Discover @AvijitChowdhury Open Source projects

Avijit Chowdhury Avroneel (@AvijitChowdhury)

AvijitChowdhury

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Location 🇧🇩 Bangladesh
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MovieRecomenderApp

Watching movies is fun, but figuring out what movie to watch next is a nerve-racking experience. Endlessly scrolling through Netflix, watching trailers on YouTube, looking up IMDb ratings, wasting half an hour and still cannot decide what to watch – does this seem familiar to you? Then you have landed on the right page! PickAMovieForMe’s movie recommendation engine is the answer to the question “What movie should I watch?”! Your film choices are about to be simplified greatly. Our quiz-based movie picker finds the perfect movie for your mood, occasion and individual preferences in just a few minutes! Whether you’re watching a movie by yourself, joining a movie night with friends or going on a movie date with your crush – our quiz will guide you to an awesome choice!

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C-Cplusplus-DSA

SentimentAnalysisApp

This is a ML app for predicting a person sentiment.

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HousePricingML

This a simple ml app. This app basically predict the house price .

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ML-Project

ViChat

This is a video calling app

Directing-Customers-to-Subsciption-Through-Financial-AppBehavior-Analysis

To get more accuracy, we train all supervised classification algorithms but you can try out a few of them which are always popular. After training all algorithms, we found that SVC and XGBoost classifiers are given high accuracy than remain but we have chosen XGBoost. As ML Engineer, we always retrain the deployed model after some period of time to sustain the accuracy of the model. We hope our efforts will give more profit to the fin-tech company.

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Multiple_Disease_Prediction

Many of the existing machine learning models for health care analysis are concentrating on one disease per analysis. For one analysis is for diabetes analysis, one for cancer analysis, and one for skin diseases like that. There is no common system where one analysis can perform more than one disease prediction. This Model is for predicting those diseases.

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Algorithm-Course

In mathematics and computer science, an algorithm (/ˈælɡərɪðəm/ (listen)) is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation.[1] Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can perform automated deductions (referred to as automated reasoning) and use mathematical and logical tests to divert the code execution through various routes (referred to as automated decision-making).

yolo-based-image-video-object-detection

YOLO (You Only Look Once) is a method / way to do object detection. It is the algorithm /strategy behind how the code is going to detect objects in the image. The official implementation of this idea is available through DarkNet (neural net implementation from the ground up in C from the author

MallCustomerSegmentationPredictor

Unsupervised Learning mainly deals with identifying the structure or pattern of the data. In this algorithm, we do not have labeled data(or the dependent variable is absent), for example, clustering data, recommendation systems, etc. Unsupervised Learning provides excellent results as one can deduce many hidden relations between different attributes or features. So, In this project, I have solved the problem with unsupervised machine learning and the k-means algorithm.

Cat-DogDetector

Convolutional Neural Network (CNN) is an algorithm taking an image as input and then assigning weights and biases to all the aspects of an image and thus differentiates one from the other. Neural networks can be trained by using batches of images, each of them having a label to identify the real nature of the image (cat or dog here). A batch can contain a few tenths to hundreds of images. For each and every image, the network prediction is compared with the corresponding existing label, and the distance between the network prediction and the truth is evaluated for the whole batch. Then, the network parameters are modified to minimize the distance and thus the prediction capability of the network is increased. The training process continues for every batch similarly. The main goal of this project is to develop a system that can identify images of cats and dogs. The input image will be analyzed and then the output is predicted. The model that is implemented can be extended to a website or any mobile device as per the need. The Dogs vs Cats dataset can be downloaded from the Kaggle website. The dataset contains a set of images of cats and dogs. Our main aim here is for the model to learn various distinctive features of cats and dogs. Once the training of the model is done it will be able to differentiate images of cats and dogs.

Olympics-Data-Analytic-Project

The modern Olympic Games or Olympics are leading international sports events featuring summer and winter sports competitions in which thousands of athletes from around the world participate in a variety of competitions. The Olympic Games are considered the world’s foremost sports competition with more than 200 nations participating. The total number of events in the Olympics is 339 in 33 sports. And for every event there are winners. Therefore various data is generated. So, by using Python we will analyze this data. Modules Used Pandas: It is used for analyzing the data, NumPy: NumPy is a general-purpose array-processing package. Matplotlib: It is a numerical mathematics extension NumPy seaborn: It is used for visualization of statistical graphics plotting in Python

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GraduateAdmissionPrediction

The use of machine learning can be seen almost everywhere around us, be it Facebook recognizing you or your friends, or YouTube recommending you a video or two based on your history — Machine Learning is everywhere! However, the ‘magic’ of machine learning is not just limited to only these areas. Machine Learning is broadly categorized as Supervised and Unsupervised Learning. Supervised Learning is one in which we teach the machine by providing both independent and dependent variables, for example, Classifying or predicting values. Unsupervised Learning mainly deals with identifying the structure or pattern of the data. In this type of algorithms, we do not have labeled data(or the dependent variable is absent), for example, clustering data, recommendation systems, etc. Unsupervised Learning provides amazing results as one can deduce many hidden relations between different attributes or features. In response to the highly competitive job market at present times, an increased interest in graduate studies has arisen. This has not only burdened applicants but also led to an increased workload on admission faculty members of universities. Any chance of abridging the admission process impelled applicants and faculty workers to look for faster, efficient, and more accurate methods for predicting admissions. So,In this project i have solved the problem with supervised machine learning and Gradient Boost Classifier algorithm.

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