You can talk about pre-processing data before sending it to scikit-learn model. Learn how to use PCA to reduce dimensionality and use support vector machine on the output.
- [Instructor] Sometimes, we need to preprocess the data…before we feed it into our model.…Some algorithms are sensitive to the range of feature values…Let's look at our data, we create,…we'll import pandas as pd,…and then we create a data frame,…df equals pd.DataFrame.…The values will be boston dot data,…and the columns are going to be the boston…the feature names.…
Then let's have a look at the maximal value,…df.max and the axis will be zero,…so we'll do it column-wise.…You see for example, that the NOX maximal value is 0.87…while the TX value has a maximal of 711.…This is a huge difference.…RandomForestRegressor handles these with ease…but let's see another algorithm, Support Vector Machine.…From sklearn.svm import SVR.…
Then clf is SVR and clf.fit X_train and y_train,…and clf.score X_test and y_test.…Ouch, let's see what happens…when we scale all the parameters to the same scale.…From sklearn import preprocessing.…Then Xs equal preprocessing.scale boston data.…
Let's see what the scaler did.…We're going to do the same trick with a data frame.…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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