deadskull7

Agricultural-Price-Prediction-and-Visualization-on-Android-App

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

Cats-Vs-Dogs-CNN-using-Keras-

The training set consisted of 25,000 images out of which 5,000 images were taken out as validation data. Separate test data folder consisted of 12,500 images for which the labels were predicted using trained model. My work includes preprocessing for model, Data augmentation to prevent overfitting, callbacks in keras to reduce learning rate timely, various CNN architecture trials with different layers and hyperparameters for best fit and learning curve wrt epochs. I gained a validation accuracy of 87.15 % without using any pretrained imagenet models . VGG-16 gave around 89 % as validation accuracy.

Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

Mercedes-Benz-Challenge-78th-Place-Solution-Private-LB-0.55282-Top-2-Percent-

I have used various methods and techniques to reach this place on the private leaderboard. Actually, most of it is an art. Considerable feature engineering, transformations, redundancy, duplicate features, feature count of 378, so less rows that model could overfit easily ,inconsistent categories in training and test set and many more.....This dataset was something real to work on. And more than that anonymous features to engineer, that was a whole lot different thing to come across. Still after making around 45 different versions of my script I was able to come to top 2%.

MNIST-digit-recognition-and-classification-using-CNN-with-Keras-99.70

The training dataset consists of 42000 rows each of 784 pixel values thus representing 28 x 28 sized 42000 images of different digits from 0 to 9 . I have used Convolutional Neural Networks to train the model with the help of Keras and made predictions on the 28000 images of the test dataset, also achieved 99.321 % valid accuracy with just 10 epochs . Also tuned ImageDataGenerator to promote generalization and avoid overfitting problem .

New-York-Stock-Exchange-Predictions-RNN-LSTM

BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!

One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification

✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

Pneumonia-Diagnosis-using-XRays-96-percent-Recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

Rossmann-Store-Sales-Predictions

Kaggle top performer(Grandmaster) had a score of 0.10021. I had a self validation score of 0.10874 and a public score of 0.12516. Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. Prediction is of 6 weeks of daily sales for 1,115 stores located across Germany.

White-Blood-Cells-Classification

An important problem in blood diagnostics is classifying different types of blood cells. Various samples of dead WBCs have been used to identify the nuclearity (mononuclear or polynuclear) of them and classified as well using Convolutional Neural Networks . The WBCs in the data set are lymphocytes , monocytes , neutrophils , eosinophils , basophils. I have obtained an accuracy of 98.6% on this validation set .

deadskull7/Pneumonia-Diagnosis-using-XRays-96-percent-Recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

deadskull7/Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

deadskull7/One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification

✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

deadskull7/New-York-Stock-Exchange-Predictions-RNN-LSTM

BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!

deadskull7/Agricultural-Price-Prediction-and-Visualization-on-Android-App

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

deadskull7/Rossmann-Store-Sales-Predictions

Kaggle top performer(Grandmaster) had a score of 0.10021. I had a self validation score of 0.10874 and a public score of 0.12516. Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied. Prediction is of 6 weeks of daily sales for 1,115 stores located across Germany.

deadskull7/MNIST-digit-recognition-and-classification-using-CNN-with-Keras-99.70

The training dataset consists of 42000 rows each of 784 pixel values thus representing 28 x 28 sized 42000 images of different digits from 0 to 9 . I have used Convolutional Neural Networks to train the model with the help of Keras and made predictions on the 28000 images of the test dataset, also achieved 99.321 % valid accuracy with just 10 epochs . Also tuned ImageDataGenerator to promote generalization and avoid overfitting problem .

deadskull7/Face-Emotion-Classification-for-dementia-patients

The product being developed is a mobile application for android operating system. It is an emotion and pain assessment tool and can be incorporated on other platforms also, which satisfy the minimum requirements of system. The application will allow the doctors to select or capture an image of the patient to be assessed. Then the image will be uploaded to the server and given to the Convolutional Neural Network model to process. The model is trained to generate score of each possible emotion. Then the severity algorithm will work on generated scores. The result will be sent to app.

deadskull7/Mercedes-Benz-Challenge-78th-Place-Solution-Private-LB-0.55282-Top-2-Percent-

I have used various methods and techniques to reach this place on the private leaderboard. Actually, most of it is an art. Considerable feature engineering, transformations, redundancy, duplicate features, feature count of 378, so less rows that model could overfit easily ,inconsistent categories in training and test set and many more.....This dataset was something real to work on. And more than that anonymous features to engineer, that was a whole lot different thing to come across. Still after making around 45 different versions of my script I was able to come to top 2%.

deadskull7/Deep-Learning-Coursera

Deep learning assignments submitted while taking Andrew Ng Deep learning course at coursera .

deadskull7/awesome-deep-learning-papers

The most cited deep learning papers

deadskull7/Deep-Learning-Papers-Reading-Roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deadskull7/Keras-GAN

Keras implementations of Generative Adversarial Networks.

deadskull7/AdversarialNetsPapers

The classical paper list with code about generative adversarial nets

deadskull7/awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. New PR ☛☛☛

deadskull7/Kaggle-Contests

Solutions for various datasets and contests on Kaggle

deadskull7/LSTM-Human-Activity-Recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier

deadskull7/Mask_RCNN_Humanpose

Mask R-CNN for Human Pose Estimation on Keras and TensorFlow.

deadskull7/medical-imaging-datasets

A list of Medical imaging datasets. Source : https://sites.google.com/site/aacruzr/image-datasets

deadskull7/papers

:paperclip: Summaries of papers on deep learning

deadskull7/CLR

deadskull7/CryptocurrencyPrediction

Predict Cryptocurrency Price with Deep Learning

deadskull7/devol

Genetic ConvNet architecture search with Keras

deadskull7/ganhacks

starter from "How to Train a GAN?" at NIPS2016

deadskull7/ImageAI

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

deadskull7/pandas-ta

Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

deadskull7/plot_metric

Python package to simplify plotting of metric like ROC curve, confusion matrix etc..

deadskull7/quandl-python

deadskull7/recsys-music

Music/Songs Recommendation System

deadskull7/tacotron2

Tacotron 2 - PyTorch implementation with faster-than-realtime inference