added ML algorithms

app.py

@@ -3,8 +3,12 @@ import numpy as np
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+from sklearn.tree import DecisionTreeClassifier
 
-def train_iris_model():
+def train_iris_model(algorithm):
     df = pd.read_csv("Iris.csv")
     df['Species'] = df['Species'].map({'Iris-setosa': 0, 'Iris-virginica': 1, 'Iris-versicolor': 2})
     del df["Id"]
@@ -12,7 +16,18 @@ def train_iris_model():
     y = df['Species']
     X_train, _, y_train, _ = train_test_split(X, y, test_size=0.3, random_state=42)
 
-    model = KNeighborsClassifier()
+    if algorithm == 'KNN':
+        model = KNeighborsClassifier()
+    elif algorithm == 'SVM':
+        model = SVC()
+    elif algorithm == "logistic regression":
+        model = LogisticRegression()
+    elif algorithm == 'Random Forest':
+        model = RandomForestClassifier()
+    elif algorithm == 'Ada boost classifier':
+        model = AdaBoostClassifier()
+    elif algorithm == 'Decision tree':
+        model = DecisionTreeClassifier()
     model.fit(X_train, y_train)
 
     return model
@@ -29,13 +44,20 @@ def predict_iris_species(model, input_data):
 def main():
     st.title("Iris Species Prediction App")
 
+    # Choose ML algorithm for training
+    algorithm = st.sidebar.selectbox("Select ML Algorithm", ['KNN', 'SVM', 'logistic regression', 'Random Forest', 'Ada boost classifier', 'Decision tree'])
+
+    # Train the model based on user's choice
+    trained_model = train_iris_model(algorithm)
+
+    st.image(r"C:\Users\ChinmayB.WINJITBIOS\Downloads\download.jpg")
+
     st.sidebar.header("User Input")
     sepal_length = st.sidebar.slider("Sepal Length", 0.0, 10.0, 5.0)
     sepal_width = st.sidebar.slider("Sepal Width", 0.0, 10.0, 5.0)
     petal_length = st.sidebar.slider("Petal Length", 0.0, 10.0, 5.0)
     petal_width = st.sidebar.slider("Petal Width", 0.0, 10.0, 5.0)
 
-    trained_model = train_iris_model()
     input_values = [sepal_length, sepal_width, petal_length, petal_width]
     prediction_result = predict_iris_species(trained_model, input_values)
 
@@ -60,4 +82,4 @@ def main():
         st.image('versicolor_image.jpg', caption='Iris-versicolor', use_column_width=True)
 
 if __name__ == "__main__":
-    main()
+    main()