--- a
+++ b/model.py
@@ -0,0 +1,34 @@
+from sklearn.svm import SVC
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+import os
+from sklearn.metrics import log_loss, ConfusionMatrixDisplay, average_precision_score, accuracy_score
+from sklearn.metrics import roc_curve, precision_recall_curve, auc, f1_score, confusion_matrix
+import matplotlib.pyplot as plt
+import numpy as np
+import pickle
+#def read_data(path):
+#    data = pd.read_csv(path)
+    #data.set_index("ID_REF",inplace = True)
+    #labels = data.pop("Result")
+#    return data, labels
+dataset = pd.read_csv('GenesExp1.csv')
+dataset.set_index("ID_REF",inplace = True)
+X = dataset.iloc[:, :20]
+y = dataset.iloc[:, -1]
+path_model="F:/GeneModel/"
+#X,y=read_data("GenesExp1.csv")
+# name="Model"
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=1)
+model=RandomForestClassifier(criterion='gini', max_depth=6, min_samples_leaf=1, min_samples_split=2,
+                       n_estimators=100)
+model.fit(X_train, y_train)
+
+y_pred=model.predict(X_test)
+pickle.dump(model, open('model.pkl','wb'))
+
+model = pickle.load(open('model.pkl','rb'))
+print(model.predict([[11.27285579,13.11888698,13.04983865,7.160173909,11.84600012,11.38408063,12.46225539,10.35803641,10.43634604,10.31537082,8.195574032,11.00985731, 9.804574801,	7.811523898,9.271842845,8.808279933,8.473070081,8.818380484,9.115116886, 9.315489635
+]]))