Dice Game WebsiteLast Updated on May 3, 2021
I have made this website as a frontend project.
Whenever I press refresh button it will give random no's of dice to both players and the player which has higher value will won the game
Share with someone who needs it
Bakery Management ApiLast Updated on May 3, 2021
# Bakery Management System
This **Bakery Management System** is based on Django Rest framework and Uses the Token Authentications.
For better understanding read the documentation at [docs](https://documenter.getpostman.com/view/14584052/TWDdjZYb).<br>
The project is live at [Bakery](https://bakery-management-api.herokuapp.com/).
## Steps to run the API:
1. Install requirements.txt
2. Run- python manage.py makemigrations
3. Run- python manage.py migrate
4. Run- python manage.py runserver
Now enter http://127.0.0.1:8000/ in your Browser this will give the details about the functionality offered.
To perform any of the operations mentioned just add the corresponding relative url to this http://127.0.0.1:8000/ .
***Note : All the endpoints corresponding to Ingredients and Dishes are only accessible to ADMIN.***
## MANAGING THE ACCOUNTS REGISTRATION AND AUTHENTICATION
### Registering a ADMIN
We can only register admin through the Django admin panel. To acces Django Admin panel you have to create a superuser
Follows these steps to register an ADMIN user:
1. python manage.py createsuperuser
2. Fill all details(username ,email and pasword)
3. Now got to http://127.0.0.1:8000/admin/ and login through the credentials you just entered.
4. Register admin through the USERS section(please tick the is_staff then only you will be considered as ADMIN)
### Registering a CUSTOMER
URL - http://127.0.0.1:8000/accounts/register/ REQUEST-TYPE =[POST] **:**
This uses POST request and expects username,email,password,first_name,last_name to be entered through JSON object or a Form data.The username needs to be UNIQUE
### LOGGING IN A USER
URL - http://127.0.0.1:8000/accounts/login/ REQUEST-TYPE =[POST] **:**
This uses POST request and expects username and password.After successfull login this will return a Token and Expiry.
Expiry denotes for how long is the token valid ,after the expiry you need to login again.
### LOGGING OUT A USER
URL - http://127.0.0.1:8000/accounts/logout/ REQUEST-TYPE = **:**
For this provide the token in the header.The user whose token you entered will be logged out.
## OPERATIONS ON INGREDIENTS(ACCESSIBLE ONLY TO ADMINS)
### Adding an Ingredient
URL - http://127.0.0.1:8000/ingredients/ REQUEST-TYPE =[POST] **:**
This uses POST request and expects name,quantity,quantity_type,cost_price to be entered through JSON object or a Form data.The name needs to be UNIQUE
and Django adds a primary key by name "id" by default.The quantity_type contains three choices only in which you can enter a single one either 'kg' for
kilogram ,'lt' for litre and "_" for only numbers.
### Get list of all Ingredients
URL - http://127.0.0.1:8000/ingredients/ REQUEST-TYPE =[GET] **:**
This returns a Json value containing the list of all ingredients.
### Getting details of a single Ingredients
URL - http://127.0.0.1:8000/ingredients/id/ REQUEST-TYPE =[GET] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the ingredient you want to fetch.This returns details of the
ingredient you mentioned.
### Deleting a single Ingredients
URL - http://127.0.0.1:8000/ingredients/id/ REQUEST-TYPE =[DELETE] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the ingredient you want to fetch.This deletes the
ingredient you mentioned.
## OPERATIONS ON MENU(ACCESSIBLE ONLY TO ADMINS)
### Adding an dish to menu
URL - http://127.0.0.1:8000/menu/ REQUEST-TYPE =[POST] **:**
This uses POST request and expects name , quantity , description , cost_price , selling_price , ingredients to be entered through JSON object or a Form data.
The name needs to be UNIQUE and Django adds a primary key by name "id" by default.The ingredients field can contain multiple ingredients id.
### Get list of all dishes(Available to CUSTOMER also)
URL - http://127.0.0.1:8000/menu/ REQUEST-TYPE =[GET] **:**
This returns a Json value containing the list of details of all dishes.
