A comprehensive project focusing on data preprocessing, regression analysis, marketing analytics, and customer behavior analysis using machine learning techniques.
This project consists of three main subprojects that focus on data preprocessing, regression analysis, marketing analytics, and customer behavior analysis using Python and machine learning techniques. The goal is to derive insights from customer data to enhance online shopping experiences and optimize marketing strategies.
This subproject involves loading and preprocessing the dataset, followed by implementing PCA (Principal Component Analysis). Key tasks include:
- Importing necessary libraries
- Loading the dataset using
pandas - Handling missing values
- Encoding categorical features and normalizing the data
- Implementing PCA from scratch
In this subproject, various regression techniques are applied, and association rules are generated. Key tasks include:
- Performing linear, polynomial, ridge, lasso, and elastic net regression
- Visualizing data distribution and correlation
- Applying association rule mining techniques
This subproject focuses on analyzing customer behavior in e-commerce using machine learning techniques. Key tasks include:
- Loading and cleaning the dataset (
customers_intention.csv) - Data visualization of customer purchase intentions
- Implementing classification using various regression models
- Dimensionality reduction and clustering of customer data
- Over-sampling techniques for imbalanced data
To run this project, you will need to have Python and the following libraries installed:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- mlxtend
- imbalanced-learn
You can install these libraries using pip