Predict User's Annual Spending on E-commerce Website Using Machine Learning

Table of Contents

1. Introduction
2. Importing Required Libraries
3. Reading the Dataset
4. Data Preprocessing and Visualization
5. Splitting the Dataset into Train and Test Sets
6. Applying Linear Regression Model
7. Evaluating the Model
8. Conclusion
9. Future Scope
10. References

Introduction

In this article, we will explore how to Create and train a machine learning model to predict the annual spending of a customer on an e-commerce Website. We will use a multiple linear regression model for this task. The dataset used for training and testing the model will be provided.

1. Importing Required Libraries

To start this project, we need to import the necessary libraries such as numpy, pandas, and matplotlib. These libraries help with data manipulation, analysis, and visualization.

2. Reading the Dataset

We will use the pandas library to Read the dataset from a CSV file. The dataset contains various parameters like email, address, Avatar, average session length, time on app, time on website, length of membership, and yearly amount spent. We will analyze these parameters to identify which factors influence the annual spending.

3. Data Preprocessing and Visualization

To prepare the data for training, we will preprocess it by selecting the Relevant columns and splitting it into independent (X) and dependent (y) variables. We will Visualize the relationships between these variables using scatter plots.

4. Splitting the Dataset into Train and Test Sets

Before training the model, we need to split the dataset into a training set and a test set. The training set will be used to train the model, while the test set will be used to evaluate its performance. We will use the train_test_split function from the sklearn library for this purpose.

5. Applying Linear Regression Model

We will Apply the multiple linear regression model to our training dataset using the sklearn library. This model allows us to predict the annual spending Based on the selected independent variables. We will create an instance of the linear regression class and fit the model to our training data.

6. Evaluating the Model

After training the model, we will evaluate its performance by predicting the annual spending for the test dataset. We will compare the predicted values with the actual values to measure the accuracy of our model. The closer the predicted values are to the actual values, the more accurate our model is.

7. Conclusion

In conclusion, we have successfully created and trained a machine learning model to predict the annual spending of a customer on an e-commerce website. We used a multiple linear regression model and evaluated its performance using a test dataset.

8. Future Scope

There are several possible improvements and extensions to this project. For example, we can explore other regression models and compare their performance. We can also conduct feature engineering to select the most relevant independent variables. Additionally, we can further analyze the dataset to identify additional factors that affect annual spending.

9. References

• Kaggle
• GitHub