573-pankaj/check-whether-a-Orthopedic-patients-is-Normal-or-Abnormalnot

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573-pankaj/check-whether-a-Orthopedic-patients-is-Normal-or-Abnormalnot

573-pankaj

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KNN :K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems. However, it is mainly used for classification predictive problems in industry. The following two properties would define KNN well − Lazy learning algorithm − KNN is a lazy learning algorithm because it does not have a specialized training phase and uses all the data for training while classification. Non-parametric learning algorithm − KNN is also a non-parametric learning algorithm because it doesn’t assume anything about the underlying data

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#Analyses the features of Orthopedic patients and making a decision whether a patients is Normal or Abnormal. Algorithms Used : K-NN Testing Score : 83% K-NN Training Score : 75% Naive Bayes Testing Score : 76% Naive Bayes Training Score : 78% Precision = TP / (TP + FP) Precision : 0.886 Recall = TP /(TP + FN) Recall : 0.8703 F1_score = 2*(Precision*Recall)/(Precision+Recall) F1_score : 0.8785 Key Activities : 1. Normalization 2. EDA 3. Classification_report 4. StandardScaler 5. Feature importance

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