Repository containing all the tasks assigned to me during my internship tenure at Technocolabs.
Data Analysis on Dr. Semmelweis and the Discovery of Handwashing Dataset
Under this project we had to perform Exploratory Data Analysis on Dr. Semmelweis and the Discovery of Handwashing Dataset and had to statistically prove the importance of washing hands before surgery to prevent child bed fever.
Under this project we were given a credit card dataset for 30000 customers, which contained the past 6 months credit history of customers. Our task was to perform Exploratory Data Analysis on the Data, derive valuable insights from the data and build a machine learning model that can predict whether the customer will default next month or not.
Deployed Web Application Link: https://predict-credit-card-default.herokuapp.com/
Forecasting blood supply is a serious and recurrent problem for blood collection managers: in January 2019, "Nationwide, the Red Cross saw 27,000 fewer blood donations over the holidays than they see at other times of the year." Machine learning can be used to learn the patterns in the data to help to predict future blood donations and therefore save more lives.
In this Project, you will work with data collected from the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes its blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. The dataset, obtained from the UCI Machine Learning Repository, consists of a random sample of 748 donors. Your task will be to predict if a blood donor will donate within a given time window. You will look at the full model-building process: from inspecting the dataset to using the tpot library to automate your Machine Learning pipeline.
Here, The model used is Logistic Regression as we have selected the model using TPOT library. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
In App, the input parameters to be specified are :
- (Recency - months since the last donation)
- (Frequency - total number of donation)
- (Monetary - total blood donated in c.c.)
- (Time - months since the first donation) and the result is the Target i.e
Target - (1 stands for donating blood; 0 stands for not donating blood).
Also, along with prediction, the prediction probability is also specified.
Deployed Web Application: https://predict-blood-donation.herokuapp.com/
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
MIT © Anmol Pant