prediction de regression terminee

frontend/pages/prediction.py

@@ -1,56 +0,0 @@
-import streamlit as st
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import LogisticRegression
-from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-from sklearn import datasets
-from sklearn.impute import SimpleImputer  # Add this line
-import pandas as pd
-import numpy as np
-st.header("Prediction")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("my_form"):
-        header = st.columns([2,1,2])
-        header[0].subheader("Model")
-        header[1].subheader("Data Name")
-
-        row1 = st.columns([2,1,2])
-        model = row1[0].selectbox("", ["Random Forest Classifier", "Random Forest Regressor", "Logistic Regression", "Linear Regression"])
-        data_name = row1[1].selectbox("", data.columns)
-
-        st.form_submit_button('launch')
-            
-    if model == "Random Forest Classifier":
-        model = RandomForestClassifier()
-    elif model == "Random Forest Regressor":
-        model = RandomForestRegressor()
-    elif model == "Logistic Regression":
-        model = LogisticRegression()
-    elif model == "Linear Regression":
-        model = LinearRegression()
-    
-    x = data.drop(data_name, axis=1)
-    y = data[data_name]
-    
-    # Convert categorical data to numerical values
-    x = pd.get_dummies(x)
-    
-    # Handle missing values
-    imputer = SimpleImputer()
-    x = imputer.fit_transform(x)
-    x = pd.get_dummies(x)
-    
-    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
-    model.fit(x_train, y_train)
-    y_pred = model.predict(x_test)
-    if model == "Random Forest Classifier":
-        st.write("Accuracy: ", accuracy_score(y_test, y_pred))
-    elif model == "Random Forest Regressor" or model == "Logistic Regression" or model == "Linear Regression":
-        st.write("Mean Squared Error: ", np.mean((y_pred - y_test) ** 2))
-else:
-    st.error("file not loaded")

frontend/pages/prediction_classification.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd
+
+st.header("Prediction: Classification")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("classification_form"):
+        st.subheader("Random Forest Parameters")
+        
+        data_name = st.multiselect("Features", data.select_dtypes(include="object").columns, help="Sélectionnez les caractéristiques pour l'entraînement.")
+        target_name = st.selectbox("Target", data.columns, help="Sélectionnez la variable cible pour l'entraînement.")
+        
+        n_estimators = st.number_input("Number of estimators", step=1, min_value=1, value=100, help="Nombre d'arbres dans la forêt.")
+        max_depth = st.number_input("Max depth", step=1, min_value=1, value=10, help="Profondeur maximale des arbres.")
+        
+        submit_button = st.form_submit_button('Train and Predict')
+
+    if submit_button and data_name and target_name:
+        le = LabelEncoder()
+        X = data[data_name].apply(le.fit_transform)
+        y = le.fit_transform(data[target_name])
+
+        model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=111)
+        model.fit(X, y)
+
+        st.subheader("Enter values for prediction")
+
+        pred_values = [st.selectbox(f"Value for {feature}", options=data[feature].unique(), key=f"value_{feature}") for feature in data_name]
+        pred_values_encoded = [le.transform([val])[0] for val in pred_values]
+
+        prediction = model.predict([pred_values_encoded])
+
+        prediction_decoded = le.inverse_transform(prediction)
+
+        st.write("Prediction:", prediction_decoded[0])
+else:
+    st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from sklearn.linear_model import LinearRegression
+
+st.header("Prediction: Regression")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("regression_form"):
+        st.subheader("Linear Regression Parameters")
+        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
+        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
+        st.form_submit_button('Train and Predict')
+
+    if data_name and target_name:
+        X = data[data_name]
+        y = data[target_name]
+
+        model = LinearRegression()
+        model.fit(X, y)
+        
+        st.subheader("Enter values for prediction")
+        pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
+        prediction = model.predict([pred_values])
+
+        st.write("Prediction:", prediction[0])
+else:
+    st.error("File not loaded")