add training script and get predictions

Dockerfile

@@ -11,5 +11,6 @@ WORKDIR ./app
 
 COPY ./get_dataset.py ./
 COPY ./get_stats.py ./
-
+COPY ./train.py ./
+COPY predict.py ./
 CMD bash

Jenkinsfile

@@ -49,5 +49,21 @@ pipeline {
                 archiveArtifacts artifacts: 'dataset.csv,df_train.csv,df_test.csv', onlyIfSuccessful: true
             }
         }
+        stage("Prediction") {
+            agent {
+                dockerfile {
+                    filename 'Dockerfile'
+                    reuseNode true
+                }
+            }
+
+            steps {
+                sh "chmod +x ./train.py"
+                sh "chmod +x ./predict.py"
+                sh "python ./train.py"
+                sh "python ./predict.py"
+                archiveArtifacts artifacts: 'model.keras,predictions.txt', onlyIfSuccessful: true
+            }
+        }
     }
 }

predict.py

@@ -0,0 +1,15 @@
+import pandas as pd
+from tensorflow import keras
+import numpy as np
+np.set_printoptions(threshold=np.inf)
+
+data = pd.read_csv("df_test.csv")
+X_test = data.drop("Performance Index", axis=1)
+y_test = data["Performance Index"]
+
+model = keras.models.load_model("model.keras")
+
+predictions = model.predict(X_test)
+
+with open("predictions.txt", "w") as f:
+    f.write(str(predictions))

train.py

@@ -0,0 +1,49 @@
+import pandas as pd
+from tensorflow import keras
+from tensorflow.keras import layers
+
+
+class RegressionModel:
+    def __init__(self, optimizer="adam", loss="mean_squared_error"):
+        self.model = keras.Sequential([
+            layers.Input(shape=(5,)),  # Input layer
+            layers.Dense(32, activation='relu'),  # Hidden layer with 32 neurons and ReLU activation
+            layers.Dense(1)  # Output layer with a single neuron (for regression)
+        ])
+        self.optimizer = optimizer
+        self.loss = loss
+        self.X_train = None
+        self.X_test = None
+        self.y_train = None
+        self.y_test = None
+
+    def load_data(self, train_path, test_path):
+        data_train = pd.read_csv(train_path)
+        data_test = pd.read_csv(test_path)
+        self.X_train = data_train.drop("Performance Index", axis=1)
+        self.y_train = data_train["Performance Index"]
+        self.X_test = data_test.drop("Performance Index", axis=1)
+        self.y_test = data_test["Performance Index"]
+
+    def train(self, epochs=30):
+
+        self.model.compile(optimizer=self.optimizer, loss=self.loss)
+        self.model.fit(self.X_train, self.y_train, epochs=epochs, batch_size=32, validation_data=(self.X_test, self.y_test))
+
+    def predict(self, data):
+        prediction = self.model.predict(data)
+        return prediction
+
+    def evaluate(self):
+        test_loss = self.model.evaluate(self.X_test, self.y_test)
+        print(f"Test Loss: {test_loss:.4f}")
+
+    def save_model(self):
+        self.model.save("model.keras")
+
+
+model = RegressionModel()
+model.load_data("df_train.csv", "df_test.csv")
+model.train()
+model.evaluate()
+model.save_model()