Project where data sets of different drivers with different driving behavior were classified with linear regression and machine learning to train and test data.

1. D.R.K.N.MODI UNIVERSITY
Newai,Rajasthan.
INDUSTRIAL TRAINING REPORT ON
“LINEAR REGRESSION MODEL”.
Submitted By:
Satyam Desh Pandey
2016BTCS018
Department of Computer Science And Engineering
2019-2020

2. Contents:
• Linear Regression
• Project Pre-requisite
• System Requirements and Environment
• Methodology
• Functional Approach
• Conclusion

3. Linear Regression
Linear regression analysis is a powerful technique used for predicting the unknown value of a
variable (dependent variable) from the known values of other variables.
Linear regression performs the task to predict a dependent variable value (y) based on a given independent
variable (x).
So, this regression technique finds out a linear relationship between x (input) and y(output). Hence, the
name is Linear Regression.

4. Lithion Project
Challenge :Drivers rent battery typically for a day and then replace it with a charged battery
from the company. Company has a variable pricing model based on the driver’s driving history. As
the life of a battery depends on factors such as over speeding, distance driven per day, etc. So with
help of machine learning and regression model technique create a model where drivers can be
grouped together as clusters on their driving data.
Key issues: Drivers will be incentivized based on the cluster, so grouping has to be accurate.
Business Benefits: Increase in profits, up to 15–20% as drivers with poor history will be
charged more.

5. System Specifications
• Anaconda Integrated Python
• Libraries like sci-kit learn,numpy
• Use of matplot.lib
• Use of panda library for reading csv files and data sets

6. Methodology
• In an integrated python environment , the data set of the
drivers of the company which has details of the distance
driven by each of the driver and the over speeding km
respectively.
• The dataset was stored in an excel(csv format)
• With the help of panda library it is stored in the work space.
• Python coding is performed extensively

7. Functional Approach
Step1. Importing the packages and the data set is read

8. A dataframe is obtained which has given values of the different drivers.

9. Step 2. Now we print the dataframe using panda describe and info syntax
The following data frames are obtained

10. Step 3. Regression algortihms are imported
Arrays were obtained after the regression from the output

11. Step 4. Regression commands when clusters are taken as 2 and 4

12. Step 5. Output of the drivers was graphically displayed using matplot.library
The clusters or groups performed by regression gives us the idea of
similar types of driver over two different dataframes.

13. CONCLUSION
With the completion of the above project we can conclude that
regression analysis is a type of statistical evaluation that enables three things:
Description: Relationships among the dependent variables and the independent variables can
be statistically described by means of regression analysis.
Estimation: The values of the dependent variables can be estimated from the observed values
of the independent variables.
Prognostication: Risk factors that influence the outcome can be identified, and individual
prognoses can be determined.
Regression analysis employs a model that describes the relationships between the dependent
variables and the independent variables in a simplified mathematical form. There may be
biological reasons to expect a priori that a certain type of mathematical function will best
describe such a relationship, or simple assumptions have to be made that this is the case (e.g.,
that blood pressure rises linearly with age).