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Python News Friday, June 29
- Top 20 Python Libraries for Data Science in 2018
- Email Spam Filtering: An Implementation with Python and Scikit-learn
- PCA using Python (scikit-learn) – Towards Data Science
- A beginner’s guide to training and deploying machine learning models using Python
- How to retrieve source code of Python functions
- Home Page
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- Python Release Python 3.7.0
- Survival Analysis to Explore Customer Churn in Python
- Pandas is a Python library that provides high-level data structures and a vast variety of tools for analysis.
- There have been a few new releases of the pandas library, including hundreds of new features,enhancements,bug fixes, and API changes.
- Theimprovements regard pandas abilities for grouping and sorting data, more suitable output fortheapplymethod, and the support in performing custom types operations.
- The continuous enhancements of the library with new graphics and features broughtthe support for multiple linked views as well as animation, and crosstalk integration.
- The library provides a versatile collection of graphs, styling possibilities, interaction abilities in the form of linking plots, adding widgets, and defining callbacks, and many more useful features.
@kdnuggets: Top 20 #Python Libraries for #DataScience in 2018 https://t.co/eS9XzhjqGP https://t.co/wrA7uIgc54
- Once the dictionary is ready, we can extract word count vector (our feature here) of 3000 dimensions for each email of training set.
- Each word count vector contains the frequency of 500 dictionary words in the training file.
- The below python code will generate a feature vector matrix whose rows denote 700 files of training set and columns denote 3000 words of dictionary.
- We extract word count vector for each mail in test-set and predict its class(ham or spam) with the trained NB classifier and SVM model.
- Apart from that, there can be a lot of experiments that can be done in order to find the effect of various parameterslike – – a) Amount of training data – – b) Dictionary size – – c) Variants of the ML techniques used (GaussianNB, BernoulliNB, SVC) – – d)…
@kdnuggets: Email Spam Filtering: An Implementation with #Python and Scikit-learn #KDN https://t.co/m98dGpicPU
- import pandas as pdurl = load dataset into Pandas DataFrame – df = pd.read_csv(url, names=[‘sepal length’,’sepal width’,’petal length’,’petal width’,’target’])Original Pandas df (features +target)Standardize theData – PCA is effected by scale so you need to scale the features in your data before applying PCA.
- from sklearn.decomposition import PCApca = = = pd.DataFrame(data = principalComponents – , columns = [‘principal component 1’, ‘principal component 2’])PCA and Keeping the Top 2 Principal ComponentsfinalDf = pd.concat([principalDf, df[[‘target’]]], axis = 1)Concatenating DataFrame along axis = 1.
- fig = plt.figure(figsize = (8,8)) – ax = fig.add_subplot(1,1,1) – ax.set_xlabel(‘Principal Component 1’, fontsize = 15) – ax.set_ylabel(‘Principal Component 2’, fontsize = 15) – ax.set_title(‘2 component PCA’, fontsize = 20)targets = [‘Iris-setosa’, ‘Iris-versicolor’, ‘Iris-virginica’] – colors = [‘r’, ‘g’, ‘b’] – for target, color in zip(targets,colors): – indicesToKeep = finalDf[‘target’]…
- # all parameters not specified are set to their defaults – # default solver is incredibly slow which is why it was changed to ‘lbfgs’ – logisticRegr = LogisticRegression(solver = ‘lbfgs’)Step 3: Training the model on the data, storing the information learned from the data – Model is learning the…
- Time it took to fit logistic regression after PCA with different fractions of VarianceRetainedImage Reconstruction from Compressed Representation – The earlier parts of the tutorial have demonstrated using PCA to compress high dimensional data to lower dimensional data.
@kdnuggets: PCA using Python (scikit-learn) https://t.co/UbszqFf0oC https://t.co/ditFaPeIgt
- A beginners guide to training and deploying machine learning models usingPython – When I was first introduced to machine learning, I had no idea what I was reading.
- Scikits-learn, the library we will use for machinelearningTraining amodel – Machine learning works by finding a relationship between a label and its features.
- The code for this model, and fake wine, is below: – Importing and exporting our Pythonmodel – The pickle library makes it easy to serialize the models into files that I create.
- I can import or export my Python model for use in other Python scripts with the code below: – Creating a simple webserver – Flask, the framework we will use to create a webserver.To deploy my model, I first have to create a server.
- Adding the model to myserver – With the pickle library, I am able to able to load our trained model into my web server.
@freeCodeCamp: A beginner’s guide to training and deploying machine learning models using Python, by Ivan Yung https://t.co/uaFzvusQHc
@opensourceway: How to retrieve source code of #Python functions: https://t.co/8iOkncBSpb https://t.co/nqxU4ng9Ud
@PythonWeekly: Pyod – A Python Toolkit for Outlier Detection (Anomaly Detection). https://t.co/30LAaXVqt4 #python https://t.co/dnTJLCmrEP
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@tdualdir: Python3.7きました https://t.co/VtPCbo6Yul
@kdnuggets: Survival Analysis to Explore Customer Churn in Python https://t.co/A3Tb9RCHsd https://t.co/IdUnLgqJu5