Data Science Project in Python on BigMart Sales Prediction

The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.


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What will you learn

  • Understanding the problem Statement

  • Importing the Dataset and performing basic EDA

  • Checking for the null values and describing the variables

  • Imputation of the Null-Values using pivot tables

  • Feature Engineering/ Creating New features

  • Using seaborn to understand the contribution of the categorical values on target variables

  • Using boxplot for identifying outliers

  • Fixing categorical variables using Label and One hot encoding

  • Applying Linear, Bayesian Regression models

  • Applying ensemble bagging models like Random Forest and Bagging models

  • Applying boosting models like Gradient Boosting Tree and XGboost

  • Applying Neural Network model MLPRegressor

  • Making function for On spot-checking and selecting the best for hyperparameter tuning

  • Defining function for HyperParameter tuning

  • Standardization and effect of Standardization

  • Understanding Robust Scaler and Normalization

  • Implementing Robust Scaler and Normalization

  • Concluding the final model and predicting for the test data set

  • Saving the model using Joblib

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.

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Curriculum For This Mini Project

03h 46m