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aeroplane-disaster---hackerearth-competition

URL : https://www.hackerearth.com/challenges/competitive/airplane-accident-severity-hackerearth-machine-learning-challenge/problems/

airplane-accident-severity-hackerearth-machine-learning-challenge

Problem Link : https://www.hackerearth.com/challenges/competitive/airplane-accident-severity-hackerearth-machine-learning-challenge/

automated-data-analysis

Data Desciption, Visualization and pre-processing in just one click.

bgr-palette-with-trackbars

OpenCV is a library of programming functions mainly aimed at real-time computer vision. In this article, Let’s create a window which will contain RGB color palette with track bars. By moving the trackbars the value of RGB Colors will change b/w 0 to 255. So using the same, we can find the color with its RGB values.

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

dengue-disease-number-prediction

We will predict the number of dengue cases at a particular time period with some different features.

face-detection-using-haar-cascade-open-cv-

Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. For this, Haar features shown in the below image are used. They are just like our convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels under the white rectangle from sum of pixels under the black rectangle. Now, all possible sizes and locations of each kernel are used to calculate lots of features. (Just imagine how much computation it needs? Even a 24x24 window results over 160000 features). For each feature calculation, we need to find the sum of the pixels under white and black rectangles. To solve this, they introduced the integral image. However large your image, it reduces the calculations for a given pixel to an operation involving just four pixels. Nice, isn't it? It makes things super-fast. But among all these features we calculated, most of them are irrelevant. For example, consider the image below. The top row shows two good features. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose. But the same windows applied to cheeks or any other place is irrelevant. So how do we select the best features out of 160000+ features? It is achieved by Adaboost.

fruittti-birthday

hacktober1

hacktober3

hacktober5

hacktoberfest-2020

:octocat: Welcome to Open-source! Simply add your details to contributors | Repo for Hacktoberfest 2020 ✅

hactober2

hactober4

hitachi-ml-competition

house-sell-prediction

keras-handwritten-digits-datset-

lunar-rock-classification

Classify the Rocks whether these are large or small in size.

lung-cancer-tumor-analysis

markle.github.io

The KGEC E-Cell website‘s job is to tremendously promote entrepreneurship as much as possible and to conduct competitions such as Quiz and Pitchathon for helping one out to start with their new journey.The link for the website is :

ml-algorithms

mobile-phone-price-prediction-deployment

motion-tracking-using-webcam

Here we can detect the motion of our body using Computer Vision. I used the inbuilt webcam to catch the motion of by body and using the help of Open cv and a basic idea of contours, thresholding, i have made this small project which can detect your motion very efficiently. To diagnose it, we used red color. You can run the code on your notebook and can see the result .

movie-recommendation-system

Recommend movie based on collaborative filtering.

nlp

object-tracking-by-color

Here we will detect the object with its specific color. We will use only red,blue and green color array to detect or track objects. That means here only the objects of blue,red or green can be detected.

open-cv

power-bi-course-assignments

predict-ad-success-hackerearth-machine-learning-challenge

Problem Link : https://www.hackerearth.com/challenges/competitive/predict-ad-success-hackerearth-machine-learning-challenge/problems/

prediction-tumours

Dataset will be given to you, base on that features we will predict the type of the tumour. We will use ML algorithms here to predict