Mobile Price Classification

Building a mobile price classification model can provide valuable insights for businesses. By understanding the relationship between mobile phone features and price ranges, companies can make data-driven decisions about product pricing and positioning in the competitive market. This notebook serves as a guide to implementing such a classification system

Setup

To run this project, follow these steps:

1. Create Environment: Create a conda environment using the provided environment.yml file:

   conda env create --file environment.yml --name env
   

2. Activate Environment: Activate the newly created environment:

   conda activate env
   

3. Start Jupyter Notebook: Launch Jupyter Notebook to access and interact with the project files:

   jupyter notebook
   

Project Overview

This Jupyter notebook contains the code for the bank statement description classification project. Here's an overview of the notebook's contents:

• Data Preprocessing: The dataset is preprocessed, including steps like tokenization, lowercasing, and removing stopwords.

• Model Training: Various machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, and Gradient Boosting are trained and evaluated for classification.

• Evaluation: The performance of trained models is evaluated using metrics such as accuracy, precision, recall, and F1-score on the test dataset. Confusion matrices and classification reports are also generated.

• Deployment: Once a satisfactory model is trained and evaluated, it can be deployed in production environments for real-time classification of bank statement descriptions.