Skip to content

Latest commit

 

History

History
35 lines (27 loc) · 1.29 KB

README.md

File metadata and controls

35 lines (27 loc) · 1.29 KB
Raw

Customer Churn Prediction

Project Overview

This project aims to predict customer churn in a telecom company using machine learning techniques. The objective is to identify customers who are likely to leave the company.

Dataset

The dataset contains various features such as:

  • CustomerID: Unique ID for each customer.
  • Gender, Age, Tenure, etc.: Customer demographics.
  • MonthlyCharges, TotalCharges: Charges information.
  • Churn: Target variable indicating if the customer has churned.

Project Steps

  1. Data Preprocessing:

    • Handling missing values.
    • Encoding categorical variables.
    • Feature scaling.

  2. Exploratory Data Analysis (EDA):

    • Visualizing the distribution of features.
    • Analyzing correlations between features and the target variable.

  3. Model Building:

    • Training various machine learning models like Logistic Regression, Decision Trees, and Random Forest.
    • Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score.

  4. Model Evaluation:

    • Comparing different models.
    • Selecting the best model based on evaluation metrics.

Usage

To run this project, ensure you have the required packages installed and execute the notebook.

Requirements

Refer to the requirements.txt file for a list of dependencies.