{"id":9575,"date":"2022-11-29T20:36:15","date_gmt":"2022-11-29T11:36:15","guid":{"rendered":"https:\/\/www.stechstar.com\/user\/wordpress\/?p=9575"},"modified":"2022-11-29T20:36:15","modified_gmt":"2022-11-29T11:36:15","slug":"python%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-scikit-learn-tutorial-machine-learning-in-python","status":"publish","type":"post","link":"https:\/\/www.stechstar.com\/user\/wordpress\/python%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-scikit-learn-tutorial-machine-learning-in-python\/","title":{"rendered":"[python][\uba38\uc2e0\ub7ec\ub2dd] Scikit-learn Tutorial: Machine Learning in Python"},"content":{"rendered":"

[python][\uba38\uc2e0\ub7ec\ub2dd] Scikit-learn Tutorial: Machine Learning in Python<\/h1>\n

Scikit-learn Tutorial: Machine Learning in Python<\/h1>\n
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Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy<\/code> and SciPy<\/code>.<\/p>\n

In this tutorial we will learn to code python and apply Machine Learning with the help of the scikit-learn library, which was created to make doing machine learning in Python easier and more robust.<\/p>\n

To do this, we\u2019ll be using the Sales_Win_Loss data<\/a> set from IBM\u2019s Watson repository. We will import the data set using pandas, explore the data using pandas methods like head()<\/code>, tail()<\/code>, dtypes()<\/code>, and then try our hand at using plotting techniques from Seaborn<\/code> to visualize our data.<\/p>\n

Then we\u2019ll dive into scikit-learn and use preprocessing.LabelEncoder()<\/code> in scikit-learn to process the data, and train_test_split()<\/code> to split the data set into test and train samples. We will also use a cheat sheet to help us decide which algorithms to use for the data set. Finally we will use three different algorithms (Naive-Bayes, LinearSVC, K-Neighbors Classifier) to make predictions and compare their performance using methods like accuracy_score()<\/code> provided by the scikit-learn library. We will also visualize the performance score of different models using scikit-learn and Yellowbrick visualization.<\/p>\n

To get the most out of this post, you should probably already be comfortable with:<\/p>\n