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build-chatbots-with-python

Build chatbots for various tasks using python programming language

cardiovascular_disease_prediction_with_neural_network

In this project, I will use a dataset from Kaggle to predict the survival of patients with heart failure from serum creatinine and ejection fraction, and other factors such as age, anemia, diabetes, and so on. Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs, and this dataset contains 12 features that can be used to predict mortality by heart failure. Most cardiovascular diseases can be prevented by addressing behavioral risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity, and harmful alcohol use using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia, or already established disease) need early detection and management wherein a machine learning model can be of great help.

cover_type_classification

Objective: Build a deep learning model to predict the forest cover type from different cartographic variables. Given: 1. Cover Types: ['Spruce/Fir', 'Lodgepole Pine','Ponderosa Pine', 'Cottonwood/Willow','Aspen', 'Douglas-fir', 'Krummholz'] 2. A csv file ('cover_data.csv') that contains 581012 observations. Each observation has 55 columns (54 features and the last one being the class). Assumption(s): 1. There are no separate test dataset. So, one must hold-out a small percentage of given input as test data. 2. There is no information about the use of predictions. Hence, we do not know how what to focus on (precision or recall). Generally, it's a good idea to have both scores 'high'. Expected output: 1. A good model. 2. Model performance over epochs (accuracy, loss plots) 3. Some classification metrics (heatmap of confusion-matrix, classification-report etc). 4. Conclusions, thoughts and ways to improve classification accuracy.

covid-19_and_pneumonia_identification

The dataset contains X-ray lung scans with examples of patients who had either pneumonia, Covid-19, or no illness. Using the Keras module, I will create a classification model that outputs a diagnosis based on a patient’s X-ray scan. I hope this model can help doctors with the challenge of deciphering X-ray scans and open a dialogue between the research team and the medical staff to create learning models that are as effective and interpretable as possible.

covid19-classificaion-using-convnets

full-blockchain-solidity-course-js

Learn Blockchain, Solidity, and Full Stack Web3 Development with Javascript

hamoye

This is a repository for Hamoye ML externship 2021

heart_disease_research

In this project, I’ll investigate some data from a sample patients who were evaluated for heart disease at the Cleveland Clinic Foundation. The data was downloaded from the UCI Machine Learning Repository and then cleaned for analysis.

identifying_pneumonia_by_x-ray_images_using_convnet

2.56 million people died from pneumonia in 2017. Almost a third of all victims were children younger than 5 years, it is the leading cause of death for children under 5. Pneumonia is an infection of the tiny air sacs of the lungs, called alveoli. In a person with pneumonia the alveoli are filled with pus and fluid, which makes breathing painful and reduces the oxygen intake. Pneumonia is caused by a number of different infectious agents, including viruses, bacteria and fungi.

jamming

Jamming is a web app that lets you create and save playlists to your Spotify account

kaggle-notebooks

Sample notebooks for Kaggle competitions

language_translation_engine

Building a language translation engine using seq2seq and LSTM neural models. For the course of the project, I will be translating French to English.

medical_insurance_cost

A model to predict medical insurance cost in the USA

movfind

nba_trends_analysis

neural_network_admissions_prediction

For this project, I will create a deep learning regression model that predicts the likelihood that a student applying to graduate school will be accepted based on various application factors (such as test scores).

neural_network_life_expectancy

In this project, I will design, train, and evaluate a neural network model performing the task of regression to predict the life expectancy of various countries

new_may

pythonchatbot

This is a simple chatbot made with nltk in python

version-ctrl-with-git