Analyzing shopping trends data using Python can provide valuable insights for businesses and retailers to make informed decisions. Below is a project description that outlines the key steps and components of a shopping trends data analysis project using Python. Objective: The objective of this project is to analyze shopping trends based on historical data to identify patterns, preferences, and potential opportunities for businesses and retailers.
The complete EDA Process is completed by three easy steps those are as follows-
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Data Collection:- I had acquired a dataset containing relevant shopping data. This could include information such as purchase history, customer demographics, product details, and transaction timestamps. this is a csv file.
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Data Cleaning and Preprocessing: The data cleaning process is one of the most important step in data analytics where we actually clean the data according to our requirement. This includes Following steps such as:
- Handle missing or incomplete data.
- Convert data types if needed.
- Remove duplicates.
- Standardize and normalize data.
- Exploratory Data Analysis (EDA): Here, I had Visualized data distribution using histograms, box plots, and scatter plots, Explored correlations between different variables. Identified outliers and anomalies. At the end I had mentioned the insights recovered from data so that it can help to take data driven buisiness decisions.