Project and Dataset Introduction

The dataset used in the project is a dataset used in an internship assignment given by a company in America. The dataset provides information on whether the customer has given up using that bank, considering some customer characteristics. And our aim is to first understand our dataset better by doing a detailed data analysis, analyze the customers who have left, and make important decisions about the future of the company with some visualizations. Then, by establishing various machine learning algorithms, to predict whether a customer will leave the bank and to take some precautions accordingly.

Project steps

1. Exploratory data analysis : Statistical summaries, distributions, and patterns were examined to understand the underlying features of the data.
2. Data visualization : Graphs, charts, and visuals are used to better understand data and gain insight.
3. Data preprocessing : Data is cleaned, transformed and normalized to make it suitable for machine learning algorithms.
4. Machine learning algorithms implementation : Models are created using various machine learning algorithms to make predictions and classifications from data.
5. Analysis of results : The performance of the created models is evaluated, the results are interpreted and the necessary steps to improve the model are determined.

Machine learning algorithms used

• Support Vector Machines
• Decision Trees
• Random Forest
• XGBoost
• LightGBM
• KNN Algorithm
• Bonus Deep Learning algorithms