Pinned Repositories
2021-Olympics-in-Tokyo-Data-Visualization
asterisk-1
Mirror of the official Asterisk (https://www.asterisk.org) Project repository. No pull requests here please. Use Gerrit:
Big-Mart-Sales-Prediction
Sales Prediction for Big Mart Outlets The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet. Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales. Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly. Data Dictionary We have a train (8523) and test (5681) data set, the train data set has both input and output variable(s). You need to predict the sales for the test data set. Train file: CSV containing the item outlet information with a sales value Variable Description ItemIdentifier ---- Unique product ID ItemWeight ---- Weight of product ItemFatContent ---- Whether the product is low fat or not ItemVisibility ---- The % of the total display area of all products in a store allocated to the particular product ItemType ---- The category to which the product belongs ItemMRP ---- Maximum Retail Price (list price) of the product OutletIdentifier ---- Unique store ID OutletEstablishmentYear ---- The year in which the store was established OutletSize ---- The size of the store in terms of ground area covered OutletLocationType ---- The type of city in which the store is located *OutletType ---- Whether the outlet is just a grocery store or some sort of supermarket ItemOutletSales ---- sales of the product in t particular store. This is the outcome variable to be predicted. Test file: CSV containing item outlet combinations for which sales need to be forecasted Variable Description ItemIdentifier ----- Unique product ID ItemWeight ---- Weight of product ItemFatContent ----- Whether the product is low fat or not ItemVisibility ---- The % of the total display area of all products in a store allocated to the particular product ItemType ---- The category to which the product belongs ItemMRP ----- Maximum Retail Price (list price) of the product OutletIdentifier ----- Unique store ID OutletEstablishmentYear ----- The year in which store store was established OutletSize ----- The size of the store in terms of ground area covered OutletLocationType ---- The type of city in which the store is located OutletType ---- whether the outlet is just a grocery store or some sort of supermarket Submission file format Variable Description ItemIdentifier ----- Unique product ID OutletIdentifier ----- Unique store ID ItemOutletSales ----- Sales of the product in t particular store. This is the outcome variable to be predicted. Evaluation Metric Your model performance will be evaluated on the basis of your prediction of the sales for the test data (test.csv), which contains similar data-points as train except for the sales to be predicted. Your submission needs to be in the format as shown in the same sample submission. We at our end, have the actual sales for the test dataset, against which your predictions will be evaluated. We will use the Root Mean Square Error value to judge your response.
blog_app
Backend django App
eisershop
Fake-or-True-News-detection-and-Classification
fake_or_true_news_detection
Fece-Mask-Detection
home_rental_app
Basic React native Home rental app three page
how-to-rasa
Abdurrahmans's Repositories
Abdurrahmans/Big-Mart-Sales-Prediction
Sales Prediction for Big Mart Outlets The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet. Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales. Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly. Data Dictionary We have a train (8523) and test (5681) data set, the train data set has both input and output variable(s). You need to predict the sales for the test data set. Train file: CSV containing the item outlet information with a sales value Variable Description ItemIdentifier ---- Unique product ID ItemWeight ---- Weight of product ItemFatContent ---- Whether the product is low fat or not ItemVisibility ---- The % of the total display area of all products in a store allocated to the particular product ItemType ---- The category to which the product belongs ItemMRP ---- Maximum Retail Price (list price) of the product OutletIdentifier ---- Unique store ID OutletEstablishmentYear ---- The year in which the store was established OutletSize ---- The size of the store in terms of ground area covered OutletLocationType ---- The type of city in which the store is located *OutletType ---- Whether the outlet is just a grocery store or some sort of supermarket ItemOutletSales ---- sales of the product in t particular store. This is the outcome variable to be predicted. Test file: CSV containing item outlet combinations for which sales need to be forecasted Variable Description ItemIdentifier ----- Unique product ID ItemWeight ---- Weight of product ItemFatContent ----- Whether the product is low fat or not ItemVisibility ---- The % of the total display area of all products in a store allocated to the particular product ItemType ---- The category to which the product belongs ItemMRP ----- Maximum Retail Price (list price) of the product OutletIdentifier ----- Unique store ID OutletEstablishmentYear ----- The year in which store store was established OutletSize ----- The size of the store in terms of ground area covered OutletLocationType ---- The type of city in which the store is located OutletType ---- whether the outlet is just a grocery store or some sort of supermarket Submission file format Variable Description ItemIdentifier ----- Unique product ID OutletIdentifier ----- Unique store ID ItemOutletSales ----- Sales of the product in t particular store. This is the outcome variable to be predicted. Evaluation Metric Your model performance will be evaluated on the basis of your prediction of the sales for the test data (test.csv), which contains similar data-points as train except for the sales to be predicted. Your submission needs to be in the format as shown in the same sample submission. We at our end, have the actual sales for the test dataset, against which your predictions will be evaluated. We will use the Root Mean Square Error value to judge your response.
Abdurrahmans/2021-Olympics-in-Tokyo-Data-Visualization
Abdurrahmans/asterisk-1
Mirror of the official Asterisk (https://www.asterisk.org) Project repository. No pull requests here please. Use Gerrit:
Abdurrahmans/blog_app
Backend django App
Abdurrahmans/eisershop
Abdurrahmans/Fake-or-True-News-detection-and-Classification
Abdurrahmans/fake_or_true_news_detection
Abdurrahmans/Fece-Mask-Detection
Abdurrahmans/home_rental_app
Basic React native Home rental app three page
Abdurrahmans/how-to-rasa
Abdurrahmans/Movies_reviews_classification
Abdurrahmans/rasa_waadachatboot
Abdurrahmans/redux_toolkit
Basic Redux Example
Abdurrahmans/saddam
Abdurrahmans/Water-Quality-Prediction
Abdurrahmans/Abdurrahmans
Abdurrahmans/CybroAddons
Abdurrahmans/DataBreaches-2004-2021-
Abdurrahmans/demo_project
Abdurrahmans/E-commerce
Abdurrahmans/Fire-and-Smoke-Detection-
Abdurrahmans/Google-speech-to-text-python-websocket-server-using-microphone-stream
Python WebSocket server which converts input audio stream from microphone to text using Google speech to text
Abdurrahmans/javasritp
Javasript Dom Project Practice
Abdurrahmans/odoo
Odoo. Open Source Apps To Grow Your Business.
Abdurrahmans/odoodiscussions
Abdurrahmans/problem_solving
Abdurrahmans/socket_programing
Socket Programming
Abdurrahmans/String-Problem-Solving
Abdurrahmans/think-in-a-react-way
All the lessons given regarding React JS in Learn with Sumit React JS Tutorial Series called "Think in a React way"
Abdurrahmans/think-in-a-redux-way
Think in a Redux way - React-Redux basic to advanced Bangla course by Learn with Sumit