Attention Feature Fusion base on spatial-temporal Graph Convolutional Network（AFFGCN）

Skip-Gram Model From Scratch

Small tools for csv file processing (onehot encoding, format checking and converting to libsvm).

Category transformation

This project focus on building different machine learning models to make predictions on employee attrition and performance

A model for predicting the selling price of a used car using machine learning algorithms. This model is deployed in the Heroku Cloud Platform.

Encoding: converting categorical data into a numerical data

We use multiple Tree boosting models and compare their performance to calculate the fit percentage of a candidate for the job they apply for. Also try to handle categorical methods using various methods.

Convert a unstructured array into a stuctured dataframe.

Bank Institution Term Deposit Predictive Model

Implementing Semantic Segmentation on Satellite images using U-Net architecture

Deep Learning -Artificial Neural Network

Diacritics are short vowels with a constant length that are spoken. The same word in the Arabic language can have different meanings and different pronunciations based on how it is diacritized. In this project, we implement a pipeline to predict the diacritic of each character in an Arabic text using Natural Language Processing techniques.

The most common discrete and continuous distributions, showing how they find use in decision and estimation problems, and constructs computer algorithms for generating observations from the various distributions. and applications, and lastly the most important concept is covered is entropy

A recommendation system created for H&M created with the help of EDA(Exploratory Data Analysis) and ALS (Alternative Least Squares) which optimizes a users recommendations taking into considerations an account`s view history and uses matrix optimization to give the best possible recommendations.

Feature Engineering with Python

In this project we predict the forest cover type using the cartographic variables in the training/test datasets.

Value to Business :: Using this Regression model, the decision-makers will able to understand the properties of various products and stores which play an important and key role in optimizing the Marketing efforts and results in increased sales.