Project Title: Myntra Sales Analysis in R
Project Description: The objective of this project is to analyze and understand the sales data of Myntra. The dataset contains information about various products, including their brand, category, original price, discount price, ratings, and whether they were sold or not.
Link for Dataset: https://www.kaggle.com/code/swetanishad/data-analysis-of-myntra-store
The project involves the following steps:
Data Exploration: Explore the dataset to gain insights into the sales data. Analyze the distribution of variables, identify missing values, and understand any patterns or trends.
Data Cleaning: Handle missing values, outliers, and any inconsistencies in the dataset. Clean the data to ensure its quality and reliability for analysis.
Feature Engineering: Create new features that can provide additional insights into the sales data. For example, you can calculate the discount percentage, create categorical variables based on brand or category, or derive new variables based on existing ones.
Data Analysis: Perform various analyses to gain insights into the sales patterns. You can explore relationships between variables, identify the best-selling brands or categories, analyze the impact of discounts on sales, or evaluate the relationship between ratings and sales.
Predictive Modeling: Build a predictive model to predict whether a product will be sold or not based on the available features. Train the model on a portion of the dataset and evaluate its performance using appropriate metrics. You can use techniques like logistic regression, decision trees, or random forests for this task.
Interpretation and Reporting: Interpret the results obtained from the analysis and modeling. Prepare a report or presentation summarizing the findings, including key insights, patterns, and recommendations for the e-commerce platform to improve sales.