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.