***Note-This API depend on the type of User logged in. If the Customer user is logged in than this will the name and prices only***
### Getting details of a single Dish
URL - http://127.0.0.1:8000/menu/id/ REQUEST-TYPE =[GET] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the Dish you want to fetch.This returns details of the
Dish you mentioned.
### Deleting a single Dish
URL - http://127.0.0.1:8000/ingredients/id/ REQUEST-TYPE =[DELETE] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the Dish you want to fetch.This deletes the
Dish you mentioned
## OPERATIONS ON ORDER(ACCESSIBLE TO THE CUSTOMER )
### Adding/Placing an order
URL - http://127.0.0.1:8000/order/ REQUEST-TYPE =[POST] **:**
This uses POST request and expects orderby,items_ordered to be entered through JSON object or a Form data.Django adds a primary key by name "id" by default.
The items_ordered field can contain multiple dishes id.
### Getting details of a single Order
URL - http://127.0.0.1:8000/order/id/ REQUEST-TYPE =[GET] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the Order you want to fetch.This returns details of the
Order you mentioned.
### Deleting a single Order
URL - http://127.0.0.1:8000/order/ REQUEST-TYPE =[DELETE] **:**
The "id" mentioned in the above url must be an integer referring to the "id" of the Order you want to delete.This deletes the
Order you mentioned
### Order History
URL - http://127.0.0.1:8000/order/history/ REQUEST-TYPE =[GET] **:**
This will return the all the orders placed by the Customer making the request.(Latest first)
HacktubeLast Updated on May 3, 2021
A Chrome extension that fights online harassment by filtering out comments with strong language.
YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.
What it does
HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.
How we built it
Challenges we ran into
Accomplishments that we're proud of
We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.
What we learned
We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.
What's next for HackTube
Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.
Finding Donors For Charity MlLast Updated on May 3, 2021
In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.
The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.
Oxygen Generator Plants By LindeLast Updated on May 3, 2021
Our PSA oxygen generator plants are based on a reliable, flexible and trouble-free vacuum pressure swing adsorption (VPSA) process. They are the perfect fit for on-stream applications that require low-cost gaseous oxygen with purity levels of up to 95 percent per volume.
Which Linde oxygen generator is right for you?
Our portfolio consists of three different types of oxygen generator (V)PSA plants as following:
- VPSA: Our customised oxygen VPSA plants range in capacity from around 300 Nm³/h up to 10,000 Nm³/h and can produce oxygen purities between 90 and 95 percent per volume.
- VPSA C series: We offer several pre-engineered, fully standardised and containerised VPSA plants for capacities between 300 Nm³/h and 2,000 Nm³/h (our C series). The C series plants are easily accessible and easy to maintain. They are quick to set up and commissioned on site and can also be easily relocated.
- PSA: Furthermore, we offer an alternative oxygen PSA process, without vacuum regeneration for low oxygen production capacities of 50 Nm³/h to 500 Nm³/h.
Our oxygen generator PSA and VPSA plants deliver a host of benefits including:
- Oxygen on demand
- Energy efficiency
- Easy partial load operation
- High availability
- Fully automated operation
Linde Engineering – Full flexibility in oxygen production
Linde Engineering is specialized in efficient plant construction. Our focus on customer demands enables us to develop plants with optimum energy efficiency that significantly reduce costs – whether oxygen production demands are high or low in volume.
ClassificationLast Updated on May 3, 2021
What is classification?
In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters.
Types of Classification
}3.1 Logistic Regression
}3.2 K-Nearest Neighbors (K-NN)
}3.3 Support Vector Machine
}3.4 kernel svm
}3.5 Naïve bayes
}3.6 Decision tree classification
}3.7 Random forest classification
Table of contents
}Importing the libraries
}Importing the dataset
}Splitting the dataset into the Training set and Test set
}Training the model on the Training set
}Predicting a new result
}Predicting the Test set results
} Making the Confusion Matrix
}Visualizing the Training set results
}Visualizing the Test set results
}Problem description: A car company is releasing a new suv car model . we are given a dataset of 400 outcomes with customer’s age , salary and whether they have purchased it before or not I have to predict which customer is going to buy that suv .
RESULT FOR ALL:
K-Nearest Neighbors (K-NN)
Support Vector Machine