Hi I am Abdur Rahman

Software& Machine Learnig Engineer

I am a Software & Machine Learning Engineer Apart from that I am learning continue different technology such as Machine Learning, Deep Learning,Chatbot,Tensorflow,Pytorch,Darknet,NLTK,Spacy,NLP,Data Science and contributing to open source| I am currently working on Django,Machine Learning,React & ODOO.

Skills:

Languages & Web Framework:

ML/DL/NLP

Database:

Tools & technologies:

IDEs:

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Abdur Rahman's Projects

asterisk

Crystal-lang shard to manage all the Asterisk PBX libraries (ARI, AMI and all types of AGI)

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.