I've performed exploratory data analysis (EDA) on Walmart sales CSV files. I inspected the structure, calculated statistics, and visualized trends. Additionally, I engineered features, tested hypotheses, analyzed correlations, and explored geospatial patterns. This process aids in informed decision-making and strategy optimization.

1. Introduction
2. Dataset Overview
3. Exploratory Data Analysis (EDA)
4. Data Preprocessing
5. Data Exploration
6. Conclusion

This repository contains the Exploratory Data Analysis (EDA) conducted on Walmart sales data from four CSV datasets: stores, features, test, and train. The analysis aims to gain insights into sales patterns, trends, and factors influencing sales performance.

• Dataset Name: Walmart Sales Forecast
• Data Source: Kaggle
• Data Description:
  • Stores Dataset: Information about Walmart stores, including store number, type, and size.
  • Features Dataset: Additional features related to each store, such as temperature, fuel prices, and unemployment rates.
  • Train Dataset: Historical sales data including store number, department number, date, and weekly sales.
  • Test Dataset: Similar to the train dataset, used for model evaluation.

• Data Inspection: Check dataset structure, data types, and missing values.
• Summary Statistics: Calculate descriptive statistics for numerical variables.
• Data Visualization: Utilize visualizations like histograms, box plots, and time series plots to explore data distributions and trends.
• Feature Engineering: Create new features or transform existing ones to extract meaningful insights.
• Correlation Analysis: Examine relationships between variables.
• Hypothesis Testing: Formulate and test hypotheses about factors influencing sales.

• Data Cleaning: Handle missing values and outliers.
• Feature Scaling/Normalization: Normalize numerical features if needed.
• Feature Encoding: Encode categorical variables for model compatibility.
• Train-Test Split: Split the data into training and testing sets for model evaluation.

Descriptive Statistics: Calculate basic statistics (mean, median, standard deviation, etc.) for numerical features to understand their central tendencies and variability.

Univariate Analysis: Explore individual features using histograms, bar charts, and summary statistics. For example, you can analyze the distribution of store types, department types, or the frequency of sales over time.

Bivariate Analysis: Investigate relationships between pairs of variables. For instance, you can examine the correlation between the store number or the department number and the weekly casualities.

Multivariate Analysis: Explore interactions among multiple variables. You can create visualizations like heatmaps to identify patterns and trends.

The EDA provides valuable insights into Walmart sales data, including trends, patterns, and factors influencing sales performance. The findings can inform data-driven decision-making and optimization of business strategies to enhance sales efficiency and profitability.

For detailed analysis and code implementation, please refer to the Jupyter Notebook provided in this repository.