Fixes and improvements from code review

README.md

@@ -18,7 +18,7 @@
 
 
 ### Overview 
-In this project, the objective is to predict whether the person has Diabetes or not based on various features suach as 
+In this project, the objective is to predict whether the person has Diabetes or not based on various features such as
 - Pregnancies
 - Insulin Level
 - Age

app.py

@@ -1,34 +1,34 @@
-# Importing essential libraries
-from flask import Flask, render_template, request
-import pickle
-import numpy as np
-
-# Load the Random Forest CLassifier model
-filename = 'diabetes-prediction-rfc-model.pkl'
-classifier = pickle.load(open(filename, 'rb'))
-
-app = Flask(__name__)
-
-@app.route('/')
-def home():
-	return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if request.method == 'POST':
-        preg = int(request.form['pregnancies'])
-        glucose = int(request.form['glucose'])
-        bp = int(request.form['bloodpressure'])
-        st = int(request.form['skinthickness'])
-        insulin = int(request.form['insulin'])
-        bmi = float(request.form['bmi'])
-        dpf = float(request.form['dpf'])
-        age = int(request.form['age'])
-        
-        data = np.array([[preg, glucose, bp, st, insulin, bmi, dpf, age]])
-        my_prediction = classifier.predict(data)
-        
-        return render_template('result.html', prediction=my_prediction)
-
-if __name__ == '__main__':
-	app.run(debug=True)
+# Importing essential libraries
+from flask import Flask, render_template, request
+import pickle
+import numpy as np
+
+# Load the Random Forest Classifier model
+filename = 'diabetes-prediction-rfc-model.pkl'
+classifier = pickle.load(open(filename, 'rb'))
+
+app = Flask(__name__)
+
+@app.route('/')
+def home():
+	return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if request.method == 'POST':
+        preg = int(request.form['pregnancies'])
+        glucose = int(request.form['glucose'])
+        bp = int(request.form['bloodpressure'])
+        st = int(request.form['skinthickness'])
+        insulin = int(request.form['insulin'])
+        bmi = float(request.form['bmi'])
+        dpf = float(request.form['dpf'])
+        age = int(request.form['age'])
+
+        data = np.array([[preg, glucose, bp, st, insulin, bmi, dpf, age]])
+        my_prediction = classifier.predict(data)
+
+        return render_template('result.html', prediction=my_prediction)
+
+if __name__ == '__main__':
+	app.run(debug=True)