Datamining
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This document outlines a fruit classifier system that uses data mining techniques to predict fruit names based on their features. It describes the motivation, scope, dataset, features, algorithms, and technologies used. The system uses KNN, Naive Bayes, and decision tree algorithms to classify fruits, and it provides a comparison of their accuracies. It also discusses the preprocessing, cross validation strategy, and tools/techniques implemented, including Python libraries and algorithms like KNN, Naive Bayes, and decision trees.
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Datamining
- 2. AGENDA Introduction Motivation Scope Dataset description Features Technologies used Confusion Matrix
- 3. INTRODUCTION Problem Statement A system which builds training models using data mining techniques to predict the fruit name using its features.
- 4. MOTIVATION The product can be used in jam manufacturing industries or similar factories where fruit segregation is required to be done automatically and no human intervention should be needed.
- 5. SCOPE The system built assumes that the metrics used for prediction are available beforehand. The scope currently is to classify the fruits but not the sub types.
- 6. DATASET USED The fruits dataset was created by Dr. Jain Murray from University of Edinburgh. And then the professors at University of Michigan formatted the fruits data slightly.
- 7. FEATURES The system uses three classification algorithms: 1. KNN 2. Naïve Bayes 3. Decision trees The accuracy is also shown for each model. The option for prediction of fruit label is provided for an unknown dataset. The system also has an interactive GUI. LINK FOR SRS:
- 8. PRE PROCESSING Scaling: The features in the dataset are scaled so as to consider equal weight of all the attributes. Because the value of height and weight attribute is much higher than colour score. Attribute Selection: The attributes not required like the fruit subtype is removed. And redundancy of attribute is removed by considering only one.
- 9. CROSS VALIDATION STRATEGY We train the model using only 70% of the dataset and remaining is reserved for testing the validity of the model. The splitting is done by randomly selecting some sets to train the model. And remaining sets are used for predicting using the model built.
- 10. ALGORITHMS USED 1. K nearest neighbour : The distance of new data point to all other training data points is calculated and selects k nearest data points. Finally, it assigns the data point to which majority of the k data points belong.
- 11. 2. Naïve Bayes: Every pair of features is classified independent of each other. It is a probabilistic model based on Bayes’ theorem.
- 12. 2. Decision trees: Tries to solve problem using tree representation and selects best attributes for upper levels of the tree.
- 13. COMPARISON KNN Naïve Bayes Decision Tree Accuracy 0.9 0.65 0.8 Detail Memory based technique Considers all attribute independent Follows SOP representation
- 14. Tools/techniques used Tools: 1. Jupyter notebook 2. Python 3. Libraries used: Tkinter, matplotlib, pandas Techniques: 1. KNN-K nearest neighbor algorithm 2. Naïve Bayes algorithm 3. Decision tree
- 19. Demonstration
- 20. REFERENCES https://towardsdatascience.com/solving-a-simple-classification-problem-with- python https://stackoverflow.com/ https://matplotlib.org/contents.html https://youtu.be/JQpWKGcDm70
- 21. Thank You