Customer attrition, also known as customer churn, is a critical challenge faced by businesses. The goal of this project is to develop a predictive model that can identify customers who are likely to churn, allowing the organization to implement targeted retention strategies and reduce customer churn rate. By understanding the key factors that influence customer churn, we aim to provide valuable insights that will help the company make informed decisions to improve customer retention and loyalty.
Before running the code, make sure you have the required Python packages installed. You can install them using pip:
pip install pyodbc
pip install python-dotenv
pip install pandas
pip install sklearn
pip install openpyxl
pip install imblearnimport pyodbc
from dotenv import dotenv_values
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import GridSearchCV
import warnings
warnings.filterwarnings('ignore')In this section, we load the data from your SQL Server database and other sources. Ensure you have your database credentials set up in a .env file.
Perform exploratory data analysis to understand the dataset. This includes data visualization, checking for missing values, and understanding the correlation between variables.
Address potential data issues such as missing values, data types, class imbalance, and feature scaling. Ensure that the data is prepared for model training.
Perform any necessary feature engineering to create new features or transform existing ones to improve model performance.
Train machine learning models such as Logistic Regression, Decision Trees, and Random Forest on the preprocessed data.
Evaluate the trained models using appropriate metrics and visualization techniques.
Fine-tune model hyperparameters using techniques like GridSearchCV to optimize model performance.
Interpret the model results and visualize key findings.
- Classifier: An algorithm that is used to map input data to a specific category.
- Classification Model: The model that predicts the input data given for training.
- Feature: An individual measurable property of the phenomenon being observed.
- Labels: The characteristics on which the data points of a dataset are categorized.
By following this README, you can replicate the process of predicting customer churn and gain valuable insights for your business. Customer churn prediction can help you proactively implement strategies to retain valuable customers and improve overall customer satisfaction.