diff --git a/3217-classification-lr-example1.py b/3217-classification-lr-example1.py
deleted file mode 100644
index 2e52436c3f817deed0347943c3859e055ddb9370..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example1.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#Taken from Scikit
-
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LogisticRegression
-from sklearn import datasets
-from sklearn.inspection import DecisionBoundaryDisplay
-
-# import some data from a predefined datatset
-iris = datasets.load_iris()
-X = iris.data[:, :2] # we only take the first two features.
-Y = iris.target
-#print shape of the array for X and Y. Also get value of targets
-print (X.shape)
-print (Y)
-print (Y.shape)
-
-
-# Create an instance of Logistic Regression Classifier and fit the data.
-logreg = LogisticRegression(C=1)
-logreg.fit(X, Y)
-
-_, ax = plt.subplots(figsize=(4, 3))
-DecisionBoundaryDisplay.from_estimator(
- logreg,
- X,
- cmap=plt.cm.Paired,
- ax=ax,
- response_method="auto",
- plot_method="pcolormesh",
- shading="auto",
- xlabel="Sepal length",
- ylabel="Sepal width",
- eps=0.5,
-)
-
-# Plot the training points
-plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
-
-
-plt.xticks(())
-plt.yticks(())
-
-plt.show()
diff --git a/3217-classification-lr-example2.py b/3217-classification-lr-example2.py
deleted file mode 100644
index 8896f7e5c4680a3dfe44e9a69d2b1f8e6dd66659..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example2.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from sklearn import datasets, neighbors, linear_model
-from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score
-import matplotlib.pyplot as plt
-
-X_digits, y_digits = datasets.load_digits(return_X_y=True)
-X_digits = X_digits / X_digits.max()
-
-n_samples = len(X_digits)
-
-ratio = 0.9
-print (n_samples)
-#train data
-X_train = X_digits[: int(ratio * n_samples)]
-y_train = y_digits[: int(ratio * n_samples)]
-print (X_train.shape)
-
-
-#test data
-
-X_test = X_digits[int(ratio * n_samples) :]
-y_test = y_digits[int(ratio * n_samples) :]
-print (X_test.shape)
-
-logistic = linear_model.LogisticRegression(max_iter=1000)
-
-print(
- "LogisticRegression score: %f"
- % logistic.fit(X_train, y_train).score(X_test, y_test))
-
-#Get results on actual test labels and predicted labels
-
-predictions = logistic.predict(X_test)
-
-#print (predictions)
-#print (y_test)
-#get f1 score
-
-print (f1_score(y_test, predictions, average='macro'))
-
-#get confusion matrix
-cm = confusion_matrix(y_test, predictions, labels=logistic.classes_)
-disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logistic.classes_)
-disp.plot()
-plt.show()
diff --git a/3217-classification-lr-example3.py b/3217-classification-lr-example3.py
deleted file mode 100644
index c7b464de4a26735759d96a65b1f46f64485770ae..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example3.py
+++ /dev/null
@@ -1,75 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-
-from sklearn import datasets
-from sklearn.decomposition import PCA
-from sklearn.linear_model import LogisticRegression
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import GridSearchCV
-from sklearn.preprocessing import StandardScaler
-
-# Define a pipeline to search for the best combination of PCA truncation
-# and classifier regularization.
