chirag314 / customer-churn- Goto Github PK

🚀 Getting Started In this binary classification task focused on predicting customer churn in a bank dataset, the primary objective is to discern whether a customer will exit the bank (1) or not (0). Leveraging essential features such as credit score, age, tenure, and balance, the modeling approach involves constructing a predictive algorithm, often employing logistic regression or other suitable classifiers. The model's performance will be evaluated using metrics such as accuracy, precision, and recall, providing insights into its efficacy in identifying potential churn among bank customers.

🔧 Tools and Libraries We will be using Python for this project, along with several libraries for data analysis and machine learning. Here are the main libraries we'll be using:

Pandas: For data manipulation and analysis. NumPy: For numerical computations. Matplotlib and Seaborn: For data visualization. Scikit-learn: For machine learning tasks, including data preprocessing, model training, and model evaluation. Gradient Boosting (e.g., XGBoost, LightGBM): Ensemble method building decision trees sequentially,Often yields high predictive performance. Handles complex relationships and feature interactions.

📚 Dataset The dataset for this competition, comprising 'train.csv' and 'test.csv', has been generated from a deep learning model trained on the Bank Customer Churn Prediction dataset. Although closely mirroring the original dataset, there are subtle variations in feature distributions. we are encouraged to explore these differences and are provided with the option to incorporate the original dataset during training for a comprehensive analysis of model performance.

🎯 Objective The primary objective of this competition is to predict the binary target variable 'Exited' in the test dataset. we will work with the training data ('train.csv'), where we are tasked with building models that can effectively determine the probability of a customer exiting the bank. The flexibility to use the original dataset facilitates an exploration of potential improvements in model performance by incorporating insights gained from the initial data source. The 'sample_submission.csv' file offers a format reference for the submission of predicted probabilities.

📈 Workflow Here's a brief overview of our workflow for this project:

Data Loading and Preprocessing: Load the data and preprocess it for analysis and modeling. This includes handling missing values, encoding categorical variables, and scaling numerical variables..

Exploratory Data Analysis (EDA): Explore the data to gain insights and understand the relationships between different features and the .

Model Training: Train the model on the preprocessed data.

Model Evaluation: Evaluate the model's performance using various metrics, such as accuracy, precision, recall, F1-score, Cohen's Kappa, and Matthews Correlation Coefficient.

Error Analysis: Analyze the instances where the model made errors to gain insights into potential improvements.

Future Work: Based on our findings, suggest potential directions for future work.

Let's get started!

About Churn 📚 This dataset is a rich source of information for understanding Exited outcomes prediction. Let's break down the features and their potential implications:

📝 [Describe features and their implications for survival prediction.]

📝 Bank churn is a dataset can be used to make a classification, machine learning, and data visualization.."Bank churn" typically refers to the phenomenon of customer churn within the banking industry. Customer churn, in this context, refers to customers closing their accounts or discontinuing their relationship with a bank. Understanding and predicting customer churn is crucial for banks as it allows them to implement strategies to retain customers, reduce attrition, and maintain a stable customer base. Datasets related to bank churn often include various customer-related features such as credit score, age, account balance, transaction history, and other relevant metrics. The goal is to analyze historical data to identify patterns and factors that contribute to customer attrition. Machine learning models trained on these datasets aim to predict which customers are more likely to churn, enabling proactive measures for customer retention.

The dataset contains sufficeint observations of Bank based on different attributes based on which churn Prediction

The Features in the dataset are:

CustomerId: Integer values representing unique customer identifiers.

Surname: Object (likely string) values representing the surnames of customers.

CreditScore: Integer values representing the credit score of customers.

Geography: Object values representing the geography or location of customers.

Gender: Object values representing the gender of customers.

Age: Float64 values representing the age of customers.

Tenure: Integer values representing the number of years a customer has been with the bank.

Balance: Float64 values representing the balance amount in the customer's account.

NumOfProducts: Integer values representing the number of bank products the customer is using.

HasCrCard: Float64 values representing whether the customer has a credit card.

IsActiveMember: Float64 values representing whether the customer is an active member.

EstimatedSalary: Float64 values representing the estimated salary of customers.

Exited: Integer values representing whether the customer has exited the bank (1 for exited, 0 for not exited).