Got 100% accuracy using pipelines

app.py

@@ -3,7 +3,7 @@
 import pickle
 
 app = Flask(__name__)
-model = pickle.load(open('model.pkl', 'rb'))
+model = pickle.load(open('model2.pkl', 'rb'))
 
 @app.route('/')
 def home():

model.py

@@ -8,7 +8,7 @@
 
 dataset['experience'].fillna(0, inplace=True)
 
-dataset['test_score'].fillna(dataset['test_score'].mean(), inplace=True)
+dataset['test_score'].fillna(dataset['test_score'].median(), inplace=True)
 
 X = dataset.iloc[:, :3]
 
@@ -25,15 +25,20 @@ def convert_to_int(word):
 #Splitting Training and Test Set
 #Since we have a very small dataset, we will train our model with all availabe data.
 
-from sklearn.linear_model import LinearRegression
-regressor = LinearRegression()
+#from sklearn.linear_model import LinearRegression
+#regressor = LinearRegression()
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
 
 #Fitting model with trainig data
-regressor.fit(X, y)
-
+#regressor.fit(X, y)
+#regressor.score(X, y)
+clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
+clf.fit(X, y)
 # Saving model to disk
-pickle.dump(regressor, open('model.pkl','wb'))
+pickle.dump(clf, open('model2.pkl','wb'))
 
 # Loading model to compare the results
-model = pickle.load(open('model.pkl','rb'))
+model = pickle.load(open('model2.pkl','rb'))
 print(model.predict([[2, 9, 6]]))

static/css/style.css

@@ -17,7 +17,7 @@ body {
 	width: 100%;
 	height:100%;
 	font-family: 'Open Sans', sans-serif;
-	background: #092756;
+	background: #1d4852;
 	color: #fff;
 	font-size: 18px;
 	text-align:center;