Data Science Project in Python on BigMart Sales Prediction

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

The Business Problem
02m
Exploring The Dataset
08m
Exploratory Data Analysis (eda) - Outliers
08m
Exploratory Data Analysis (eda) - Graphs
06m
Converting Categorical To Numerical
05m
Seperating Training And Test Data
02m
Running The Models
10m
Hyper Parameter Tuning XGB And GBR
09m
Standard Scaling
06m
Robust Scaling
05m
Final Predictions On The Test Dataset
05m
Saving The Final Model
03m