Install required dependencies for matplotlib GUI frontend and all pip other packages for this project ```bash sudo apt install python3-tk python3.9 -m pip install -r requirements.txt ``` Given a set of tuple `(X,Y)` data points as `[(X, Y), .., (X, Y)]`, determine the best fitting line plot, and then apply this projection to predict the dependent `Y` value using an independent `GIVEN_X` value. ```bash python3.9 linear-regression.py -h usage: linear-regression.py [-h] [--silent] [--file [FILE_PATH]] [GIVEN_X] [X,Y ...] Find most fitting line plot for given data points and predict value given some X positional arguments: GIVEN_X Value for X for prediction using linear regression (default: '4.5') X,Y A list of data points separated by spaces as: x,y x,y x,y ... (default: '[(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)]') optional arguments: -h, --help show this help message and exit --silent When this flag is set, line plot visualization will not be shown (default: 'False') --file [FILE_PATH], -f [FILE_PATH] Optionally provide file for data to be read from. Each point must be on it's own line with format x,y ``` Running linear regression program ```bash python3.9 linear-regression.py --file ./input.txt --silent Finding fitting line plot for given data [(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)] points_avg: (5.117647058823529, 5.235294117647059) variance: (241.76470588235296, 193.05882352941177) sigma: (3.887196176892422, 3.4736402333270258) covariance: 0.8455882352941174 correlation: 0.0626235432924427 Our line Y = BX + A must pass through the point (5.117647058823529, 5.235294117647059) Y = (0.05596107055961069)X + 4.9489051094890515 For X = 4.5, Y is predicted to be 5.200729927007299 ``` By default, the following linear regression is calculated and displayed ```bash python3.9 linear-regression.py Finding fitting line plot for given data [(1, 3), (2, 7), (3, 5), (4, 9), (5, 11), (6, 12), (7, 15)] points_avg: (4.0, 8.857142857142858) variance: (28.0, 104.85714285714286) sigma: (2.160246899469287, 4.180453381654971) covariance: 8.666666666666666 correlation: 0.9596775116832306 Our line Y = BX + A must pass through the point (4.0, 8.857142857142858) Y = (1.8571428571428565)X + 1.4285714285714315 For X = 4.5, Y is predicted to be 9.785714285714285 ``` ![](screenshot.png)