Decision-Tree-Project-ML

Project Goal This project aims to build a complete Decision Tree Classification Model using a structured workflow. The purpose is to identify the key patterns in the dataset, run Exploratory Data Analysis (EDA), perform Feature Engineering, train a Decision Tree model, evaluate it, and finally allow users to test predictions.

1. Exploratory Data Analysis (EDA)

The first step is understanding the dataset. In this phase, we: Inspected the shape, missing values, and data types Visualized the target variable distribution Examined relationships using countplot Explored categorical variables and their relationship with the target

Goal of EDA: To understand the structure of the data, detect imbalance, find important features, and prepare for Feature Engineering.

2. Feature Engineering

This step prepares the dataset for modeling. It included:

Handling missing values Dropping duplicated or unnecessary columns Encoding categorical variables (Label Encoding, One-Hot Encoding) Generated a correlation poxplot for numerical features Scaling numerical features when necessary

Goal of Feature Engineering: To transform raw data into a clean, machine-learning-ready format that improves model performance.

3. Building the Decision Tree Model

In this phase, we:

define the target and feature Split the dataset into training and testing sets useing the randomizedsearchCV to select the pest hyperparamere Built a Decision Tree using: Criterion: Gini Index or Entropy ,Max depth tuning ,Minimum samples for split and leaf

Trained the model on the training set

Goal of Model Building: To create a Decision Tree capable of learning patterns and making accurate predictions.

4. Model Evaluation

We evaluated the model using:

Accuracy Score : Accuracy was good around 85% and that is mean it can detect patrterns effectively

Feature Importance analysis and we found that the most influential featurs MonthlyIncome _ Total Working Years _ Marital Status
Stock Option Level _ Distance From Home _ Age

Goal of Evaluation: To measure how well the model predicts and identify strengths and weaknesses.

5. User Testing

The project allows user interaction the model

Conclusion

This project demonstrates the complete workflow of building a Decision Tree classifier, starting from raw data to a fully functional predictive model. It serves as a practical example of how machine learning models are developed in real-world applications.

project related file

DICSION TREE CODE contains the full code for execution

employee attration the datast

the data source is from kaggle