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ewis:laboratoare:11 [2021/05/05 12:44] alexandru.predescu [Requirements] |
ewis:laboratoare:11 [2023/04/26 19:18] (current) alexandru.predescu [Resources] |
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| ===== Lab 11. Project / Case Study ===== | ===== Lab 11. Project / Case Study ===== | ||
| + | This week is for working on the laboratory project and preparing the final presentation. | ||
| ==== Requirements ==== | ==== Requirements ==== | ||
| Select an algorithm and a dataset to make a presentation / case study of 10-25 slides. | Select an algorithm and a dataset to make a presentation / case study of 10-25 slides. | ||
| The combination of an algorithm and a dataset has to be unique for each student. | The combination of an algorithm and a dataset has to be unique for each student. | ||
| + | |||
| + | **The list of algorithms can be found on MS Teams / Laboratory channel** | ||
| <note important> | <note important> | ||
| An algorithm can be selected by at most 3 students. | An algorithm can be selected by at most 3 students. | ||
| - | If you choose an algorithm that was used in the Laboratory, you will have to use an additional dataset and compare the results. | + | * If you choose an algorithm that was used in the Laboratory, you will have to use another dataset. |
| + | * If you choose another algorithm, you can also use a dataset from the Laboratory. | ||
| </note> | </note> | ||
| Line 15: | Line 19: | ||
| === Introduction === | === Introduction === | ||
| - | Describe the context of your case study. Introduce the algorithm and possible applications | + | Describe the context of your case study. Introduce the algorithm and possible applications in real world scenarios. |
| === Methodology === | === Methodology === | ||
| - | Describe the algorithm in more detail (text, pseudocode, flowcharts) and the way it can be used on a real data set. | + | Describe the algorithm in more detail (text, pseudocode, flowcharts), with use cases and methods for working with real data sets. |
| === Case Study === | === Case Study === | ||
| Line 29: | Line 33: | ||
| Describe the outcome of the case study and applications in real world scenarios in broader terms. | Describe the outcome of the case study and applications in real world scenarios in broader terms. | ||
| + | |||
| + | === Resources === | ||
| + | |||
| + | Present the resources used for the case study: algorithm, data sets and other references | ||
| ==== Resources ==== | ==== Resources ==== | ||
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| Useful libraries for data science in Python include: | Useful libraries for data science in Python include: | ||
| - | == [Machine Learning] == | + | **Machine Learning** |
| * [[https://scikit-learn.org/stable/|Scikit-learn]] | * [[https://scikit-learn.org/stable/|Scikit-learn]] | ||
| + | |||
| + | **Deep Learning** | ||
| * [[https://www.tensorflow.org/learn|TensorFlow]] | * [[https://www.tensorflow.org/learn|TensorFlow]] | ||
| * [[https://keras.io/|Keras]] | * [[https://keras.io/|Keras]] | ||
| - | == [Data Processing] == | + | **Data Processing** |
| * [[https://pandas.pydata.org/|Pandas]] | * [[https://pandas.pydata.org/|Pandas]] | ||
| * [[https://numpy.org/|NumPy]] | * [[https://numpy.org/|NumPy]] | ||
| * [[https://www.scipy.org/|SciPy]] | * [[https://www.scipy.org/|SciPy]] | ||
| - | == [Visualizations] == | + | **Visualizations** |
| * [[https://matplotlib.org/|matplotlib]] | * [[https://matplotlib.org/|matplotlib]] | ||
| * [[https://seaborn.pydata.org/|Seaborn]] | * [[https://seaborn.pydata.org/|Seaborn]] | ||
| Line 56: | Line 66: | ||
| * [[https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/|Analytics Vidhya]] | * [[https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/|Analytics Vidhya]] | ||
| + | * [[https://machinelearningmastery.com/clustering-algorithms-with-python/|Machine Learning Mastery]] | ||
| === Datasets === | === Datasets === | ||
| Line 61: | Line 72: | ||
| You can use any open dataset. Some common sources include: | You can use any open dataset. Some common sources include: | ||
| - | == [Dataset aggregators for Machine Learning projects] == | + | **Dataset aggregators for Machine Learning projects** |
| * [[https://www.kaggle.com/datasets|Kaggle]] | * [[https://www.kaggle.com/datasets|Kaggle]] | ||
| * [[https://data-flair.training/blogs/machine-learning-datasets/|Data Flair]] | * [[https://data-flair.training/blogs/machine-learning-datasets/|Data Flair]] | ||
| * [[https://www.openml.org/search?type=data|OpenML]] | * [[https://www.openml.org/search?type=data|OpenML]] | ||
| + | * [[https://archive.ics.uci.edu/ml/datasets.php|UCI Machine Learning Repository]] | ||
| - | == [Open Data] == | + | **Open Datasets** |
| * [[https://data.europa.eu/data/datasets?locale=data&minScoring=0|data.europa.eu]] | * [[https://data.europa.eu/data/datasets?locale=data&minScoring=0|data.europa.eu]] | ||
| * [[https://data.worldbank.org/|World Bank Open Data]] | * [[https://data.worldbank.org/|World Bank Open Data]] | ||
