BigMart_SalesAnalysis-and-Prediction

README

What is this repository for?

• Quick summary This paper analyses the Big Mart Sales Prediction, in an effort to create an effective sales predictor that can be used for business decision-making purposes

• Problem Statement The aim is to build a predictive model and find out the sales of each product at a particular store.

• Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales

How do I get set up?

How to run

1. pip install requirements.txt: pip install -r requirements.txt
2. run python sales_prediction.py with arguments found in Command Line Arguments section below.

Example:

python sales_prediction.py \
    --input_Test=resources/Test.csv \
    --input_Train=resources/Train.csv \
    --output=output/Lasso Regressor.csv\
    --output=output/Linear Regression.csv\
    --output=output/Random Forest Regressor.csv

Command line arguments

1. --input_Test: The input Oyster LinkIndex file . Must be a full path to a tsv file. For example, --input_OysterLinkIndex=/Users/mohammedmutaharshaik/Desktop/UALR /Courses+Assignments/3 rd semester /ML/ML Main projects /resources/Test.csv.
2. --input_Train: The input DWM LinkIndex file. Must be a full path to a csv file. For example, --input_DWMLinkIndex=/Users/mohammedmutaharshaik/Desktop/UALR /Courses+Assignments/3 rd semester /ML/ML Main projects /resources/Train.csv.
3. --output: The output file. Must be a full path to a csv file. For example, --output=/Users/mohammedmutaharshaik/Desktop/UALR /Courses+Assignments/3 rd semester /ML/ML Main projects /Output

Contribution guidelines

• Writing tests
• Code review
• Other guidelines

Who do I talk to?

• Repo owner or admin
• Other community or team contact