GitHub - Ninad077/Machine_Learning-Logistic_Regression: Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.ipynb_checkpoints		.ipynb_checkpoints
LogicReg_AccuracyScore.jpg		LogicReg_AccuracyScore.jpg
LogicReg_ConfusionMatrix.jpg		LogicReg_ConfusionMatrix.jpg
LogicReg_OddsRatio.jpg		LogicReg_OddsRatio.jpg
LogicReg_ProbMatrix.jpg		LogicReg_ProbMatrix.jpg
LogicReg_Steps.jpg		LogicReg_Steps.jpg
LogicReg_graph.jpg		LogicReg_graph.jpg
Logistic_Regression.ipynb		Logistic_Regression.ipynb
Sigmoid_curve.png		Sigmoid_curve.png
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
adult_new.csv		adult_new.csv

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Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.