https://github.com/chekoduadarsh/vicara-data-science-challenge
https://github.com/chekoduadarsh/vicara-data-science-challenge
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/chekoduadarsh/vicara-data-science-challenge
- Owner: chekoduadarsh
- Created: 2021-01-10T13:20:54.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-01-11T06:19:46.000Z (over 4 years ago)
- Last Synced: 2025-02-08T15:48:13.923Z (4 months ago)
- Language: Jupyter Notebook
- Size: 12.7 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Vicara Data Science Challenge: Activity Recognition from Single Chest-Mounted Accelerometer
Dataset : https://archive.ics.uci.edu/ml/machine-learning-databases/00287/Activity%20Recognition%20from%20Single%20Chest-Mounted%20Accelerometer.zip
#### Challange:
The dataset collects data from a wearable accelerometer mounted on the chest. Uncalibrated Accelerometer Data are collected from 15 participants performing 7 activities. The dataset is intended for Activity Recognition research purposes. It provides challenges for identification and authentication of people using motion patterns.
| ID | ACTION |
|:--:|:---------------------------------------------:|
| 1 | Working at Computer |
| 2 | Standing Up, Walking and Going up\down stairs |
| 3 | Standing |
| 4 | Walking |
| 4 | Going Up\Down Stairs |
| 6 | Walking and Talking with Someon |
| 7 | Talking while Standing |
## requirements.txt : Recommended touse Anaconda
## Submission: Submission.ipynb
#### Achived Accuracy:
| Algorithm | Accuracy |
|:----------------------------:|:--------:|
| Random Forest Classification | 81.13% |
| XGBOOST Classifier | 81.13% |
| Artificial Neural Network | 95.24% |
## Random Forest Classification
Random forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction (see figure below).
The fundamental concept behind random forest is a simple but powerful one — the wisdom of crowds. In data science speak, the reason that the random forest model works so well is:
A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models.
The low correlation between models is the key. Just like how investments with low correlations (like stocks and bonds) come together to form a portfolio that is greater than the sum of its parts, uncorrelated models can produce ensemble predictions that are more accurate than any of the individual predictions. The reason for this wonderful effect is that the trees protect each other from their individual errors (as long as they don’t constantly all err in the same direction). While some trees may be wrong, many other trees will be right, so as a group the trees are able to move in the correct direction. So the prerequisites for random forest to perform well are:
1. There needs to be some actual signal in our features so that models built using those features do better than random guessing.
2. The predictions (and therefore the errors) made by the individual trees need to have low correlations with each other.
## XGBOOST Classifier
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now. Please see the chart below for the evolution of tree-based algorithms over the years.
### Algorithmic Enhancements:
**Regularization:** It penalizes more complex models through both LASSO (L1) and Ridge (L2) regularization to prevent overfitting.
**Sparsity Awareness:** XGBoost naturally admits sparse features for inputs by automatically ‘learning’ best missing value depending on training loss and handles different types of sparsity patterns in the data more efficiently.
**Weighted Quantile Sketch:** XGBoost employs the distributed weighted Quantile Sketch algorithm to effectively find the optimal split points among weighted datasets.
**Cross-validation:** The algorithm comes with built-in cross-validation method at each iteration, taking away the need to explicitly program this search and to specify the exact number of boosting iterations required in a single run.
## Artificial Neural Network
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found. ANN is also known as a neural network.
A single neuron is known as a perceptron. It consists of a layer of inputs(corresponds to columns of a dataframe). Each input has a weight which controls the magnitude of an input. The summation of the products of these input values and weights is fed to the activation function. Activation functions are really important for a Artificial Neural Network to learn and make sense of something really complicated and Non-linear complex functional mappings between the inputs and response variable.
They introduce non-linear properties to our Network.Their main purpose is to convert a input signal of a node in a A-NN to an output signal. That output signal now is used as a input in the next layer in the stack. Specifically in A-NN we do the sum of products of inputs(X) and their corresponding Weights(W) and apply a Activation function f(x) to it to get the output of that layer and feed it as an input to the next layer.
