From 442be7ec1e2bfa584e7c3560eadda8c80283cbb3 Mon Sep 17 00:00:00 2001 From: Bobby Date: Mon, 11 Apr 2022 22:49:10 -0400 Subject: eda and readme upd --- README.md | 49 ++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 44 insertions(+), 5 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index 02d4f18..a2d82e4 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Different ML Techniques on the Iris dataset. For more information, see [this](ht ### About the data -Iris dataset is a multivariate data set with 150 samples and 4 features. The data set is named after the iris plant. The data set is loaded from [this](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data) link. It comes pre-installed with the Python package `sklearn`. +Iris dataset is a multivariate data set with 150 samples and 4 features. The data set is named after the iris plant. The data set is loaded from [this](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data) link. It comes pre-installed with the Python package `sklearn`. We have obtained the data set from [this](https://www.kaggle.com/uciml/iris) Kaggle link. ### Features @@ -15,9 +15,48 @@ The data set contains the following features: | Feature | Description | | --- | --- | -| sepal length in cm | the length of the sepals | -| sepal width in cm | the width of the sepals | -| petal length in cm | the length of the petals | -| petal width in cm | the width of the petals | +| Id | The id of the sample | +| SepalLengthCm | the length of the sepals | +| SepalWidthCm | the width of the sepals | +| PetalLengthCm | the length of the petals | +| PetalWidthCm | the width of the petals | +| Species | the species of the iris plant | + + +## ML Techniques + +Tentative ML techniques: +- [K-Nearest Neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) +- [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) +- [Decision Tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) +- [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) +- [Naive Bayes](https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) +- [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) +- [K-Means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) +- [Linear Discriminant Analysis](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html) +- [Quadratic Discriminant Analysis](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html) +- [Gaussian Process](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html) +- [Gradient Boosting](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) +- [Bagging](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) +- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) +- [Extra Trees](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) +- [Voting Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html) +- [Stacking Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html) +- [Bagging Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) +- [Extra Trees Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) +- [Gradient Boosting Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) +- [Gaussian Process Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html) +- [Random Forest Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) +- [Voting](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html) +- [Stacking](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html) +- [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) +- [Perceptron](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html) +- [Passive-Aggressive](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html) +- [Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html) +- [SGD](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) +- [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) +- [Linear SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) +- [NuSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html) +- [One-Class SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html) -- cgit v1.2.3