@Ferdib-Al-Islam
github

Ferdib-Al-Islam

Twitter logo Share LinkedIn logo Share Facebook logo Share

Top repositories

1

detect-parkinsons-disease

Parkinson disease is associated with movement disorder symptoms, such as tremor, rigidity, bradykinesia, and postural instability. The manifestation of bradykinesia and rigidity is often in the early stages of the disease. These have a noticeable effect on the handwriting and sketching abilities of patients, and micrographia has been used for early-stage diagnosis of Parkinson’s disease. While handwriting of a person is influenced by a number of factors such as language proficiency and education, sketching of a shape such as the spiral has been found to be non-invasive and independent measure.
Python
12
star
2

lstm-time-series-prediction-pytorch

Long Short Term Memory unit (LSTM) was typically created to overcome the limitations of a Recurrent neural network (RNN). The Typical long data sets of Time series can actually be a time-consuming process which could typically slow down the training time of RNN architecture. We could restrict the data volume but this a loss of information. And in any time-series data sets, there is a need to know the previous trends and the seasonality of data of the overall data set to make the right predictions.
Jupyter Notebook
8
star
3

python-outlier-detection

The performance of the machine learning algorithm also depends on properly detecting outliers in the dataset. Particularly the regression algorithms are very easily influenced by the outliers. In this case, if the dataset is not correctly cleaned by removing the outlier, then the model performance is unlikely to be as expected. PyOD - Python Toolkit for detecting Outliers. This package contains about 20 algorithms for detecting outliers.
Jupyter Notebook
7
star
4

fb-prophet-time-series-forcasting

Time series forecasting is a technique for the prediction of events through a sequence of time. Time-series forecasting decomposes the historical data into the baseline, trend, and seasonality. When a forecasting model doesn’t run as planned, we want to tune the parameters of the method with regards to the specific problem at hand. Tuning these methods requires a thorough understanding of how the underlying time series models work. A typical analyst/engineer will not know how to adjust these orders to avoid the problem. The Prophet package provides intuitive parameters which are easy to tune. Even someone like me who lacks expertise in forecasting models can use this to make meaningful predictions for a variety of problems in a business scenario.
Jupyter Notebook
3
star
5

crime-data-visualization-and-analysis

Jupyter Notebook
1
star
6

Data_Science

Principle Component Analysis and Text Mining With R
R
1
star
7

openvino-people-counter

The people counter application is one of a series of IoT reference implementations aimed at instructing users on how to develop a working solution for a particular problem. It demonstrates how to create a smart video IoT solution using Intel® hardware and software tools. This solution detects people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.
JavaScript
1
star
8

image-anomaly-detecton

In Machine Learning, anomaly detection (outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.
Jupyter Notebook
1
star
9

sentiment-analysis-sagemaker-deployment

In this project we will construct a recurrent neural network for the purpose of determining the sentiment of a movie review using the IMDB data set. we will create this model using Amazon's SageMaker service. In addition, we will deploy our model and construct a simple web app which will interact with the deployed model.
HTML
1
star