Mini project

1. EVALUATION OF COMPRESSIVE
STRENGTH OF COCNRETE BY USING
MACHINE LEARNING
19BQ1A0116
19BQ1A0122
19BQ1A0114
19BQ1A0115
19BQ1A0125

2. CONTENTS
• INTRODUCTION
• OBJECTIVES
• METHODOLOGY
• RESULTS AND DISCUSSION
• CONCLUSION

3. INTRODUCTION
Machine learning is an application of artificial intelligence that involves algorithms and data that
automatically analyse and make decision by itself without human intervention.
It is one of the emerging technologies that’s getting rapidly prevalent in many of our day-to-day
activities
Some real life applications:
TRAFFIC PREDICTION
SOCIAL MEDIA SERVICES
EMAIL SPAM FILTERING
PRODUCT RECOMMENDATION
ONLINE FRAUD DETECTION

4. NORMAL COMPUTER VS MACHINE LEARNING
“The difference between normal computer software and machine
learning is that a human developer hasn’t given codes that instructs
the system how to react to situation, instead it is being trained by a
large number of data.”

5. OBJECTIVES:
• To apply Machine learning to civil engineering application viz. EVALUATING
COMPRESSIVE STRENGTH OF CONCRETE
• To get hands-on knowledge about emerging technologies.
• To come up with an alternative solution to the conventional method of determining the
compressive strength of concrete
• To expand our knowledge base about Machine Learning

6. METHODOLOGY
• We used python language to carry out this project.
• Several inbuilt libraries like Numpy,Scipy,Pandas have been used.
• We got the required prerequisite knowledge about anaconda and jupyter notebook
• Then,a data set has been collected from KAGGLE website.

7. • Jupyter Notebook from anaconda platform was used for this project.
• We have gone through several algorithms by training each one with our dataset thereby getting insights
about the same.
• By evaluating each model on the basis of its accuracy score,we have selected an algorithm named
“ExtraTreeRegressor”.
• Finally,by training this model with our data set,we have evaluated the concrete compressive strength
and carried out its prediction.

8. RESULTS
(http://localhost:8888/notebooks/linear%20regression%20.ipynb) – Linear Regression
(http://localhost:8888/notebooks/MLminiproject.ipynb ) -Extra Tress Regression
Above links redirect to Jupyter Notebook presenting us with the code that gets executed in
background when model is deployed.

9. CONCLUSION
• We have successfully applied Machine Learning techniques.
• This study presented us with huge implications and inspired us to explore further.

10. THANK YOU