Prediction Using Supervised Machine LearningLast Updated on May 3, 2021
PROJECT : PREDICTION USING SUPERVISED MACHINE LEARNING
Predict the percentage of an student based on the no. of study hours.
Dataset used : http://bit.ly/w-data
Github link : https://lnkd.in/g7eZh_6
Youtube link : https://lnkd.in/gw3q42d
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Human Computer Interaction Using Iris,Head And Eye DetectionLast Updated on May 3, 2021
HCI stands for the human computer interaction which means the interaction between the humans and the computer.
We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.
We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.
It is our moral responsibility to make it accessible to disabled people.
So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.
It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.
It also uses the formula theta equals tan inverse of b/a to calculate the deviation.
Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.
Here we actually build the neural network and train it to predict
Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.
As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.
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
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.
Web AppLast Updated on May 3, 2021
In this project I developed a web app using JSP and Html.
I've also used various styling using CSS.
This was a part of my academic project wherein I created a web app like pinterest .
I added a login page using JSP and if the password is incorrect it directs back to login page and if its correct it will direct to the main page where I've splitted the screen into various frameset using html .
In the main frame I've added marquee of html and at the top I've added various links like home page , know about us , show us our interest.
In the home page options it always directs us to the main page if we are at some other page and click at home page. I've used response.sendRedirect of JSP for the directing options to other pages.
In show us our interest I've added various interest options Using JSP using form of JSP which takes input of interest of the visitors.
On the left side of the main frame there are various options like photography , travel , hairstyle etc.
clicking upon them will direct to the page showing various pictures of that interest.
The main page is login.html used for opening the site.
The website runs of Local host .
The server used for the deployment is APACHE-TOMCAT.
The project was done under the guidence of our JAVA professor , through this we also learned various JAVA scriptlet concepts.
Identify The Best Model For Class Imbalance Data In Multiclass ProblemLast Updated on May 3, 2021
In Robust model for Imbalanced class of data, a research on an Infinite possibility of imbalanced class of data and we would like to investigate what are the best models through all possible imbalanced situation of a data set. Usually we do Up-Sampling Or Down-Sampling of the imbalanced data and make it balanced before applying machine learning models. In both the cases, We lose information about that data set. In this project, we would like to investigate what are the best models through all possible imbalanced situation of a data set. There is no particular definition for imbalanced class of data. In general, data that is not balanced is called imbalanced. Generating Data Points in Square Pattern Keeping a boundary classifying the data points as belongs to multiple class and name them as class_1, class_2 and class_3. Adding some jitter points to every data points to make every data points fall under different class and make them misclassify in itself. Make the balance dataset to imbalance by making one class with the proportion of samples like 1%,2%,3%.......10% keeping other classes same. Referring to the above that at least one of the class having significantly less number of training examples or the examples in the training data belonging to one class heavily outnumber the examples in the other class. Currently, most of the Machine learning algorithms assume the training data to be balanced like SVM, Logistic-Regression, Naïve-Bayes etc., Last few decades ,some effective methods have been proposed to attack this problem like upsampling, down-sampling, Smote etc…