/I-phone-purchase-project--Prediction-with-KNN-Classification

Prediction of customer will purchase iPhone or not using KNN classifier model and multiple supervised ML model.

Primary LanguageJupyter Notebook

Prediction-with-KNN-Classification

Implementation of KNN algorithm in Python 3

Description

  • K-Nearest-Neighbors algorithm is used for classification and regression problems.
  • In this project, it is used for classification.
  • puchased Iphone dataset used for project.

Data set format

  • CSV (Comma Separated Values) format.
  • Attributes can be integer or real values.
  • Responses can be integer, real or categorical.

Overview

The primary goal is predict wheather customer will purchase Iphone or not from their store based on gender, age and salary.

liabrary

  • pandas, numpy, matplotlib,seaborn,sklearn,joblib used in project

Methodology

  1. Machine learning life cycle:

    • followed indistry standard practice of machine learning life cycle steps.

  2. Preprocessing and EDA:

    • implement necessary transformation, preprocessing of dataset.
    • conduct exploratory data analysis on dataset.

  3. Visualization:

    • visualised data using visualisation library like matplotlib, seaborn.

  4. Algorithm:

    • scikit library use for KNN algorithm.

  5. model validation:

    • model validate with accuracy score of diff K, confusion metrix.

  6. save model:

    • joblib library used to dump model.
    • model is saved in .ipynb formate as i_phone_purchase_product_using_KNN_model.

EDA:

  • Total female are 51% and male are 49%.
  • Female average salary is more than male average salary.
  • Total iphone purchased - 143 no's ( female purchased - 77 no's and male purchased - 66 no's)
  • Maximum iphone purchased between age group of 46 to 50 years.( female - 47 to 48 yrs, male - 46 to 50 yrs)
  • No correlation between salary with age and salary with number of iphone purchased.

KNN model:

  • model validated with k values 19, 21 and 15 which was calculated by standard method and error method.
  • accurancy score of k=15 is 0.875, so it is greater than other model.So it is considered and saved.