Data Preprocessing for Neural Networks - Credit Card Default Dataset

This repository contains a step-by-step tutorial on data preprocessing for beginners, using the "Default of Credit Card Clients" dataset. The goal of this project is to prepare the dataset for a binary classification task using a neural network. This tutorial includes detailed explanations and code implementation in a Jupyter notebook format, covering the following preprocessing steps:

Data Loading: Loading the dataset using pandas. Exploratory Data Analysis (EDA): Checking for missing values, data types, and summary statistics. Handling Missing Values: Strategies to manage missing data through imputation or removal. Encoding Categorical Variables: Converting categorical features into numerical values using One-Hot Encoding. Feature Scaling: Normalizing continuous variables to a range of [0, 1] using Min-Max Scaling. Train-Test Split: Splitting the data into training and testing sets for model evaluation. The tutorial also includes a script for a voice-over that explains each step in simple terms, ideal for beginners to understand the essential preprocessing techniques required before feeding data into a neural network.

Files Included: ANN Assignment.ipynb: Jupyter notebook with the tutorial and code for data preprocessing. default of credit card clients.csv: The dataset used for this project. ANN Assignment.docx: Documentation with metadata and preprocessing explanation for the dataset. Voice-over Script: A script for an audio explanation that accompanies the tutorial. How to Use: Clone the repository. Open the Jupyter notebook file (ANN Assignment.ipynb). Follow the markdown explanations and run each code cell to complete the preprocessing steps.