Abalone Age Prediction using various ML classifiers

[vrn25]

Description

Given Multi-class Classification problem, predict the age of Abalone (a type of Shellfish) using various features provided. Target output is value of Rings (Age = Rings + 1.5).
The dataset can be found here
The given features are:

1. Sex
2. Length
3. Diameter
4. Height
5. Whole weight
6. Shucked weight
7. Viscera weight
8. Shell weight

Details

• Technical Specifications: python, pandas, numpy, scikit-learn
• Type of issue: multiple issues
• Time Limit: 2 days for each classifier implementation

Issue requirements / progress

Train the model on training data. Predict target values using test data. Use Accuracy and F1_score metrics to evaluate the performance. Note: Each pull request should be a solution using only one model.

• [x] Using Logistic Regression
• [x] Using Decision Tree Classifier
• [ ] Using Gaussian Naive Bayes
• [x] Using K-Nearest Neighbor Classifier
• [ ] Using Support Vector Machine Classifier
• [x] Using Random Forest Classifier
• [x] Using Multi-layer Perceptron (MLP) Classifier
• [ ] Using Quadratic Discriminant Analysis
• [ ] Using AdaBoost Classifier

Resources

• To help you get started, the starter code abalone_age_starter_code.ipynb is present in /machineLearning/abalone_age
• Jupyter notebook installation
• Python tutorial
• Numpy tutorial
• Pandas tutorial
• Scikit-learn Logistic Regression
• Scikit-learn Decision Tree Classifier
• Scikit-learn Gaussian Naive Bayes
• Scikit-learn K-Nearest Neighbours
• Scikit-learn Support Vector Machines
• Scikit-learn Random Forest Classifier
• Scikit-learn MLP Classifier
• Scikit-learn Quadratic Discriminant Analysis
• Scikit-learn AdaBoost Classifier

Directory Structure

Place your solution file in path as follows.

• For Logistic Regression: /machineLearning/abalone_age/lr/<your_solution_file>
• For Decision Tree Classifier: /machineLearning/abalone_age/dtc/<your_solution_file>
• For Gaussian Naive Bayes: /machineLearning/abalone_age/gnb/<your_solution_file>
• For K Neighbors Classifier: /machineLearning/abalone_age/knn/<your_solution_file>
• For Support Vector Classifier: /machineLearning/abalone_age/svc/<your_solution_file>
• For Using Random Forest Classifier: /machineLearning/abalone_age/rfc/<your_solution_file>
• For Using Multi-layer Perceptron (MLP) Classifier: /machineLearning/abalone_age/mlp/<your_solution_file>
• For Using Quadratic Discriminant Analysis: /machineLearning/abalone_age/qda/<your_solution_file>
• For Using AdaBoost Classifier: /machineLearning/abalone_age/adb/<your_solution_file>

Note

Please claim the issue first by commenting here before starting to work on it. For any further issues, feel free to contact me @vrn25 or @niranjanrao1509 any time. Requests for review are welcomed. Happy Learning!

[kampaitees]

I'm doing it

[vrn25]

@kampaitees each model requires a new pull request. Please choose and specify one of the models you would like to work on.

[kampaitees]

I had used decision tree classifier

[Shuddhatm22]

i want to take up this issue using K neighbours clasiification

[vrn25]

@Shuddhatm22 yes please start working on it.

[vk-mittal14]

I want to take up this issue using Multi-layer Perceptron (MLP) Classifier.

[vrn25]

@i-m-vivek Go ahead

[vk-mittal14]

Added MLP Classifier #232 please review it.

[shashikanthgk]

I am taking up random forest classifier

[vrn25]

@shashikanthgk, Go ahead

[ArquiteturaJoel]

I would like to take up this problem - Logistic Regression

[vrn25]

@ArquiteturaJoel, Go ahead

[SaurabhAgarwala]

Hacktoberfest 2019 ends. Closing the issue.