bhuyanamit986 Goto Github PK

amazon-fine-food-sentiment

I worked on kaggle kernel on Amazon Fine Food Reviews dataset and applied all the text cleaning, preprocessing and model fitting steps. I cleaned the deduplicated data after which nearly about 69% of original data remained. I applied preprocessor and tokenizer to remove stopwords and emoctions etc.. I used bag of words model and tfidf models to separate out the most useful words. Then I created a pipeline and evaluated the dataset on a logistic regression model to get accuracy of 93.24%%

animated-buttons

apple-sign-up-form

blog-palace

blogrlandingpage

cat-vs-dog

Here I made my own neural network from scratch during my Neural Networks and Deep Learning course from deeplearning.ai by Andrew Ng. I trained a class vs dog dataset on my neural network and had found accuracy of 80% on evaluation.

charity-ml

In this project, we will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. We will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Our goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income).

deepstyle

# Introduction to Neural Style Transfer

e-commerce-app-ui-flutter

Nice and clean Online Shop app UI by using #Flutter.

end-to-end-machine-learning

Here I prepared a basic machine learning project of Housing price prediction, in which I provided all the steps to create a machine learning project from end to end, from reading data, and visualisation to training it and choosing proper hyperparameters.

exploratory-data-analysis

Here I did EDA on the iris dataset using histograms, scatterplots, probability density function(PDF), cumulative distribution function(CDF), box plots, whisker ports

flowerclassification

# Flower Classification

football-video-captioning

Source code for "Bi-modal Transformer for Dense Video Captioning" (BMVC 2020)

gen-social

A social platform where you can connect with your friends and update about your day to day life.

get-your-food

hackerearthchallenge

zipfile

hacktoberfest_2020

here_comes_the_customer

# Getting Started In this project, we will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

hse-introduction-to-deep-learning

Here are my assignments that I completed in my course Introduction to Deep Learning by coursera from Higher School of Economics on coursera's environment.

jamming-with-spotify

knn-w-o-sklearn

I made my own KNearestNeighbors algorithm to get better intution of the algorithm which outperformed the scikit-learn's official KNeighborsClassifier's performance getting 98.24% accuracy on Breast Cancer Diagnostic Winscoin dataset.

learnopencv

Learn OpenCV : C++ and Python Examples

logistic-regression-from-scratch

I created a logistic regression from scratch and plotted the decision regions to visualise the decision boundary.

machine-learning

Content for Udacity's Machine Learning curriculum

machine-learning-techniques

Here I have applied many machine learning regression and classification technique in the Applied Machine Learning course by University of Michigan for understanding.

matplotlibtutorials

Here I did some data visualisations with matplotlib.

microsoft-bot

mnist-digit-classifier

Here I made a MNIST digit classifier in which I imported the MNIST dataset and performed all the techniques of machine learning and showed how One Versus One and One Versus All techniques are used for multiclass classification.

mooddetector

# Introduction

movie-recommmendor-system

Here I made a content based movie recomendor system using basic mathematics and used it to recommend movies based on the ratings of the customer given to the movies..