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