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Support Vector Classification\nPython script to predict Occupancy using SVM in [sklearn](https://scikit-learn.org/stable/modules/svm.html) library.\n\n**Note:**  \nThis script uses 60% Training and 40% testing of the data file.  \nRandom seed used in model_selection.train_test_split is *100.*  \nTested in python: 3.5, Pandas: 0.23.4, scikit-learn: 0.20.2  \nLicense: *GNU-GPL*\n\n**Installing Dependencies**\n```\npip3 install pandas scikit-learn\n```\n**Data-Insight**\n```\n$ head Data/430pm_Managed.txt\n201611071630 CP:C96GB CHAdeMO Occ\n201611071630 CP:C96GB FastAC43 Free\n201611071630 CP:C96GB ComboCCS Occ\n201611071630 CP:C9QKW CHAdeMO Occ\n201611071630 CP:C9QKW FastAC43 Occ\n201611071630 CP:C9QKW ComboCCS Occ\n201611071630 CP:RC13 CHAdeMO Free\n201611071630 CP:RC13 FastAC43 Free\n201611071630 CP:RC13 ComboCCS Occ\n201611071630 CP:C85WS CHAdeMO Free\n```\n**Usage**\n```\n\u003e\u003e\u003e python3 PredictOccupancy.py Data/430pm_Managed.txt\n\nAccuracy: 0.714516129032258\nReport:\n               precision    recall  f1-score   support\n\n    Occupied       0.51      0.19      0.28       178\n        Free       0.74      0.93      0.82       442\n\n   micro avg       0.71      0.71      0.71       620\n   macro avg       0.62      0.56      0.55       620\nweighted avg       0.67      0.71      0.67       620\n\nConfusionMatrix:\n [[ 34 144]\n [ 33 409]]\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvaasudevans%2Fsvc-support-vector-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvaasudevans%2Fsvc-support-vector-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvaasudevans%2Fsvc-support-vector-classification/lists"}