added more lectures

res/Data_LogReg.csv

+,Income,Purchase(Y/N)
+0,59221.0,0
+1,61823.0,1
+2,60545.0,1
+3,57752.0,0
+4,53783.0,0
+5,62283.0,1
+6,62818.0,1
+7,56215.0,0
+8,64098.0,1
+9,58925.0,0
+10,66578.0,1
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+12,59160.0,0
+13,63094.0,1
+14,60278.0,1
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res/stock_market.csv

+,Year,Month,Interest_Rate,Unemployment_Rate,Stock_Index_Price
+0,2017,12,2.75,5.3,1464
+1,2017,11,2.5,5.3,1394
+2,2017,10,2.5,5.3,1357
+3,2017,9,2.5,5.3,1293
+4,2017,8,2.5,5.4,1256
+5,2017,7,2.5,5.6,1254
+6,2017,6,2.5,5.5,1234
+7,2017,5,2.25,5.5,1195
+8,2017,4,2.25,5.5,1159
+9,2017,3,2.25,5.6,1167
+10,2017,2,2.0,5.7,1130
+11,2017,1,2.0,5.9,1075
+12,2016,12,2.0,6.0,1047
+13,2016,11,1.75,5.9,965
+14,2016,10,1.75,5.8,943
+15,2016,9,1.75,6.1,958
+16,2016,8,1.75,6.2,971
+17,2016,7,1.75,6.1,949
+18,2016,6,1.75,6.1,884
+19,2016,5,1.75,6.1,866
+20,2016,4,1.75,5.9,876
+21,2016,3,1.75,6.2,822
+22,2016,2,1.75,6.2,704
+23,2016,1,1.75,6.1,719

res/stock_market.json

+{
+  "Year": [
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2017,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016,
+    2016
+  ],
+  "Month": [
+    12,
+    11,
+    10,
+    9,
+    8,
+    7,
+    6,
+    5,
+    4,
+    3,
+    2,
+    1,
+    12,
+    11,
+    10,
+    9,
+    8,
+    7,
+    6,
+    5,
+    4,
+    3,
+    2,
+    1
+  ],
+  "Interest_Rate": [
+    2.75,
+    2.5,
+    2.5,
+    2.5,
+    2.5,
+    2.5,
+    2.5,
+    2.25,
+    2.25,
+    2.25,
+    2,
+    2,
+    2,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75,
+    1.75
+  ],
+  "Unemployment_Rate": [
+    5.3,
+    5.3,
+    5.3,
+    5.3,
+    5.4,
+    5.6,
+    5.5,
+    5.5,
+    5.5,
+    5.6,
+    5.7,
+    5.9,
+    6,
+    5.9,
+    5.8,
+    6.1,
+    6.2,
+    6.1,
+    6.1,
+    6.1,
+    5.9,
+    6.2,
+    6.2,
+    6.1
+  ],
+  "Stock_Index_Price": [
+    1464,
+    1394,
+    1357,
+    1293,
+    1256,
+    1254,
+    1234,
+    1195,
+    1159,
+    1167,
+    1130,
+    1075,
+    1047,
+    965,
+    943,
+    958,
+    971,
+    949,
+    884,
+    866,
+    876,
+    822,
+    704,
+    719
+  ]
+}

