Utilising Blockchain Technology In Control And Distribution Of Pharmaceutical SuppliesLast Updated on May 3, 2021
In a recent report by the world health Organisation, drug counterfeiting has been identified as a global problem. It estimates that in low- and middle-income countries, every 10th drug in market circulation is counterfeit or has a poor quality. The use of such substandard products may have a negative impact on the mortality rate. Medicines move through a supply chain in which several participants participate. These usually include the manufacturer, wholesaler and retailer. They are engaged in the production, transportation and sale of these products. Also in these systems, there is a key participant - the regulating authority responsible for each stage of the movement of batches of products throughout the chain. In particular, at the state level, this participant may be some authorised body of the state apparatus, for example, a special Agency for the control of turnover of medicinal products. Its main task is to delegate the rights to manufacture medicines according to state standards, as well as to control the movement of all units of goods ever produced. As for the consumer, there is another problem - the control of drugs, issued only by prescription. Dispensing without a prescription is illegal, however, the control of honesty of retailers, as well as with counterfeit medicines, is not easy and requiring a special approach.
This project is still in progress and is expected to be completed by Nov 2021.
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Government Fund Tracking System Using BlockchainLast Updated on May 3, 2021
The main idea behind the project is to track the funds hierarchically i.e from central government to the common man including in this chain. We have considered four hierarchical components which are: Central government, state government, Contractor, resource provider/dealer. In the beginning, the budgets which would get finalized in the house will be uploaded according to their respective category. After funds allocation state government will instigate the required projects by documenting them and will send the document to the central government. Now the Central government will verify the project details and if satisfied, they will grant the project funds to the state government else they can reject the project. After receiving funds from the central government, the state government will open the tenders for the contractor and by proper bidding system the contractor will be chosen for the specific project. As bidding and tender allocation will be carried out by an automation bidding system with no human intervention involved, it would reduce corruption. Government committee will check the amount of work done synchronously and will mark every progress by submitting a brief report to the hierarchical officer, who will add it to the blockchain. In this report the progress can be portrayed in the form of images, videos, written plan of the building or structure, etc. To get the payment the contractor will have to submit a form of his total spendings with proper distribution over the duration. This form details will then be checked by the respective authority of the state government and then will initiate the payment to the contractor. In this way doing work over a period gets paid, this process will repeat until a particular work is being done completely.
Disease Prediction SystemLast Updated on May 3, 2021
This is a demo project to elaborate how Machine Learn Models are deployed on production using Flask API
You must have Scikit Learn, Pandas (for Machine Leraning Model) and Flask (for API) installed.
This project has four major parts :
- model.py - This contains code fot our Machine Learning model to predict employee salaries absed on trainign data in 'hiring.csv' file.
- app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
- request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
- templates - This folder contains the HTML template to allow user to enter employee detail and displays the predicted employee salary.
Running the project
- Ensure that you are in the project home directory. Create the machine learning model by running below command -
This would create a serialized version of our model into a file model.pkl
- Run app.py using below command to start Flask API
By default, flask will run on port 5000.
- Navigate to URL http://localhost:5000
Enter valid numerical values in all 3 input boxes and hit Predict.
- You can also send direct POST requests to FLask API using Python's inbuilt request module Run the beow command to send the request with some pre-popuated values -
Eda On Sample_SuperstoreLast Updated on May 3, 2021
In this project I have performed Exploratory Data Analysis on a Sample_Superstore Dataset and concluded some of the major key insights which helps the store to generate more revenue. I have used python programming language to perform operations on my dataset. Firstly I identify the missing values , null values and the outliers. I used the libraries to make my work more easier, some of them are pandas, numpy, matplotlib and seaborn . It is must to remove outliers from our dataset because if we don't it will effect our result and produce wrong observations. So to remove them I have used IQR i.e. Interquartile range . With the help of matplotlib and seaborn library I visualize some of my observations with the help of graphs and charts. I have used heatmap to define the co-relation between different features present in our dataset which gives us a brief idea about how one feature is related to other features. I have used different different segments from my dataset for performing analysis and I have concluded some of the parameters like which segment is producing highest profit, loss , discount . Not just finding out the problems I have also mentioned some of the solutions by observing my result in order to increase the profit gain and to reduce the losses faced by the the store. I have performed detailed analysis on this dataset and All these observations that I have performed will definitely help the store to overcome with the problems
Foreign Direct Investment (Fdi)Last Updated on May 3, 2021
I have used "TABLEAU" as my data visualization tool in the project. I have taken the dataset of foreign direct investment and perform various operations to produce interactive dashboard with the help of the functions provided by the tableau. I think we should have some knowledge of a business intelligence tool like tableau , PowerBI because to build these amazing dashboard we can't use programming language . It will be very complex to draw such visualization with the help of programming languages. That's why I have taken this project so that I can improve my Knowledge in these BI tools. Here I have tried to understand the trend of investments through the years with the help of some unique and different graphs and charts. I have concluded some of the observations like the sectors which are getting the heigest investment and which sectors are getting very minimum investments as well as the growth levels of the different sectors. I have also visualized that how the magnitude of investment is changing across the years. I have also tried to scale the future trend of investment for the upcoming years. I can't explain my all observation here because it will go too lengthy I will mention my project link below But If I want to conclude my project in a line , what I have done is in this project I have covered all the aspects of investments over the years and produced a interactive and eye-catching dashboard.
A Review On Weather Forecasting Techniques Using Machine LearningLast Updated on May 3, 2021
Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.
Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.
In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.
Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller