Introduction
The Telecom Customer Churn Prediction is a machine learning project that aims to predict the likelihood of customers leaving a telecom company. The project is developed using Python programming language and machine learning libraries such as scikit-learn, pandas, and numpy.
Problem Statement
The telecom industry is highly competitive, and retaining customers is one of the main challenges. Customer churn is a major problem for telecom companies as it impacts their revenue and growth. Therefore, predicting customer churn is essential for telecom companies to take necessary actions and prevent customers from leaving.
Dataset
The dataset used for this project is obtained from Kaggle, which contains 7,043 observations and 21 variables. The variables include customer demographics, services availed, and customer churn status.
Methodology
The project follows a standard machine learning pipeline, including data cleaning, preprocessing, feature engineering, model selection, and evaluation. The following steps are involved in the project:
- Data Cleaning: The dataset is cleaned by removing missing values and duplicates.
- Exploratory Data Analysis (EDA): EDA is performed to understand the relationships between variables and identify patterns.
- Preprocessing: The dataset is preprocessed by converting categorical variables into numerical values using encoding techniques.
- Feature Selection: Selecting the features
- Model Selection: Several machine learning models are trained and evaluated to select the best model for prediction.
- Evaluation: The performance of the selected model is evaluated using various evaluation metrics such as accuracy, precision, recall, and F1 score.
Machine Learning Models
The following machine learning models are used in this project:
- Logistic Regression
- Decision Tree
- Random Forest
- Navie Bayes
Requirements
The following libraries are required to run the project:
- Python 3.7 or higher
- scikit-learn
- pandas
- numpy
- matplotlib
- seaborn
Installation
Clone the repository:
git clone https://github.com/TarakRam1818/Telecom_customer_churn_Prediction..git
Navigate to the project directory: cd Telecom_customer_churn_Prediction
Install the required libraries: pip install -r requirements.txt
Conclusion The Telecom Customer Churn Prediction is a machine learning project that helps telecom companies predict customer churn and take necessary actions to prevent customers from leaving. The project demonstrates the use of various machine learning models and evaluation metrics to select the best model for prediction. The project can be further improved by using more advanced machine learning techniques and by collecting more data.