Prediction-with-Multiple-linear-Regression

Implementation of multiple linear regression model.

Description

• Multiple linear regression algorithm is used for regression problems.
• Dataset is having continous data and multiple independent variable, so multiple linear regression algorithm used for building model.
• 50 start ups dataset used for model building.

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 profit of start up based on R&D spend, administration, marketing spend and state.

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, RMSE.

6. save model:

   • joblib library used to dump model.
   • model is saved in .ipynb formate as 50_startups_multiple_regression_model.

EDA:

• No any null values in dataset.
• profit having strong correlation with R&D Spend - 97% and then marketing spend- 74%.
• No outliers in dataset.
• california state profit shows high increase trend wrt to R&D spend.
• california state profit shows high increase trend with marketing spend.
• highest profitable state is new york - 1.93M.
• R&D spend in new york - 35.1% of total spend.
• highest marketing spend is in florida state.

Multiple-linear-regression-model:

• multiple number of model build using different independent variable.
• state categorial data converted into numeric using get dummies.
• model with r2_score - 90% is found highest accuracy, so it is load and saved using joblib.