Mini Project

Project Description

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 of this data science project 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.

The data has missing values as some stores do not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

We will handle this problem in a structured way. We will be following the table of content given below.

• 1).Problem Statement

• 2).Hypothesis Generation

• 3).Loading Packages and Data

• 4).Data Structure and Content

• 5).Exploratory Data Analysis

• 6).Univariate Analysis

• 7).Bivariate Analysis

• 8).Missing Value Treatment

• 9).Feature Engineering

• 10).Encoding Categorical Variables

• 11).Label Encoding

• 12).One Hot Encoding

• 13).PreProcessing Data

• 14).Modeling

• 15).Linear Regression

• 16).Regularized Linear Regression

• 17).RandomForest

• 18).XGBoost

• 19).Summary

Curriculum For This Project

1. The Business Problem Exploring

2. The Dataset

3. Exploratory Data Analysis (eda) - Outliers

4. Exploratory Data Analysis (eda) - Graphs

5. Converting Categorical To Numerical

6. Seperating Training And Test Data

7. Running The Models

8. Hyper Parameter Tuning XGB And GBR

9. Standard Scaling 06m Robust Scaling

10. Final Predictions On The Test Dataset

11. Saving The Final Model

Download The Data Set

307KB

9961_14084_bundle_archive.zip

archive

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