src/log_reg-data/main.py

+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve
+
+
+class LogReg:
+    def __init__(self, file_path: str):
+        # Einlesen der CSV Datei, Index ist schon gegeben in der 0ten Spalte, deswegen index_col
+        self.df = pd.read_csv(file_path, index_col=0)
+
+        # Variablen für Autovervollständigung
+        self.X = self.df["Income"]
+        self.y = self.df["Purchase(Y/N)"]
+
+    def plot_initial(self):
+        # Plotte die initialen Daten
+        plt.scatter(self.X, self.y, label="initial data")
+
+        # cosmetics
+        plt.xlabel("Income")
+        plt.ylabel("Purchase Y/N")
+        plt.title("Income vs Purchase")
+        plt.legend()
+
+        # Zeige alle Plots
+        plt.show()
+
+    def plot_logistic_regression(self):
+        # Erstellen eines neuen Standardscalers
+        scaler = StandardScaler()
+
+        # Die X-Werte dem Standardscaler hinzufügen
+        # Werte liegen in [.., ..] vor, müssen aber in [[..], [..]] vorliegen, deshalb reshape
+        X_scaled = scaler.fit_transform(self.X.to_numpy().reshape(-1, 1))
+
+        # Unterteilen der Daten in Trainings und Testdaten
+        X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(X_scaled, self.y, test_size=0.2,
+                                                                          random_state=0)
+        X_train = scaler.inverse_transform(X_train_scaled)
+        X_test = scaler.inverse_transform(X_test_scaled)
+
+        # Erstellen eines neuen Regressions Objekt
+        regressor = LogisticRegression()
+
+        # Die Trainingsdaten dem Regressions Objekt hinzufügen
+        regressor.fit(X_train_scaled, y_train)
+
+        # Plotten der inititalen Daten
+        plt.scatter(self.X, self.y, label="Initial data")
+
+        # Plotte die Ergebnisse des Trainingssets
+        plt.scatter(X_train, y_train, label="Training data")
+
+        # Plotte die Trainingsdaten nochmal, aber sortiert
+        X_train = np.sort(X_train)
+        X_train_scaled = np.sort(X_train_scaled)
+        y_pred_proba = regressor.predict_proba(X_train_scaled)[:, 1]
+        plt.scatter(X_train, y_pred_proba, label="Logistic Regression")
+
+        # Plotte die Testdaten
+        plt.scatter(X_test, y_test, label="Test data")
+
+        # cosmetics
+        plt.title("Logistic Regression")
+        plt.xlabel('Income')
+        plt.ylabel('Purchase(Y/N)')
+        plt.legend()
+
+        # Zeige alle Plots
+        plt.show()
+
+    def plot_confusion_matrix(self):
+        # Erstellen eines neuen Standardscalers
+        scaler = StandardScaler()
+
+        # Die X-Werte dem Standardscaler hinzufügen
+        # Werte liegen in [.., ..] vor, müssen aber in [[..], [..]] vorliegen, deshalb reshape
+        X_scaled = scaler.fit_transform(self.X.to_numpy().reshape(-1, 1))
+
+        # Unterteilen der Daten in Trainings und Testdaten
+        X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(X_scaled, self.y, test_size=0.2,
+                                                                          random_state=0)
+
+        # Erstellen eines neuen Regressions Objekt und hinzufügen der Daten
+        regressor = LogisticRegression().fit(X_train_scaled, y_train)
+
+        # Vorhersagen von y-Werten
+        y_prediction = regressor.predict(X_test_scaled)
+        threshhold = 0.8
+
+        # y_pred = (regressor.predict_proba(X_test_scaled)[:, 1] >=
+        #           threshhold).astype(bool)
+
+        # Erstellen der Confusion Matrix
+        conf_matrix = confusion_matrix(y_test, y_prediction)
+
+        # Schreibe confusion matrix auf die Konsole
+        print(f"Confusion Matrix:\n{conf_matrix}")
+
+        # Errechne die Werte und schreibe sie auf die Konsole
+        print(f"Sensitivität: {conf_matrix[1, 1] / conf_matrix[1, 1] + conf_matrix[1, 0]}")
+        print(f"Spezifizität; {conf_matrix[0, 0] / conf_matrix[0, 0] + conf_matrix[0, 1]}")
+        print(f"Genauigkeit; {(conf_matrix[1, 1] + conf_matrix[0, 0]) / (conf_matrix[0, 0] + conf_matrix[0, 1] + conf_matrix[1, 0] + conf_matrix[1, 1])}")
+        print(f"Genauigkeit Regressor: {regressor.score(X_test_scaled, y_test)}")
+        print(f"Präzision: {conf_matrix[1, 1] / (conf_matrix[1, 1] + conf_matrix[0, 1])}")
+
+    def plot_roc_curve(self):
+        # Erstellen eines neuen Standardscalers
+        scaler = StandardScaler()
+
+        # Die X-Werte dem Standardscaler hinzufügen
+        # Werte liegen in [.., ..] vor, müssen aber in [[..], [..]] vorliegen, deshalb reshape
+        X_scaled = scaler.fit_transform(self.X.to_numpy().reshape(-1, 1))
+
+        # Unterteilen der Daten in Trainings und Testdaten
+        X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(X_scaled, self.y, test_size=0.2,
+                                                                          random_state=0)
+
+        # Erstellen eines neuen Regressions Objekt und hinzufügen der Daten
+        regressor = LogisticRegression().fit(X_train_scaled, y_train)
+
+        # Errechnen der roc curve
+        logit_roc_auc = roc_auc_score(y_test, regressor.predict(X_test_scaled))
+        fpr, tpr, threshholds = roc_curve(y_test, regressor.predict_proba(X_test_scaled)[:, -1])
+
+        # Plotten der ROC curve
+        plt.plot(fpr, tpr, label=f"Logistic Regression area={logit_roc_auc}")
+
+        # cosmetics
+        plt.plot([0, 1], [0, 1], 'r--')
+        plt.xlim([-0.05, 1.0])
+        plt.ylim([-0.05, 1.05])
+        plt.xlabel('False Positive Rate')
+        plt.ylabel('True Positive Rate')
+        plt.title('Receiver operating characteristic')
+        plt.legend()
+
+        plt.show()
+
+
+if __name__ == '__main__':
+    data = LogReg('../../res/Data_LogReg.csv')
+
+    # data.plot_initial()
+    # data.plot_logistic_regression()
+    # data.plot_confusion_matrix()
+    data.plot_roc_curve()
\ No newline at end of file

