/Delivery-time-prediction---Simple-Linear-Regression

To predict delivery time based on sorting time using simple linear regression model.

Primary LanguageJupyter Notebook

Prediction-with-Simple-linear-Regression

Implementation of simple linear regression to predict delivery time.

Description

  • Dataset is fall under supervised.
  • In this project, simple linear regression model is used for prediction.

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 delivery time based on sorting time.

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 linear regression.

  5. model validation:

    • model validate with r2_score and root mean square error.

  6. save model:

    • joblib library used to dump model.
    • model is saved in .ipynb formate as Delivery_pr1.

EDA:

  • delivery time is float and sorting time is int data type.
  • No null values in dataset.
  • No outliers in dataset.
  • delivery time is having 82.5% correlation with sorting time.
  • maximum occurence of sorting time is 7.
  • max delivery time is 29, min delivery time is 8, average delivery time is 17
  • max sorting time is 10, min sorting time is 2, average sorting time is 6. max sorting time count is 7.

Simple Linear Regression model:

  • model intercept_ is 6.6 and coef_ is 1.60
  • model r2_score is 57% and RMSE is 2.33