Gas & Electricity Utility Customer Churn Analysis

Project Overview

This project focuses on customer churn analysis for a gas and electricity utility provider. The goal is to identify key factors contributing to client churn and develop a strategic investigative framework. By leveraging Python (Pandas and NumPy) for data analysis and visualization techniques for trend interpretation, the project aims to engineer and optimize a Random Forest model to enhance churn prediction accuracy, with a target of achieving 85%. The findings are summarized in an executive report for the Associate Director, providing actionable insights to support decision-making.

Dataset - Client

Name	Description
id	Client company identifier
activity_new	Category of the company’s activity
channel_sales	Code of the sales channel
cons_12m	Electricity consumption of the past 12 months
cons_gas_12m	Gas consumption of the past 12 months
cons_last_month	Electricity consumption of the last month
date_activ	Date of activation of the contract
date_end	Registered date of the end of the contract
date_modif_prod	Date of the last modification of the product
date_renewal	Date of the next contract renewal
forecast_cons_12m	Forecasted electricity consumption for next 12 months
forecast_cons_year	Forecasted electricity consumption for the next calendar year
forecast_discount_energy	Forecasted value of current discount
forecast_meter_rent_12m	Forecasted bill of meter rental for the next 12 months
forecast_price_energy_off_peak	Forecasted energy price for 1st period (off peak)
forecast_price_energy_peak	Forecasted energy price for 2nd period (peak)
forecast_price_pow_off_peak	Forecasted power price for 1st period (off peak)
has_gas	Indicates if the client is also a gas client
imp_cons	Current paid consumption
margin_gross_pow_ele	Gross margin on power subscription
margin_net_pow_ele	Net margin on power subscription
nb_prod_act	Number of active products and services
net_margin	Total net margin
num_years_antig	Antiquity of the client (in number of years)
origin_up	Code of the electricity campaign the customer first subscribed to
pow_max	Subscribed power
churn	Indicates if the client churned over the next 3 months

Dataset - Price

Name	Description
id	Client company identifier
price_date	Reference date for the pricing data
price_off_peak_var	Price of energy for the 1st period (off peak)
price_peak_var	Price of energy for the 2nd period (peak)
price_mid_peak_var	Price of energy for the 3rd period (mid peak)
price_off_peak_fix	Price of power for the 1st period (off peak)
price_peak_fix	Price of power for the 2nd period (peak)
price_mid_peak_fix	Price of power for the 3rd period (mid peak)

Project Objectives

1. Data Cleaning & Preprocessing

   • Handle missing values and inconsistencies in data.
   • Convert date fields into meaningful time-based features.
   • Normalize categorical data for machine learning models.

2. Exploratory Data Analysis (EDA)

   • Perform statistical summaries: mean, median, variance, standard deviation.
   • Conduct correlation analysis using heatmaps.
   • Visualize data distribution through histograms, scatter plots, box plots, and line plots.

3. Machine Learning Models

   • Feature Engineering: Encode categorical variables using Label Encoding.
   • Churn Prediction: Train a Random Forest classifier to predict customer churn.
   • Model Evaluation: Use accuracy score, classification report, and confusion matrix to assess performance.
   • Optimization: Tune hyperparameters to achieve an 85% prediction accuracy target.

Technologies Used

• Programming Language: Python
• Libraries: pandas, numpy, scikit-learn, matplotlib
• Data Visualization Tools: seaborn, plotly

Project Workflow

1. Data Collection: Import and inspect datasets.
2. Data Cleaning & Preprocessing: Handle missing values, transform features, and encode categorical variables.
3. Exploratory Data Analysis (EDA): Generate statistical summaries and data visualizations.
4. Feature Engineering: Create new features to improve model performance.
5. Model Training: Train and optimize the Random Forest classifier for churn prediction.
6. Model Evaluation: Assess prediction performance using classification metrics.
7. Results Interpretation: Provide actionable insights for business decisions.