/Customer-Behavior-Analysis-and-Prediction

Create, train, and evaluate a Naive Bayes model for predicting customer behavior. Visualize key insights with plots like pie charts and confusion matrices.

Primary LanguageJupyter Notebook

Customer Behavior Prediction using Naive Bayes

Model Creation

  • Import necessary libraries, including scikit-learn for machine learning and data manipulation tools.
  • Load the customer behavior dataset into a Pandas DataFrame.
  • Preprocess the data by handling missing values, encoding categorical variables if needed, and splitting it into features (X) and target variable (y).
  • Import the Naive Bayes model from scikit-learn, such as MultinomialNB for discrete features or GaussianNB for continuous features.
  • Create an instance of the Naive Bayes model.

Training the Model

  • Split the dataset into training and testing sets using tools like train_test_split.
  • Train the Naive Bayes model on the training data using the fit method, providing the features (X_train) and the corresponding labels (y_train).

Testing the Model

  • Evaluate the model's performance on the testing set using metrics like accuracy and error rate.
  • Use scikit-learn functions like accuracy_score for comprehensive performance evaluation.

Prediction

  • Make predictions on new or unseen data using the trained Naive Bayes model. This is done with the predict method, providing the features of the new data.

Visualization on Customer Behavior Dataset

  • Visualize key insights from the dataset using plots and graphs:
    • Plot the distribution of the target variable (customer behavior) to understand the class distribution.
    • Create visualizations, such as pie charts, to represent categorical features.
    • Visualize the model's performance metrics using confusion matrices

Adapt the provided code and visualizations based on the specific characteristics of your customer behavior dataset and the requirements of your analysis.