Abrar2652/Detecting-sentiments-of-a-quote

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Abrar2652/Detecting-sentiments-of-a-quote

Abrar2652

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HackerEarth Machine Learning challenge: Love is love competition submission [Position: 31st]

Cash-Flow-Forecast-Challenge-Solution-2020-TopCoder

Cash Flow Forecasting Challenge held in 2020 on the popular TopCoder hackathon platform. I created my ML forecasting model using Python SciPy libraries. The step-by-step guide from data exploration to analysis has been shown in the notebook.

Jupyter Notebook

Romantic-Relationship-Love-Data-Analysis-using-Python

Facebook text messages and media data analysis using Python of a couple. The in-depth analysis of the data provides insights into the relationship pattern, strength, average messages, engagement, efforts, and so on. This research provides useful information suggesting the positive changes required to maintain a healthy relationship. It also shows the constraints of using this data and the limitations of this analysis.

Jupyter Notebook

Satellite-Image-Data-Analysis-using-Numpy

I worked on this project using Numpy library of Python under Dr. Ilkay Altintas (Top Data Scientist, San Diego Super Computer Center). This high resolution project image was taken from NASA satellite LANsat.

Jupyter Notebook

Road-Friction-Forecasting

A supervised classification machine learning approach to forecasting the road as safe (label 1) or dangerous (label 0) for driving in the arctic regions. If the friction is 0 <= x < 0.5 then we labeled it as 0, either 1 in the range 0.5 to 1.

Jupyter Notebook

nlp-roBERTa-biLSTM-attention

An NLP research project utilizing the "cardiffnlp/twitter-roberta-base-sentiment-latest" pre-trained transformer for tweet tokenization. The project includes an attention-based biLSTM model that predicts sentiment labels for tweets as negative (-1), neutral (0), or positive (1).

Jupyter Notebook

How-to-BRUTE-FORCE-a-Hash-function

The method of finding hash values and brute-forcing a hash function has been shown.

Jupyter Notebook

Pet-Adoption-HackerEarth-Hackathon

My Machine Learning Model got the accuracy of 87.38717% in the "HackerEarth Machine Learning challenge: Pet Adoption Hackathon" and my position was 401st among 24375 accepted submissions.

Jupyter Notebook

Unintended-Bias-Toxicity-Detection-Project

This is the second project to be completed in Upskill ISA Intelligent Machines. The project was done after the end of the competition. The ensemble of BERT, GPT2, XLNet was used in this model that obtained 0.94656 private scores on Kaggle.

Jupyter Notebook