Scikit-learn-Machine-Learning-in-Python-demo1

We’re working with customer segmentation data saved in our folder name as Test.csv. To help you with K-Means clustering and customer segmentation.

Scikit-learn is a powerful library for machine learning in Python, and it can be effectively used for EDA projects. Here’s a detailed guide on how to document and execute an EDA project using Scikit-learn, along with a code example:

1. Project Overview

Title: Customer Segmentation using K-Means Clustering

Abstract: This project aims to segment customers based on their purchasing behavior and demographics using the K-Means clustering algorithm.

Introduction: Customer segmentation helps businesses understand their customer base and tailor marketing strategies accordingly. This project uses data on customer purchases to identify distinct customer segments.

2. Data Description

Data Sources

• Sales Records: Contains data on customer purchases.
• Customer Profiles: Includes demographic information of customers.

Data Dictionary

|Variable|Description|Data Type|Unit| |customer_id|Unique identifier for each customer|int|-| |age|Age of the customer|int|years| |total_spent|Total amount spent by the customer|float|USD| |purchase_frequency|Number of purchases made|int|-| |recency|Days since last purchase|int|days|

Initial Observations

• The dataset contains 1,000 records with 5 variables.
• Missing values are present in the age and total_spent columns.

3. Data Preprocessing

Data Cleaning

• Handle missing values in age and total_spent.
• Remove duplicates.

Data Transformation

• Standardize numerical features.
• Create new features if necessary.

4. Exploratory Data Analysis (EDA)

Univariate Analysis

• Histograms and box plots for age, total_spent, purchase_frequency, and recency.

Bivariate Analysis

• Scatter plots and correlation matrices.

Multivariate Analysis

• Pair plots and heatmaps.

5. Modeling

Model Selection

• Use K-Means clustering for segmentation.

Model Training

• Train the K-Means model and determine the optimal number of clusters using the elbow method.

Model Evaluation

• Evaluate clusters using silhouette scores and interpret cluster characteristics.

6. Results

Key Findings

• Summary of customer segments and their characteristics.

Visualizations

• Visual representations of clusters.

Insights and Conclusions

• Actionable insights for marketing strategies.

7. Discussion

Challenges

• Addressing missing values and determining the optimal number of clusters.

Limitations

• Limited by the scope of available data.

Future Work

• Explore advanced clustering algorithms and incorporate additional data sources.

8. Appendices

Code

• Well-commented code provided in a Jupyter Notebook.

Additional Materials

• Raw data files and intermediate datasets.

9. References

• Relevant research papers and documentation links.