PYTHON_DIWALI_SALES_ANALYSIS

A Python project to analyze and visualize Diwali sales data, providing insights to optimize business strategies and boost profitability during the festive season.

• Created by:- NIKHIL PANDA
• Tools used:- Python,Jupyter Notebook

Project Description :-

• The Diwali Sales Analysis with Python is an insightful data analysis project that aims to explore and derive valuable insights from sales data during the festive season of Diwali. Diwali, also known as the Festival of Lights, is one of the most significant festivals celebrated in various regions globally, and it often witnesses a surge in consumer spending.

• The primary objective of this project is to analyze historical Diwali sales data using Python programming language and various data analysis libraries. By employing descriptive and exploratory data analysis techniques, we will uncover patterns, trends, and factors influencing sales during this festive period.

Key Objectives:-

The objective can be broken down into the following detailed component.

• Understanding the data_set.

• Exploratory Data Analysis (EDA) and data cleaning

• Analyzing the data to get useful insights

• The findings and solutions to the problem will be presented using a variety of charts and visualizations.

Data Source:

The Hotel Booking data is available on Kaggle. Download data CSV files: Diwali Sales Data.csv

Approach :

Approach:

1. Data Loading: Download the hotel booking dataset from Kaggle and load it into Jupyter Notebook using Python.

2. Data Preprocessing: Clean the data, handle missing values, and format date columns for analysis.

3. Exploratory Data Analysis (EDA): Conduct Exploratory Data Analysis (EDA) to gain insights into the dataset, visualize trends across different columns, and uncover valuable patterns and information.

4. Time Series Analysis: deployed time series techniques to uncover patterns and trends over a continuous sequence of time data.

5. Insights and Recommendations: Summarize the findings in a comprehensive report, and provide actionable recommendations to address sales decline and improve business performance.

6. Link For ipynb_file.

Conclusion:

The following are the conclusions drawn from analyzing this dataset.

• Married women age group 26-35 yrs from UP, Maharastra, and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing, and Electronics category

🔗 Links