusercontent.com/a085b4fe60690252b8aa2de917c53fc3f63aec21aafea21c8f1ecb543d2c44cb/68747470733a2f2f7777772e616c74756d696e74656c6c6967656e63652e636f6d2f6173736574732f74696d652d7365726965732d70726564696374696f6e2d7573696e672d6c73746d2d646565702d6e657572616c2d6e6574776f726b732f73696e776176655f66756c6c5f7365712e706e67"/></center>

* This is an unordered list following a header.
* This is an unordered list following a header.
* This is an unordered list following a header.
---
### Text Classification

##### Header 5
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/cnn-text-classification-tf#readme)

1. This is an ordered list following a header.
2. This is an ordered list following a header.
3. This is an ordered list following a header.
It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

###### Header 6
<center><img src="images/text_classification.png"/></center>

| head1 | head two | three |
|:-------------|:------------------|:------|
| ok | good swedish fish | nice |
| out of stock | good and plenty | nice |
| ok | good `oreos` | hmm |
| ok | good `zoute` drop | yumm |
---
### Awesome Machine Learning

### There's a horizontal rule below this.
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/awesome-machine-learning)

* * *
A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.

### Here is an unordered list:
<center><img src="images/machine_learning.jpg"/></center>

* Item foo
* Item bar
* Item baz
* Item zip
---
### Gesture Recognizer

### And an ordered list:
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/CNNGestureRecognizer)

1. Item one
1. Item two
1. Item three
1. Item four
Gesture recognition via CNN neural network implemented in Keras + Theano + OpenCV

### And a nested list:
Key Requirements: Python 3.6.1 OpenCV 3.4.1 Keras 2.0.2 Tensorflow 1.2.1 Theano 0.9.0 (obsolete and not supported any further)

- level 1 item
- level 2 item
- level 2 item
- level 3 item
- level 3 item
- level 1 item
- level 2 item
- level 2 item
- level 2 item
- level 1 item
- level 2 item
- level 2 item
- level 1 item
Suggestion: Better to download Anaconda as it will take care of most of the other packages and easier to setup a virtual workspace to work with multiple versions of key packages like python, opencv etc.

### Small image
<center><img src="images/gesture_recognition.jpg"/></center>

![Octocat](https://github.githubassets.com/images/icons/emoji/octocat.png)
---
### Human Activity Recognition

### Large image
[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/sajankedia/LSTM-Human-Activity-Recognition)

![Branching](https://guides.github.com/activities/hello-world/branching.png)
Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:

WALKING,
WALKING_UPSTAIRS,
WALKING_DOWNSTAIRS,
SITTING,
STANDING,
LAYING.
Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.

### Definition lists can be used with HTML syntax.
Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

<dl>
<dt>Name</dt>
<dd>Godzilla</dd>
<dt>Born</dt>
<dd>1952</dd>
<dt>Birthplace</dt>
<dd>Japan</dd>
<dt>Color</dt>
<dd>Green</dd>
</dl>
<center><img src="images/human_activity.jpg"/></center>

```
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
```

```
The final element.
```