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