GitHub - manan/linear-regression: This is a python machine learning project that lets you load csv/txt files containing data and can predict outcomes of new inputs based on the loaded data. No libraries / frameworks (except NumPy for linear algebra and matplotlib for plotting) are used. Gradient Descent is used for deriving the hypothesis.

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This is a python machine learning project that lets you load csv/txt files containing data and can predict outcomes of new inputs based on the loaded data. No libraries / frameworks (except NumPy for linear algebra and matplotlib for plotting) are used. Gradient Descent is used for deriving the hypothesis.

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Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
Exceptions.py		Exceptions.py
LinearRegression.py		LinearRegression.py
README.txt		README.txt
Snippet_HowToUse.png		Snippet_HowToUse.png
Snippet_HowToUse2.png		Snippet_HowToUse2.png
ex1data1.txt		ex1data1.txt
ex1data1_plot.png		ex1data1_plot.png
ex1data2.txt		ex1data2.txt
testscript.py		testscript.py

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I have committed two code snippets and two datasets to the repository and also a picture of the plot returned from calling classifier.plot() in the first snippet.

Using the framework is quite easy and here are the basic steps:
1. Initialize the classifier: lr.LinearRegression()
2. Load some data like ex1data1.txt or ex1data2.txt: classifier.load_data('filepath')
3. You can plot the data if you only have one X column and one Y column like ex1data.txt but you cannot plot data with multiple x columns and one Y column like ex1data2.txt: classifier.plot()
4. You can predict outcomes in either case: classifier.predict(matrix-shaped-(1xn))

Look at ex1data1.txt and ex1data2.txt to understand how your data should look. The last column is assumed to the Y column (and all the previous ones are assumed to be X columns) unless you give two files with X and Y matrice/vectors. 


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