The project titled "Python_project_diwali-sales" utilizes Pandas, NumPy, Matplotlib, and Seaborn to analyze datasets and derive insights. It involves importing, cleaning, preprocessing, and transforming data using Pandas. NumPy is used for efficient computations on arrays, while Matplotlib and Seaborn are employed to create visualizations that reveal patterns and relationships within the data. The project follows a structured workflow of data acquisition, cleaning, exploratory analysis, visualization, and statistical analysis. To kickstart the project, I began by installing the necessary libraries using pip, including numpy, pandas, seaborn, and matplotlib. These libraries form the backbone of data analysis in Python and enable efficient data manipulation, statistical calculations, and data visualization. 💻🔍
Next, I imported the dataset, which contained Diwali sales data, using the pandas library. After examining the shape and initial records of the dataset, I performed data cleaning tasks, such as dropping irrelevant columns and checking for null values. I also transformed the 'Amount' column to an integer data type to facilitate further analysis. 🧹📝
To gain insights into the dataset, I conducted an exploratory data analysis (EDA) using various visualizations. With the help of Seaborn and matplotlib, I created insightful graphs, including bar charts, count plots, and bar plots, to understand patterns and trends within the data. 📊📋
Here are the key insights derived from the data analysis project: -Females accounted for the majority of buyers, and their purchasing power surpassed that of males. -The age group of 26-35 exhibited the highest number of buyers, predominantly composed of females. -Uttar Pradesh, Maharashtra, and Karnataka were the top states in terms of the number of orders and total sales. -Married women had a higher purchasing power compared to other marital status groups. -The most common occupations among buyers were IT, Healthcare, and Aviation. -Food, Clothing, and Electronics emerged as the most popular product categories. -Specific product IDs within the dataset demonstrated high sales figures, indicating their popularity.
These insights can inform business strategies, such as targeting marketing campaigns toward the identified demographic groups and focusing on popular product categories to maximize sales.
Libraries used -Pandas -Numpy -Matplotlib -Seaborn
-Python -Pandas -Numpy -Matplotlib -Seaborn