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Exploring Big Mart Sales Data: EDA with Python

Table of Contents

Introduction
Importing the Required Libraries
Reading and Visualizing the Data
Analyzing the Categorical Data
- Exploring Null Values in the Dataset
- Fixing the Outlet Size Column
- Fixing the Item Fat Content Column
- Exploring the Outlet Identifier Column
- Exploring the Item Type Column
- Exploring the Outlet Location Type and Outlet Type Columns
Analyzing the Numerical Data
- Descriptive Statistics of Numerical Data
- Analyzing the Item Weight Column
- Analyzing the Item Visibility Column
- Analyzing the Item MRP Column
- Analyzing the Outlet Establishment Year Column
Bivariate Analysis
- Comparing Item Type and Item Outlet Sales
- Comparing Outlet Size and Item Outlet Sales
- Comparing Outlet Type and Item Outlet Sales
- Correlation Analysis using Heatmap
Conclusion

Introduction

In this data analysis, we will be analyzing the Big Mart sales dataset. This dataset contains information about various products sold in different outlets of Big Mart. We will perform exploratory data analysis to gain insights into the dataset and draw Meaningful conclusions based on the analysis.

1. Importing the Required Libraries

Before we start analyzing the data, we need to import the necessary libraries for performing data operations and visualization. We will import libraries such as pandas, numpy, matplotlib, seaborn, etc.

2. Reading and Visualizing the Data

Next, we will read the data from a CSV file and Visualize the data using the head() function to get an understanding of the dataset. We will also use the Shape attribute to determine the size of the dataset.

3. Analyzing the Categorical Data

The dataset contains both numerical and categorical data. In this section, we will focus on analyzing the categorical data. We will explore null values in the dataset, fix any inconsistencies in categorical columns, and analyze each categorical column individually.

3.1. Exploring Null Values in the Dataset

We will analyze the dataset for null values using the info() method and calculate the count of null values using the isnull().sum() method. We will also visualize the null values using bar plots and replace them with appropriate values.

3.2. Fixing the Outlet Size Column

We will analyze the Outlet Size column, replace the null values with the most common Outlet size, and verify if the null values have been replaced successfully.

3.3. Fixing the Item Fat Content Column

The dataset contains different notations for the item fat content column. We will fix these inconsistencies by using the replace() function and create a count plot to verify the fixed labels.

3.4. Exploring the Outlet Identifier Column

We will analyze the Outlet Identifier column and change the axis for better readability. We will also get a count of the different Outlet identifiers using the value_counts() method.

3.5. Exploring the Item Type Column

Next, we will analyze the Item Type column and create a count plot to visualize the different item types in the dataset.

3.6. Exploring the Outlet Location Type and Outlet Type Columns

We will analyze the Outlet Location Type and Outlet Type columns together as subplots and gain insights into the distribution of different outlet types and location types in the dataset.

4. Analyzing the Numerical Data

After analyzing the categorical data, we will focus on analyzing the numerical data in the dataset. We will use descriptive statistics to get an overview of the numerical data and further analyze each numerical column individually.

4.1. Descriptive Statistics of Numerical Data

We will use the describe() method to obtain statistical details of the numerical data, such as mean, max, standard deviation, etc.

4.2. Analyzing the Item Weight Column

We will analyze the Item Weight column to identify any Patterns or insights. We will plot a histogram to visualize the distribution of item weights.

4.3. Analyzing the Item Visibility Column

Next, we will analyze the Item Visibility column and plot a histogram to understand the distribution of item visibility in the dataset.

4.4. Analyzing the Item MRP Column

We will analyze the Item MRP column and gain insights into the different distributions of item maximum retail prices. This will help us understand the pricing patterns in the dataset.

4.5. Analyzing the Outlet Establishment Year Column

We will analyze the Outlet Establishment Year column to understand the establishment years of the outlets. This analysis will give us an idea of the distribution of outlets over the years.

5. Bivariate Analysis

In this section, we will perform bivariate analysis to examine the relationship between individual variables with respect to the target variable, which in this case is the Item Outlet Sales.

5.1. Comparing Item Type and Item Outlet Sales

We will create a bar plot to compare the Item Type column with the Item Outlet Sales and observe the products that contribute significantly to the outlet sales.

5.2. Comparing Outlet Size and Item Outlet Sales

We will compare the store sales for different Outlet sizes using a bar plot and analyze the sales volume based on the size of the outlets.

5.3. Comparing Outlet Type and Item Outlet Sales

We will analyze the outlet type and outlet location type with respect to the item outlet sales using bar plots. This analysis will help us understand the sales pattern in different outlet types and location types.

5.4. Correlation Analysis using Heatmap

Lastly, we will plot a heatmap to visualize the correlation between different numerical features and the Item Outlet Sales. This analysis will help us identify the features that have the highest correlation with the sales.

6. Conclusion

In this data analysis, we have explored the Big Mart sales dataset and gained valuable insights into the dataset. Some key findings from the analysis include:

Majority of the outlets in the dataset are of medium size.
Majority of the food items in the dataset are low fat items.
Fruits and vegetables, followed by snack foods, are the most available products in the stores.
Supermarket Type 1 is the most common outlet type in the dataset.
The Item MRP has the highest correlation with the Item Outlet Sales.

These findings provide important information that can be used for further analysis and decision-making in the retail industry.

Highlights

Performing in-depth data analysis on the Big Mart sales dataset
Analyzing categorical and numerical data
Exploring null values and fixing inconsistencies in categorical columns
Analyzing the distribution and patterns in numerical columns
Performing bivariate analysis to examine relationships between variables
Identifying key findings and insights from the analysis

FAQ

Q: What is the size and composition of the Big Mart sales dataset?\ A: The dataset consists of both numerical and categorical data with a specific number of rows and columns.

Q: What are the major findings from the data analysis on the Big Mart sales dataset?\ A: Some major findings include the distribution of outlet sizes, the prevalence of low fat food items, the availability of different products in the stores, and the correlation between item price and outlet sales.

Q: How can the insights from this analysis be used in the retail industry?\ A: The insights can be used to make informed decisions regarding inventory management, pricing strategies, and store operations in the retail industry.

Q: What are the limitations of this data analysis?\ A: The limitations include the scope of the dataset and the assumptions made throughout the analysis. The findings should be interpreted within the context of the dataset and may not be applicable to all retail scenarios.

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