Gateway to AI

Aim: The purpose of this post is to deal with Exploratory Data Analysis and Hypothesis testing of the Big Mart Sales dataset. This is the first step to my machine learning problem on predicting sales. So, all the changes and transformations  that will be done in this post will be mainly focused on how to make life easier for my models to predict sales. Dataset: This dataset contains 8523 observations and 12 features. Variable Description Item_Identifier Unique product ID Item_Weight Weight of product Item_Fat_Content Whether the product is low fat or not Item_Visibility % of total display area in store allocated to this product Item_Type Category to which product belongs Item_MRP Maximum Retail Price (list price) of product Outlet_Identifier Unique store ID Outlet_Establishment_Year Year in which store was established Outlet_Size Size of the store Outlet_Location_Type Type of city in which store is located Outlet_Type Grocery store or some sort of sup...

Aim: Given sales data for 1559 products across 10 stores of the Big Mart chain in various cities. The task is to build a model to predict sales for each particular product in different stores. Dataset Features:   Variable Description Item_Identifier Unique product ID Item_Weight Weight of product Item_Fat_Content Whether the product is low fat or not Item_Visibility % of total display area in store allocated to this product Item_Type Category to which product belongs Item_MRP Maximum Retail Price (list price) of product Outlet_Identifier Unique store ID Outlet_Establishment_Year Year in which store was established Outlet_Size Size of the store Outlet_Location_Type Type of city in which store is located Outlet_Type Grocery store or some sort of supermarket Item_Outlet_Sales Sales of product in particular store. This is the outcome variable to be predicted. Handling Missing Values: 2 features have missing values: Item_Weight - 1463   ...

Problem Statement: About Company Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Problem Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.  Introduction The objective of this project is to do “Exploratory Analysis” and “Hypothesis Testing” on the features of this dataset to find various insights as to how each feature affects the chances of getting a loan. Dataset The Loan Prediction dataset consists of 613 ...