The document summarizes Aniket Anil Bhavsar's industrial training project on loan approval prediction using machine learning. The project involved training logistic regression, random forest, support vector machine, decision tree, and KNN models on loan application data to predict loan eligibility. Logistic regression achieved the highest accuracy of 83.76% while KNN performed the worst with 63.4% accuracy. Visualizations were created to compare the different models' performances. In conclusion, machine learning algorithms can be effectively used for predictive modeling and automation of real-time loan approval processes based on applicant details.

1. INDUSTRIAL TRAINING
PRESENTATION
Submitted by:
Aniket Anil Bhavsar
TE B 10
Submitted to:
Prof. Smita T. Patil

2. CONTENTS
▪ Training Details
▪ Certificate
▪ Introduction to Machine Learning
▪ Project Description
▪ Algorithms Used
▪ Visualization
▪ Conclusion
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3. TRAINING DETAILS
Start Date : 10 January 2022
End Date : 10 February 2022
Mode of Training : Online
Training Company : Cognifront Private Ltd.
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4. CERTIFICATE
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5. INTRODUCTION TO MACHINE LEARNING
▪ Machine learning is a branch of Artificial Intelligence (AI) and computer
science. It focuses on the use of data and algorithms to imitate the way that
humans learn.
▪ ML explores the study and construction of algorithms that can learn from data
and make predictions on data.
▪ ML allows software applications to become more accurate at predicting
outcomes without being explicitly programmed to do so. Algorithms use
historical data as input to predict new output values.
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6. TYPES OF MACHINE LEARNING
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7. SUPERVISED LEARNING
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Fig. Supervised Learning

8. UNSUPERVISED LEARNING
▪ In an unsupervised learning model, the algorithm learns on an unlabelled dataset
and tries to make sense by extracting features, co-occurrence, and underlying
patterns on its own.
▪ Unsupervised learning cannot be directly applied to a regression or classification
problem because unlike supervised learning.
▪ We have the input data but no corresponding output data.
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9. REINFORCEMENT LEARNING
▪ Reinforcement learning is an area of Machine Learning. It is about taking suitable
action to maximize reward in a particular situation.
▪ It is employed by various software and machines to find the best possible behavior
or path it should take in a specific situation.
▪ The input should be an initial state from which the model will start
▪ There are many possible outputs as there are a variety of solutions to a particular
problem
▪ In the absence of a training dataset, it is bound to learn from its experience.
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10. PROJECT DESCRIPTION
▪ LOAN APPROVAL PREDICTION USING MACHINE LEARNING:
▪ A bank deals in all kinds of loans. They have a presence across all urban, semi-urban and
rural areas. The customer first applies for a loan and after that, the bank validates the
customer eligibility for the loan.
▪ If the banks wants to automate the loan eligibility process (real-time) based on customer
detail provided while filling out online application forms. These details are Gender,
Marital Status, Education, number of Dependents, Income, Loan Amount, Credit
History, and others.
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11. STEPS OF PREDICTIVE MODELLING
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12. ALGORITHMS AND LIBRARIES USED
▪ ALGORITHMS USED IN PROJECT:
▪ Support Vector Machine (SVM)
▪ KNN (K Nearest Neighbor)
▪ Decision Tree
▪ Logistic Regression
▪ Random Forest
▪ LIBRARIES USED:
▪ NumPy
▪ Pandas
▪ Matplotlib
▪ Seaborn
▪ Train_Test_Split
▪ Accuracy Score
▪ SVM
▪ All the other libraries required for ML models.
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13. SVM (Support Vector Machine)
▪ It is a classification method. In this algorithm, we plot each data item as a
point in n-dimensional space (where n is number of features you have) with
the value of each feature being the value of a particular coordinate.
▪ Accuracy score: 68.29%
▪ Mean absolute error: 0.3170
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14. KNN (K – NEAREST NEIGHBOR)
▪ It can be used for both classification and regression problems. However, it is
more widely used in classification problems in the industry. K nearest
neighbors is a simple algorithm that stores all available cases and classifies
new cases by a majority vote of its k neighbors.
▪ Accuracy score: 63.41%
▪ Mean absolute error: 0.3658
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15. DECISION TREE
▪ It is a type of supervised learning algorithm that is mostly used for
classification problems. Surprisingly, it works for both categorical and
continuous dependent variables. In this algorithm, we split the population into
two or more homogeneous sets. This is done based on most significant
attributes/ independent variables to make as distinct groups as possible.
▪ Accuracy score: 65.85%
▪ Mean absolute error: 0.3414
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16. LOGISTIC REGRESSION
▪ It is a classification algorithm. It is used to estimate discrete values (Binary
values like 0/1, yes/no, true/false) based on given set of independent
variable(s). In simple words, it predicts the probability of occurrence of an
event by fitting data to a logistic function. Hence, it is also known as logistic
regression. Since, it predicts the probability, its output values lies between 0
and 1 (as expected).
▪ Accuracy Score: 83.76%
▪ Mean absolute error: 0.1623
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17. RANDOM FOREST
▪ Random Forest is a trademark term for an ensemble of decision trees. In
Random Forest, we’ve collection of decision trees (so known as “Forest”). To
classify a new object based on attributes, each tree gives a classification and
we say the tree “votes” for that class. The forest chooses the classification
having the most votes (over all the trees in the forest).
▪ Accuracy score: 75.60%
▪ Mean absolute error: 0.2439
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18. OUTPUT
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19. VISUALIZATION
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20. COMPARISON OF MODELS
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21. CONCLUSION
▪ After trying and testing 5 different algorithms, the best accuracy is achieved
by Logistic Regression (83.76%) followed by Random Forest(75%), followed
by Support vector machine (68.2%), followed by Decision Tree(65%) and
KNN performed the worst (63.4%). Hereby, I learn to imply the machine
learning algorithms for prediction by using previous data or dataset for
training the model and testing it on real-time data/dataset. To make it user-
friendly I have integrated UI by which user gain better interaction experience.
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22. THANK YOU !!
Name of Student: Aniket A. Bhavsar
Email: bhavsaraniket110@gmail.com
Department of Computer Engineering
K. K. Wagh Institute of Engineering Education & Research, Nashik
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