The project focuses on predicting if a particular customer will leave or continue to stay in the bank based on various features like Tenure, Age, Balance, Gender, etc., using an Artificial Neural Network.

• Age
• Credit Score
• Gender
• Tenure
• Balance
• NumOfProducts
• HasCrCard
• IsActiveMember
• EstimatedSalary
• Geography

There are few steps which were followed while doing this project.

1. Data Extraction: Extracted thge data from the csv file from the kaggle folder.
2. Data Cleaning
   • The unnecessary columns like RowNumber, CustomerId were dropped.
   • The Object type column Gender was converted to 0 and 1 for Male and Female respectively.
   • One-Hot encoding was done for the Geographic location
   • Checked for all the null values and count.
3. Exploratory Data Analysis:
   • The correlation between each feature was checked using a pair plot
   • To get a deeper insight the pairplot was plotted against all these features.
   • To check if the data is balanced or not, the count of output feature - Exited values are plotted which shows that the data is imbalanced.
   • The Age based churn was also compared using a bar chart
4. Data Preparation All the features except for Exited is taken as the input feature and the output is created with the Exited feature. The dataset is then split into training and testing data and used in the model.
5. Model Building An Artificial Neural Network with hidden layer is created using ReLU and sigmoidal activation function, and the optimizer used is adam with a learning rate of 0.01. The accuracy of the model was found to be 87% and the loss measure used was binary crossentropy.

The confusion matrix was plotted and below were the values achieved.

• Pandas
• Numpy
• Seaborn
• Matplotlib
• tensorflow
• keras
• sklearn