-pca = PCA()
-# Define a Standard Scaler to normalize inputs
-scaler = StandardScaler()
-
-# set the tolerance to a large value to make the example faster
-logistic = LogisticRegression(max_iter=10000, tol=0.1)
-pipe = Pipeline(steps=[("scaler", scaler), ("pca", pca), ("logistic", logistic)])
-
-X_digits, y_digits = datasets.load_digits(return_X_y=True)
-
-
-
-
-
-print (X_digits.shape)
-print (y_digits.shape)
-
-# Parameters of pipelines can be set using '__' separated parameter names:
-param_grid = {
- "pca__n_components": [5, 15, 30, 45, 60],
- "logistic__C": np.logspace(-1, 1, 1),
-}
-search = GridSearchCV(pipe, param_grid, n_jobs=2,cv=5)
-search.fit(X_digits, y_digits)
-print("Best parameter (CV score=%0.3f):" % search.best_score_)
-print(search.best_params_)
-
-# Plot the PCA spectrum
-pca.fit(X_digits)
-
-fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
-ax0.plot(
- np.arange(1, pca.n_components_ + 1), pca.explained_variance_ratio_, "+", linewidth=2
-)
-ax0.set_ylabel("PCA explained variance ratio")
-
-ax0.axvline(
- search.best_estimator_.named_steps["pca"].n_components,
- linestyle=":",
- label="n_components chosen",
-)
-ax0.legend(prop=dict(size=12))
-
-# For each number of components, find the best classifier results
-results = pd.DataFrame(search.cv_results_)
-print (results)
-components_col = "param_pca__n_components"
-best_clfs = results.groupby(components_col).apply(
- lambda g: g.nlargest(1, "mean_test_score")
-)
-
-best_clfs.plot(
- x=components_col, y="mean_test_score", yerr="std_test_score", legend=False, ax=ax1
-)
-ax1.set_ylabel("Classification accuracy (val)")
-ax1.set_xlabel("n_components")
-
-plt.xlim(-1, 70)
-
-plt.tight_layout()
-plt.show()
diff --git a/3217-classification-lr-example4.py b/3217-classification-lr-example4.py
deleted file mode 100644
index 0ea5ae675780c2dae877adc3dca768c029b69bb9..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example4.py
+++ /dev/null
@@ -1,59 +0,0 @@
-
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn import datasets, linear_model
-from sklearn.metrics import mean_squared_error, r2_score
-
-# Load the diabetes dataset
-diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
-
-print (diabetes_X.shape)
-# Use only one feature
-feature_to_use = 2
-diabetes_X = diabetes_X[:, np.newaxis, feature_to_use]
-print (diabetes_X.shape)
-
-test_samples = 20
-
-# Split the data into training/testing sets
-diabetes_X_train = diabetes_X[:-test_samples]
-diabetes_X_test = diabetes_X[-test_samples:]
-
-# Split the targets into training/testing sets
-diabetes_y_train = diabetes_y[:-test_samples]
-diabetes_y_test = diabetes_y[-test_samples:]
-
-
-
-
-# Create linear regression object
-regr = linear_model.LinearRegression()
-
-
-# Train the model using the training sets
-regr.fit(diabetes_X_train, diabetes_y_train)
-
-# Make prediction using the testing set
-diabetes_y_pred = regr.predict(diabetes_X_test)
-
-print (diabetes_y_train.shape)
-
-print (diabetes_y_test.shape)
-
-# The coefficients
-print("Coefficients: \n", regr.coef_)
-# The mean squared error
-print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
-# The coefficient of determination: 1 is perfect prediction
-print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
-
-# Plot outputs
-plt.scatter(diabetes_X_test, diabetes_y_test, color="black") #grond truth actual test labels
-plt.scatter(diabetes_X_test, diabetes_y_pred, color="red") #predicted test labels
-
-plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)#predicted test labels
-
-plt.xticks(())
-plt.yticks(())
-
-plt.show()
diff --git a/3217-classification-lr-example5.py b/3217-classification-lr-example5.py
deleted file mode 100644
index fa7506699351e8ba3e9036de4caf39cb72018eb9..0000000000000000000000000000000000000000
--- a/3217-classification-lr-example5.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#Import scikit-learn dataset library
-from sklearn import datasets
-from sklearn.model_selection import train_test_split
-from sklearn import svm, metrics
-
-
-
-#Load dataset
-cancer = datasets.load_breast_cancer()
-
-# print the names of the features
-print("Features: ", cancer.feature_names)
-
-# print the label type of cancer('malignant' 'benign')
-print("Labels: ", cancer.target_names)
-
-# print data(feature)shape
-print (cancer.data.shape)
-
-
-# Split dataset into training set and test set
-X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2) # 70% training and 30% test
-
-
-#Create a svm Classifier
-clf = svm.SVC(kernel='linear') # Linear Kernel
-
-#Train the model using the training sets
-clf.fit(X_train, y_train)
-
-#Predict the response for test dataset
-y_pred = clf.predict(X_test)
-
-print("Accuracy:",metrics.accuracy_score(y_test, y_pred))