sahaashish95

face-recognization-using-openCV

In this tutorial, you will learn how to use OpenCV to perform face recognition. To build our face recognition system, we’ll first perform face detection, extract face embeddings from each face using openCV, train a face recognition model on the embeddings, and then finally recognize faces in both images and video streams with OpenCV.

Image-Recognition

In this part, we will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show how to do it – by simply changing the pictures in the input folder. For example, we will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if we want to keep it fitted to cats and dogs, then we will literally be able to a take a picture of our cat or our dog, and our model will predict which pet we have.

LPU_batch

sahaashish95/LPU_batch

sahaashish95/Sporty-Guru-mInternship-task

sahaashish95/Walmart-Sales-Forecasting

This is a walmart sales forecasting problem. This is one of my case study .In this case study our task is to predict sales for walmart stores .I take this problem from kaggle .and our final score for this problem is under top 10% of kaggle score

sahaashish95/sahaashish95

Config files for my GitHub profile.

sahaashish95/yolov5

YOLOv5 in PyTorch > ONNX > CoreML > TFLite

sahaashish95/Chemical-compound-classification

The given dataset contains details about organic chemical compounds including their chemical features, isomeric conformation, names and the classes in which they are classified. The compounds are classified as either ‘Musk’ or ‘Non-Musk’ compounds. Your task is to build a classification model on the given data.

sahaashish95/DonorsChoose

DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website. Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve: How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible How to increase the consistency of project vetting across different volunteers to improve the experience for teachers How to focus volunteer time on the applications that need the most assistance The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

sahaashish95/car-damage-detection-using-CNN

Automated car damage detection using Instance Segmentation(Mask R-CNN)

sahaashish95/Dress-pattern-classification

sahaashish95/object-detection-and-tracking-of-objects-in-computer-vison

this is the project of computer vision in which our task is to identification of object and also tracking of objects in videos

sahaashish95/predict-whether-transaction-is-happened-or-not

At Santander our mission is to help people and businesses prosper. We are always looking for ways to help our customers understand their financial health and identify which products and services might help them achieve their monetary goals. In this competition we are asked to predict if a customer will make a transaction or not regardless of the amount of money transacted. Hence our goal is to solve a binary classification problem. In the data description you can see that the features given are numeric and anonymized. Furthermore the data seems to be artificial as they state that "the data has the same structure as our real data"

sahaashish95/predict-category-of-videos-by-analysing-youtube-video-s-tittle-and-description

here first we scarp the tittle and description of youtube by using webscraping after that using machine learning algorithm to train model

sahaashish95/Image-Recognition

In this part, we will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show how to do it – by simply changing the pictures in the input folder. For example, we will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if we want to keep it fitted to cats and dogs, then we will literally be able to a take a picture of our cat or our dog, and our model will predict which pet we have.

sahaashish95/regression

sahaashish95/news-classification-based-on-short-description-and-category

here given dataset of news , our aim to make model to classify category of news based on headlines and shortdescription

sahaashish95/position-vs-salary-based-on-polynomial-regression

we have give data salary vs position ,our goal is to create model to predict salary based on position

sahaashish95/salary-vs-experience-model-based-on-linear-regression

in this we have dataset given. this is dataset of salary based on experience our goal is to make model of predicting salary based on experience.

sahaashish95/find-at-which-stock-company-invest-to-get-maximum-profit-based-on-multiple-linear-regression

Here a dataset of 50 startup is given.In this dataset given field (research and development, administration, marketing spend,country, profit) . our goal is to find in which field(research and development, administration, marketing spend) invest to get maaximum profit.After fitting model multiple linear regression we use backward elimination method to predict in which field invest ,as a result we find investing more on research and development field and we get maximum profit.

sahaashish95/Churn-Modelling-Problem

In this part we will be solving a data analytics challenge for a bank. we have given a dataset with a large sample of the bank’s customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.our goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, we are asked to rank all the customers of the bank, based on their probability of leaving. To do that, we will need to use the right Deep Learning model, one that is based on a probabilistic approach.

sahaashish95/-Stock-Price-Prediction

in this part we predict the price of stock in futre by analyse recent stock price.i using deep learning model which is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. we will take the challenge to use it to predict the real Google stock price.

sahaashish95/wine-classification

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. i use principal_componet_analysis and Logistic_regression to train this model. and accuracy of my model is 97%.it classify the three types of wines

sahaashish95/To-check-whether-message-is-spam-or-not

Here in this model we trained the machine learning model to check whether message is spam or not .Accuracy of this model is 86 %

sahaashish95/restaurants-reviews-based-on-natural-language-processing

Here in this model we trained the machine learning model to check whether review is positive or negative if output is 0 it means review is negative else review is positive

sahaashish95/face-recognization-using-openCV

In this tutorial, you will learn how to use OpenCV to perform face recognition. To build our face recognition system, we’ll first perform face detection, extract face embeddings from each face using openCV, train a face recognition model on the embeddings, and then finally recognize faces in both images and video streams with OpenCV.