Customer Review Analysis for E-Commerce Data Analysis Project

Overview

This project focuses on analyzing customer reviews for an e-commerce platform using Google Colaboratory. The dataset contains key parameters such as customer name, surname, product name, rating (ranging from 1 to 5), and their comments. By leveraging Python and its libraries, particularly Pandas, NumPy, Matplotlib, and Seaborn, we aim to gain insights into customer sentiment, product performance, and areas for improvement.

Dataset

The dataset consists of the following parameters:

• Name: First name of the customer.
• Surname: Last name of the customer.
• Product name: Name of the product being reviewed.
• Rating: A numerical rating ranging from 1 to 5, reflecting the customer's satisfaction level.
• Comment: Free-form text where customers provide detailed feedback on their experience with the product.

Project Structure

• Data Preprocessing: Cleaning and preparing the dataset for analysis, including handling missing values and data normalization.
• Exploratory Data Analysis (EDA): Visualizing the distribution of ratings, identifying trends, and exploring relationships between variables.
• Sentiment Analysis: Utilizing Natural Language Processing (NLP) techniques to analyze the sentiment of customer comments.
• Feature Engineering: Creating new features such as comment length to enhance analysis.
• Visualization: Generating visualizations to present insights effectively, including histograms, bar plots, and word clouds.
• Advanced Analysis: Exploring correlations between variables and conducting deeper analysis.
• Reporting: Summarizing key findings and insights for stakeholders.

Tools and Technologies

• Google Colaboratory: Leveraging the power of cloud-based Jupyter notebooks for collaborative data analysis.
• Python Libraries: Utilizing Pandas, NumPy, Matplotlib, Seaborn, NLTK, and WordCloud for data manipulation, visualization, sentiment analysis, and text processing.

Getting Started

1. Clone the Repository: Clone the project repository to your local machine.
2. Install Dependencies: Ensure you have the required Python libraries installed. If not, install them using pip install -r requirements.txt.
3. Upload Dataset: Upload the provided dataset to Google Colaboratory or specify the file path accordingly.
4. Run the Notebook: Execute the notebook sequentially, following the steps outlined in the sections.

Conclusion

By analyzing customer reviews for e-commerce products, this project aims to provide valuable insights for businesses to enhance customer satisfaction, optimize product offerings, and drive overall improvement in the e-commerce experience. Feel free to contribute, explore further, or adapt the analysis to suit specific business requirements.