sid321axn/skin_cancer_detection_webapp

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sid321axn/skin_cancer_detection_webapp

sid321axn

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Stock-Price-Prediction-LSTM

In this project, I have tried to predict the stock price of Microsoft using LSTM

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rasa_ml_bot

Heart-Disease-Prediction-Using-Machine-Learning-Ensemble

Jupyter Notebook

Detection_of_Malicious_URLs

In this project, we have detected the malicious URLs using lexical features and boosted machine learning algorithms

Jupyter Notebook

malaria-detection-app

Web app for Malaria detection from the human blood sample images which is trained on National Library of Medicine dataset using Flask and Python.

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bank_fin_embedding

This repository consists of customized word embedding focused on banking and finance terms which will be helpful in analyzing and classifying financial sentiments or stock price sentiment analysis.

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foreign-exchange-prediction-using-deep-learning

Jupyter Notebook

Udacity-MLND-Capstone-Gold-Price-Prediction

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

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fake-news-classifier

This repository contains different machine learning and deep learning approaches for detection of fake news from headlines

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Early-stage-diabetes-prediction-using-Machine-Learning

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Skin_Cancer_Detection_Transfer_Learning

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housing_price_prediction_king_county_USA

In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. Maximum Accuracy achieved around 98.59%.

machine_learning_my_projects

Machine Learning

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Topic-modeling

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Named-Entity-Recognizer-Deep-Learning-and-SPACY

restaurant-chatbot

kidney-diagnosis-app

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heart_risk_prediction_spark_automl

Timeseries_prediction

This repository is a collection of timeseries projects

Jupyter Notebook

detecting-corona-using-Modified-Xceptionet

Detecting of COVID-19 induced Pneumonia in Chest X-ray Images using using Modified XceptionNet

Jupyter Notebook

rasa_project_git

bangalore-housing

boston_housing

In this project, we will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts.

text_summarizer_through_url

In this repo I have implemented text summarization from websites url using NLTK using sentence scores.

Jupyter Notebook

dog_project

Apache-Spark-Tutorial

This is the repository for Apache Spark Projects from data handling to MLlib

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Naive-Bayes-SMS_SPAM_CLASSIFIER

In this project I have implemented a Naive Bayes Classifier to predict whether SMS is spam or ham

Jupyter Notebook

corona-webapp

malicious_url_scanner

text_summ_app

bang-housing

Patient_Condition_Classification_Using_Drug_reviews

In this project, we have classified patient condition using drug reviews dataset

Jupyter Notebook