This is the third T5 Data Science Bootcamp project, which is about building classification models that address a useful prediction and/or interpretation problem using Python with Sklearn. Below is a detail about the automobile company we collaborating with to help them with their problem, the dataset description, and the tools we will be using for the project.
An automobile company is planning to enter new markets with its existing products (P1, P2, P3, P4, and P5). After doing extensive market research, they conclude that the behavior of the new market is similar to the behavior of the existing market. In the current market, the sales team has categorized all customers into 4 segments (A, B, C, D). Next, they conducted segmented outreach and outreach to a different segment of clients. This strategy worked very well for them. They plan to use the same strategy for new markets and have identified 2,627 new potential customers. We have to help the manager to predict the right group of new clients.
The dataset is Customer Segmentation from Kaggle Website, it contains 8068 rows and 11 columns. For a better understanding of the database there is the description of a column below:
| Variable | Definition |
|---|---|
| ID | Unique ID. |
| Gender | Gender of the customer. |
| Ever_Married | Marital status of the customer. |
| Age | Age of the customer. |
| Graduated | Is the customer a graduate?. |
| Profession | Profession of the customer. |
| Work_Experience | Work Experience in years. |
| Spending_Score | Spending score of the customer. |
| Family_Size | Number of family members for the customer (including the customer). |
| Var_1 | Anonymised Category for the customer. |
| Segmentation | (target) Customer Segment of the customer. |
These are the technologies and libraries that we will be using for this project:
- Technologies: Python, Jupyter Notebook.
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, plotly, Scikit-learn.
Please feel free to let me know if you have any questions. Email: tahani.almutery@gmail.com and maram.alfaifi@hotmail.com