#SPYDER 3 PRO 4.0.2 DOWNLOAD INSTALL#
#SPYDER 3 PRO 4.0.2 DOWNLOAD DOWNLOAD#

This will give us an independent final check on the accuracy of the best model. Now we want to get an idea of the accuracy of the model on our test set. The SVM algorithm was the most accurate model that we tested. You can see that the box and whisker plots are squashed at the top of the range, with many samples achieving 100% accuracy. There is a population of accuracy measures for each algorithm because each algorithm was evaluated 10 times (10 fold cross validation). Let us create a plot of the model evaluation results and compare the spread and the mean accuracy of each model. It is clear from the output that the accuracy of Support Vector Model (SVM) model is the largest. Kfold = model_selection.KFold(n_splits=10, random_state=seed)Д¬v_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) Models.append((вЂCARTвЂ™, DecisionTreeClassifier())) Models.append((вЂKNNвЂ™, KNeighborsClassifier())) Models.append((вЂLDAвЂ™, LinearDiscriminantAnalysis())) Models.append((вЂLRвЂ™, LogisticRegression())) It ensures the results are directly comparable. We reset the random number seed before each run to ensure that the evaluation of each algorithm is performed using exactly the same data splits. This is a good mixture of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithms. Classification and Regression Trees (CART).X_trainВ and Y_trainВ are for preparing models while X_validationВ and Y_validationВ sets will be used later to validate the models.ДЄccuracy is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage.ДЇrom the plots we get an idea that some of the classes are partially linearly separable. X_train, X_validation, Y_train, Y_validation = model_ain_test_split(X, Y, test_size=validation_size, random_state=seed) We will later use the test dataset to check the accuracy of the best model.

We will split the loaded dataset into two, 85% of which we will use to train our models and 15% that we will hold back as a validation(test) dataset. Now, let us look at the interaction between the variables (multivariate plot) with the help of scatter plot. Two of the input variables have a normal distributionпїј.

We can create box plot anf histogram for all the four input variables which gives us better idea about distribution of input variables.Дot(kind=вЂ™boxвЂ™, subplots=True, layout=(2,2), sharex=False, sharey=False) Data Visualization :ДЇirst let us look at theВ plots of each individual variable (univariate plot). This will give us the idea about the data. Sepal_length sepal_width petal_length petal_width class # class distribution (number of rows that belong to each class) Names = Дataset = pandas.read_csv(iris_url, names=names) Dataset Summary :

#SPYDER 3 PRO 4.0.2 DOWNLOAD DOWNLOAD#

Download the iris dataВ file into your working directory and load it using the same method, changing URL to the local file name. This will help later when we explore the data. Note that we are specifying the names of each column when loading the data.

We will also use pandas next to explore the data both with descriptive statistics and data visualization. We can load the data directly from the UCI Machine Learning repository. Python: 2.7.13 |Anaconda 4.4.0 (x86_64)| (default, Dec 20 2016, 23:05:08)ДЇrom import scatter_matrixДЇrom trics import classification_reportДЇrom trics import confusion_matrixДЇrom trics import accuracy_scoreДЇrom sklearn.linear_model import LogisticRegressionДЇrom ee import DecisionTreeClassifierДЇrom sklearn.neighbors import KNeighborsClassifierДЇrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisДЇrom sklearn.naive_bayes import GaussianNBДЇrom sklearn.svm import SVC Load Dataset : Print(вЂscipy: вЂ™.format(sklearn._version_)) Now, open spyder and check the version of python and different library.

#SPYDER 3 PRO 4.0.2 DOWNLOAD INSTALL#

So need to install the required packages separately. I mean it has the most useful packages for Mathematics, Science and Engineering already installed for you. The reason for using anaconda is beacause Anaconda gives you just everything. Use this install anaconda (follow the steps depending upon the os you have) LetвЂ™s get started with your hello world machine learning project in Python.

To quantify the morphologic variation of Iris flowers of three related species.

Collected by Edgar Anderson and GaspeМЃ Peninsula.

Also called FisherвЂ™s Iris data set or AndersonвЂ™s Iris data set.

Spyder 3 pro 4.0.2 download