College MagzineLast Updated on May 3, 2021
This project is related to the college, Where staff can post the thoughts, files, images etc. students can have only watch feature. student should need to create an account with their college mail id. if any student try to register with personal mail this web app through an error.
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Regression Analysis On Wallmart Sales 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
1. Which store has maximum sales
2. Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation
3. Which store/s has good quarterly growth rate in Q3’2012
4. 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
5. 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.
Rock Paper ScissorsLast Updated on May 3, 2021
This is a handy game which is generally played between 2 players and which is certainly loved by every child on the earth. Each player performs 1 out of 3 shapes that is Rock, Paper, Scissors.
Rock beats scissors, Paper beats Rock and Scissors beat Paper.
There are 2 outcomes of this game which is loose or win. Random module is used in this game project. The random module will select a value between the given range. So as to install the random module, simply go to command prompt and type “pip install random”
There are 2 functions in this code which is “choose_option_for_user" and "computer_option".
In first function, it allows the player to choose one among rock paper and scissors and in the second function it allows the computer to make its choice. Here, the computer will choose the option randomly with the help of random module. And the last is the while loop, where we determine whether the player or the computer wins the round or whether it’s a tie.
The main logic of the game is that the player will choose their choice then the computer will choose the choice then both the choices will be compared and winner will be determined. If the player wants to play again then they can choose yes/no in it and if they doesn’t want to play it will break the loop.
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
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.
Customer Management SystemLast Updated on May 3, 2021
Customer Management System (CMS) deals with the collection and storage of contact information of customers, addresses, phone numbers and other contact details. Computerized system poses high speed of processing precision, storage, its versatility and high retrieval system makes possible the completion of task that could never have been achieved with manual system because of the difficulty of completing them on time for the results to be useful. In the present world it is very important for the organizations, shops etc. to maintain their customers data. This project will help the them to maintain their customers data. With the help of this project such firms will be able keep record of their customers. Since all the work is done through the system, there will be no usage of paper. Data about the customers will be stored in a secured way in order to avoid the misuse of information. Python programming language is used in the project. We will save the data in the database as well as in pickle format in the file. This project tends to use latest advancements in information technology and provide a CRUD operation. This will help many stakeholders of these firms to quickly do some basic operations instead of doing same manually.