Clustering The Super Market Data-(Java)

Last Updated on May 3, 2021

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This project i have done using java plain code by taking logic of Clustering. 

I have clustered the people into different groups(clusters) using their data. 

By using this model we can cluster people into groups which helps in 

improving business of the market.

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Nyc Yellow Taxi Prediction

Last Updated on May 3, 2021

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I did this project in my second semester of Mtech studies at Ahmedabad University. In NYC, taxicabs come in two varieties: yellow and green; they are widely recognizable symbols of the city. Taxis painted yellow (medallion taxis) are able to pick up passengers anywhere in the five boroughs. in Upper Manhattan, the Bronx, Brooklyn, Queens, Staten Island. The yellow taxi cab was first introduced in 1915 by a car salesman named John Hertz. Hertz decided to paint his taxis yellow because of a study by a Chicago university to establish what color would grab the attention of passers-by more easily. The results proved that yellow with a touch of red was most noticeable. As a result, Hertz started to paint all his taxicabs yellow and went on to start the Chicago-based Yellow Cab Company in 1915. During pre-processing of data there were many outliers such as there was 100 dollars fare for a 0-mile trip. Then there were few outliers in rate code id. We pre-processed and removed them all and cleaned the data. After cleaning the data we visualized data in which we got different insights people like to travel single in the taxi. Area 236 has the most taxi bookings. Also, we observed that at midnight (1 to 6 am) people don’t like to travel much often. FOr the prediction part, we predicted the fare using different regression methods and for taxi booking, we used k means clustering.

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Python Snake Game

Last Updated on May 3, 2021

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This game reminds everyone their childhood memories.

In this snake game, the player has to move the snake to the fruit in order to eat it. The score will increase once the fruit is eaten. Also, the length of the snake will increase if the snake eats the fruit. The game will get over if the snake touches itself.

The turtle and random modules are used in this game project. So as to install these libraries, simply type “pip install turtle” and “pip install random” on the command prompt.

Turtle library allows us to create pictures, diagrams in a virtual form whereas random module gives the value between the given range of it.

There are 3 functions defined in this game which is “change”, “inside function”, and “move” function. In change function, the x-axis and y-axis are defined. In inside function, the logic of the game is written and in the move function, movement to the snake is given.

There are 4 keys mentioned in the code “right, left, up, down”.

If the player presses the right key, the snake will move to right direction, If the player presses the left key , the snake will move to left direction, If the player presses the up key , the snake will move to upward direction, If the player presses the down key , the snake will move to downward direction and if the snake touches itself the game will get over.

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Online Gardening Store

Last Updated on May 3, 2021

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This is a project made in Nodejs, MySQL and some npm packages .The aim of the project is to provide gardening people a easy interface from where they could buy necessities for gardening through online. There are various categories of the products from which the user can buy them.

We have options of adding options into cart, modifying them as well as deleting the required items. We have user authentication also in the application, To make it easier for the customers while making a payment we have an option from where one can directly choose the saved cards for the payment, Taxes are also calculated on the sub total once obtained. As of now no payment integration is done. Once a user submits the order, he/she will also able to see the history of their previous orders.

Once a user registers in the application or even when he/she confirms a order a verification of the order as well as login is sent to the registered email-id and mobile numbers.

For future enhancement we have thought of :-

  • adding a filtering options
  • search feature
  • Take user input through some forms for their requirement and use NLP to retrieve the necessary products
  • A chatbot for the whole application for the customers if they have any queries
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Long Term Tool

Last Updated on May 3, 2021

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My previous project was shear project project that is Long term tool .This tool is used by wind farm owners who want to know in which location it is going to give best profits.

Suppose A wants to start a wind farm business A is having money but he is not aware of wind speeds at particular location ,so he took help from B (The wind pioneers) wind pioneers uses sensor for every wind station to find the wind speed and wind direction. Here wind pioneers role is to record the data which contain wind speeds and wind directions for every hour.

wind pioneers measuring wind speeds at various heights of sensor like ws_120m,ws_100m. For each minute we have some observations ,for every hour the number of observations will increases ,so it is very large data to deal. so we cannot do manual calculations for analyzing this big data. So here we come up with one tool that is long term tool.

I worked on this project along with team this tool provide you interactive software for performing all the analysis like plots, correlation values, scatter plots for finding relationship between two variables. You can just simply download the files that you are working for. It will going to give you everything in detail.

Here we are taking Reference data as NASA data of past 30 years which contains wind speed and wind direction In order to predict the wind speeds of particular location for next 30 years by making use of linear regression model .

Here we are predicting wind speeds of next 30 years for particular location by taking reference data as NASA data.

We are performing linear model for various time periods 1hr,6hr,1 day,3day,7day,10 day,1 month. Again sometimes your weather file and climate file may be differ with time In order to compensate time period we are using time shifting for reference file.



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Retail Analysis Of Walmart Data

Last Updated on May 3, 2021

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DESCRIPTION

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.

 

Dataset Description

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

 

Holiday Events

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

 

Analysis Tasks

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

 

Statistical Model

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.


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