anVSS1/PFA--Revealing-Insights-

This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.

Sales Analytics and Prediction Project

This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn,customer lifetime value (CLV) prediction, forecast product demand, and segment customers based on their purchasing behavior.

Table of Contents

• Project Overview
• Data
• Models and Analyses
• Power BI Dashboard
• Usage
• Limitations and Future Work

Project Overview

This repository contains Jupyter Notebooks and Python scripts for comprehensive sales analysis, addressing these key business challenges:

1. Customer Churn Prediction: Identifies customers at risk of leaving, enabling proactive retention strategies.
2. Demand Forecasting: Forecasts future product demand to optimize inventory levels and sales planning.
3. Customer Segmentation: Groups customers based on purchase behavior, enabling targeted marketing and personalization.
4. Customer Lifetime Value (CLV) Prediction: Estimates the long-term value of each customer to guide resource allocation and customer relationship management.

Data

• data_cleaned.csv: Cleaned dataset containing transaction-level information (customer ID, order ID, date, quantity, total price, product details).
• Data Source: You can download the data file from here: https://drive.google.com/file/d/10JEt8sq1NEhuv5AI7FDAmufsCy0qhVYC/view?usp=drive_link

Models and Analyses

Churn Prediction

• Model: XGBoost Classifier (optimized via GridSearchCV)
• Features:
  • Aggregated metrics: total orders, average order value, recency, frequency, unique products purchased
  • Calculated features: time since first/last purchase, change in purchase frequency, etc.
• Evaluation:
  • Metrics: Accuracy, precision, recall, F1-score, ROC-AUC, Precision-Recall Curve.
  • Interpretation: SHAP (SHapley Additive exPlanations) for understanding feature importance.

Demand Forecasting

• Model: Facebook Prophet
• Data: Aggregated daily (or other frequency) sales data.
• Optional: External regressors (holidays, promotions, etc.).

Customer Segmentation

• Model: K-Means Clustering
• Features: Standardized subset of relevant customer features.
• Evaluation: Elbow method, Silhouette analysis, interpretability of clusters.

Customer Lifetime Value (CLV)

• Model: BG/NBD (Beta Geometric/Negative Binomial Distribution) model from the lifetimes library
• Features: Recency, frequency, monetary value of transactions
• Output: Predicted CLV for each customer over a 12-month period.

Power BI Dashboard

This interactive dashboard provides a user-friendly way to explore and visualize the key insights from your sales data analysis. It's organized into three main sections, offering a holistic view of customer behavior, product performance, and overall sales trends.

Sales Report

1. Revenue Analysis:

   • Map: Geographic distribution of sales figures by city.
   • Animation: Evolution of customer segments based on total spending.
   • Line Chart: Total sales trend over time, broken down by month and year.
   • Table: Detailed sales data for each city, with summaries by family, range, and order counts.

2. Product Insights:

   • Cards: Top-selling products, sub-families, and ranges.
   • Table: Granular order data, including product counts and order values.
   • Pie Chart: Distribution of orders by day of the week.

Clients Analysis

1. Customer Overview:

   • Card: Number of customers
   • Card: City with the most customers
   • Histogram: Customer count by city
   • Interactive Map: Exploration of customer locations.
   • Timeline/Table: First and last order dates for each customer.

2. Churn Insights:

   • Pie Chart: Proportion of churned vs. not churned customers.
   • Table: Detailed customer data, including churn predictions.

Products Analysis

1. Product Overview:
   • Cards: Number of products and unique ranges.
   • Interactive Map: Sales by city and product.
   • Table: Product details with associated range, order counts, and total quantity sold.

Usage

1. Install Packages: pip install pandas numpy scikit-learn xgboost prophet lifetimes plotly ipywidgets
2. Jupyter Notebooks:
   • data_preparation.ipynb: Load and preprocess your data.
   • churn_prediction.ipynb: Train and evaluate the churn prediction model.
   • demand_forecasting.ipynb: Generate demand forecasts.
   • customer_segmentation.ipynb: Perform customer segmentation.
   • clv_prediction.ipynb: Calculate Customer Lifetime Value.
3. Examine Results: Look at visualizations, evaluation metrics, and SHAP explanations.
4. Apply Insights: Translate findings into actionable recommendations for your business.

Limitations and Future Work

• Explore Additional Models: Experiment with alternative algorithms for churn prediction and demand forecasting.
• Incorporate External Data: Enhance demand forecasts by including data on promotions, holidays, and other relevant factors.
• Expand Dashboard: Add more visualizations and interactivity to the Power BI dashboard.