Taskpat: Task Manager AppLast Updated on May 3, 2021
Node.js, Express.js, JWT, MongoDB, Mongoose, Postman
- It facilitates user to add and manage tasks as per their timed deadline and can mark complete once done.
- Users can register, login, and delete their profile. For authentication, Auth.js used with “JWT” this strategy allows user to login into their own profile and view their tasks.
- For Database, MongoDB used with Mongoose client to store data on an online Atlas Database server.
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Smart Bag TrackerLast Updated on May 3, 2021
Smart bag is an application-specific design that can be useful for almost everyone in the
society. The loss or mishandling of luggage in airports is increasing nowadays,
tremendously raising its associated costs. It is expected that the constant monitoring
detects possible errors in a timely manner, allowing a proactive attitude when correcting
this kind of situations. There are several devices in the market but all have some
problems such as power consumption, location, portability, etc. The current research
provides a novel idea to track the luggage in real time with the help of a microcontroller
system, which is wearable and handy. Using wireless communication techniques, the
proposed system has been designed.
The system consists of GPS module which will fetch the current latitude and longitude and
using advanced Wi-Fi enabled microcontroller which will connect to the 4G
hotspot internet and transmit the current location of the bag to the central server. Using an
Android App the user can view the current position of the bag in google maps.
There are a lot of applications to the luggage but all of them are not controlled from the luggage, instead the commands are sent from the mobile phone to the luggage via Machine to Machine communication. The mobile phone has a pre-installed application software with a pre-installed set of instructions. They wait for the user to send the commands. This can either be for tracking its location.
Machine Learning Implementation On Crop Health Monitoring System.Last Updated on May 3, 2021
The objective of our study is to provide a solution for Smart Agriculture by monitoring the agricultural field which can assist the farmers in increasing productivity to a great extent. Weather forecast data obtained from IMD (Indian Metrological Department) such as temperature and rainfall and soil parameters repository gives insight into which crops are suitable to be cultivated in a particular area. Thus, the proposed system takes the location of the user as an input. From the location, the soil moisture is obtained. The processing part also take into consideration two more datasets i.e. one obtained from weather department, forecasting the weather expected in current year and the other data being static data. This static data is the crop production and data related to demands of various crops obtained from various government websites. The proposed system applies machine learning and prediction algorithm like Decision Tree, Naive Bayes and Random Forest to identify the pattern among data and then process it as per input conditions. This in turn will propose the best feasible crops according to given environmental conditions. Thus, this system will only require the location of the user and it will suggest number of profitable crops providing a choice directly to the farmer about which crop to cultivate. As past year production is also taken into account, the prediction will be more accurate.
Natural Language ProcessingLast Updated on May 3, 2021
The problem statement is about allocation of projects using given dataset. We are provided with some requirements like project details (project name, project location and required project skills) and
candidate details (candidate id, location, candidate skills and description). From the given dataset, we have to filter the perfect candidate based on the requirements and their skills. Our work is to check whether the candidate is having required skills to do the project and also determine the evaluation status based on their location. If suppose the candidates is having required skills and match the location, the candidate is selected for that project, if does not match we reject the candidate for that project. In such case the rejected
candidates are checked with other projects. The foremost step is to clean up the data to highlight attributes.
Cleaning (or pre-processing) the data typically consists of a number of steps like remove punctuation, tokenization and remove stop words. I have taken a set of keywords which is most related to the skills that’s given in the project based on certain criteria .To describe the presence of keywords within the cleaned data we need to vectorize the data by Bag of Words. We are going to filter the candidate skills according to the current trends. Based on their number of skills known(languages) they will be prioritized. So, we want to use NLP Toolkit to arrange the candidates by their preferences. By doing this process in the given dataset, we can able to filter 50% of data. If the skills of the prioritized candidates match with same location of the project, the similarities will be calculated and the candidate is selected for that project else the candidate is rejected.
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 -
Retail Analysis Of Walmart DataLast Updated on May 3, 2021
One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An
ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.
Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.
This is the historical data which covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:
- Store - the store number
- Date - the week of sales
- Weekly_Sales - sales for the given store
- Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week
- Temperature - Temperature on the day of sale
- Fuel_Price - Cost of fuel in the region
- CPI – Prevailing consumer price index
- Unemployment - Prevailing unemployment rate
Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13
Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13
Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13
Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13
Basic Statistics tasks
- Which store has maximum sales
- Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation
- Which store/s has good quarterly growth rate in Q3’2012
- Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together
- Provide a monthly and semester view of sales in units and give insights
For Store 1 – Build prediction models to forecast demand
- Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.
- Change dates into days by creating new variable.
Select the model which gives best accuracy.