src/stock_market/main.py

+import pandas as pd
+from sklearn import linear_model
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+class StockMarket:
+    def __init__(self, file_path: str):
+
+        # Das Dictionary wurde in eine csv Datei umgewandelt
+        # Einlesen der CSV Datei, und Umwandeln der Year und Month Spalte zu einer time Spalte
+        self.df = pd.read_csv(file_path, parse_dates={'time': [1, 2]})
+
+        # Variablen für Autovervollständigung
+        self.time = self.df['time']
+        self.interest_rate = self.df['Interest_Rate']
+        self.unemployment_rate = self.df['Unemployment_Rate']
+        self.stock_index_price = self.df['Stock_Index_Price']
+
+    def look_at_the_data(self):
+
+        # Schreiben des Datensatzes in die Konsole
+        # ProTip: .to_string() verhindert, dass der Datensatz gekürzt dargestellt wird
+        print(self.df.to_string())
+
+    def plot_initial(self):
+
+        # Erstellen einer neuen Figur und alle Plots darin zu Plotten
+        # ist nötig, da wir mehrere Plots in einem Fenster plotten wollen
+        fig, ax = plt.subplots(2, 1)
+
+        # Fixen des Abstandes zwischen den Zeilen an Plots
+        plt.subplots_adjust(hspace=.5)
+
+        # Plotte die initialen Daten, Unemployment in der ersten Zeile, Interest Rate in der zweiten
+        ax[0].scatter(self.unemployment_rate, self.stock_index_price, label="initial data")
+        ax[1].scatter(self.interest_rate, self.stock_index_price, label="initial data")
+
+        # Cosmetics
+        ax[0].set_title("Unemployment Rate vs Stock Index Price", y=1)
+        ax[1].set_title("Interest Rate vs Stock Index Price", y=1)
+        ax[0].set_xlabel("Unemployment Rate")
+        ax[0].set_ylabel("Stock Index Price")
+        ax[1].set_xlabel("Interest Rate")
+        ax[1].set_ylabel("Stock Index Price")
+        ax[0].grid(True)
+        ax[1].grid(True)
+        ax[0].legend()
+        ax[1].legend()
+
+        # Zeige alle Plots
+        plt.show()
+
+    def linear_regression(self):
+        reg = linear_model.LinearRegression()
+        reg.fit(self.df[["Interest_Rate", "Unemployment_Rate"]], self.stock_index_price)
+
+        print(f"Intercept: {reg.intercept_}")
+        print(f"Coefficients: {reg.coef_}")
+        print(f"Prediction: Interest_Rate=2.1, Unemployment_Rate=6.0, Stock_Index_Price = {reg.predict([[2.1, 6.0]])}")
+
+    def plot_3d(self):
+
+        # Erstellen einer 3d Achse
+        ax = plt.figure().gca(projection='3d')
+
+        # Plotten der Werte
+        ax.scatter(self.unemployment_rate, self.interest_rate, self.stock_index_price)
+
+        # cosmetics
+        ax.set_title("Unemployment Rate vs Interest Rate vs Stock Index Price")
+        ax.set_xlabel("Unemployment Rate")
+        ax.set_ylabel("Interest Rate")
+        ax.set_zlabel("Stock Index Price")
+
+        # Zeige alle Werte
+        plt.show()
+
+
+
+
+if __name__ == '__main__':
+    data = StockMarket('../../res/stock_market.csv')
+
+    # data.look_at_the_data()
+    # data.plot_initial()
+    # data.linear_regression()
+    data.plot_3d()
\ No newline at end of file