Decision Tree Classifier

PROJECT TITLE

Customer Purchase Prediction using Decision Tree Classifier

GOAL

Aim: To predict whether a customer will purchase a product or service based on demographic and behavioral data using a Decision Tree Classifier.

DATASET

Bank Marketing Dataset - UCI Machine Learning Repository

DESCRIPTION

This is a classification problem where we need to predict if a customer will make a purchase. The dataset consists of demographic and behavioral features of customers, which are used to build a Decision Tree Classifier model.

WHAT I HAD DONE

1. Performed exploratory data analysis (EDA) on the dataset.

   • Loaded the dataset and viewed the top 5 rows.
   • Calculated statistical data for features in the dataset.
   • Analyzed the correlation between the features.
   • Performed data visualization using libraries like matplotlib and seaborn.

2. Preprocessed the data:

   • Handled missing values.
   • Encoded categorical variables into numerical formats.
   • Split the data into training and testing sets.

3. Built the Decision Tree Classifier model:

   • Used Gini and Entropy as criteria to create two different decision tree models.
   • Optimized the model by tuning hyperparameters such as max_depth and min_samples_split.

4. Evaluated the model using the following metrics:

   • Accuracy
   • Precision
   • Recall
   • F1-Score

5. Compared the performance of the two Decision Tree models (one using Gini and the other using Entropy criteria) based on evaluation metrics.

MODELS USED

• Decision Tree Classifier (Gini criterion): A decision tree built using Gini impurity as the split criterion.
• Decision Tree Classifier (Entropy criterion): A decision tree built using information gain as the split criterion.

LIBRARIES NEEDED

• Numpy: For numerical computations.
• Pandas: For data manipulation and preprocessing.
• Matplotlib: For data visualization.
• Seaborn: For advanced data visualization.
• Scikit-Learn: For building and evaluating the machine learning models.

INSIGHTS

By this project, you will gain insights into:

• How to preprocess data, handle missing values, and encode categorical variables.
• Building and tuning Decision Tree models using different criteria (Gini and Entropy).
• Evaluating classification models with accuracy, precision, recall, and F1-score.
• Visualizing the results using data visualization libraries.