diff --git a/Diabetes-prediction b/Diabetes-prediction
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+# -*- coding: utf-8 -*-
+
+!apt-get install openjdk-8-jdk-headless -qq > /dev/null
+!pip install pyspark==2.4.4
+
+"""# Environment Path"""
+
+import os
+os.environ['JAVA_HOME'] = '/usr/lib/jvm/java-8-openjdk-amd64'
+
+"""# Run a SparkSession"""
+
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder.appName("spark").getOrCreate()
+
+"""# Clone Diabetes Dataset"""
+
+! git clone https://github.com/education454/diabetes_dataset
+
+! ls diabetes_dataset
+
+df = spark.read.csv('/content/diabetes_dataset/diabetes.csv',header=True,inferSchema=True)
+
+df.show()
+
+df.printSchema()
+
+df.groupby('Outcome').count().show()
+
+df.describe().show()
+
+"""# Cleaning Data"""
+
+for col in df.columns:
+  print(col+":",df[df[col].isNull()].count())
+
+def count_zeros():
+  columns_list =['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
+  for i in columns_list:
+    print(i+":",df[df[i]==0].count())
+
+count_zeros()
+
+from pyspark.sql.functions import *
+for i in df.columns[1:6]:
+  data = df.agg({i:'mean'}).first()[0]
+  print("Mean value for {} is {}".format(i,int(data)))
+  df = df.withColumn(i,when(df[i]==0,int(data)).otherwise(df[i]))
+
+df.show()
+
+"""# Correlation"""
+
+for col in df.columns:
+  print("correlation to outcome for {} is {}".format(col,df.stat.corr('Outcome',col)))
+
+"""# Feature Selection"""
+
+from pyspark.ml.feature import VectorAssembler
+assembler = VectorAssembler(inputCols=['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'],outputCol='features')
+output_data = assembler.transform(df)
+
+output_data.printSchema()
+
+output_data.show()
+
+"""# Build & Train Model"""
+
+from pyspark.ml.classification import LogisticRegression
+final_data = output_data.select('features','Outcome')
+
+final_data.printSchema()
+
+train , test = final_data.randomSplit([0.7,0.3])
+models = LogisticRegression(labelCol='Outcome')
+model = models.fit(train)
+
+summary = model.summary
+
+summary.predictions.describe().show()
+
+"""# Evaluation & Test Model"""
+
+from pyspark.ml.evaluation import BinaryClassificationEvaluator
+predictions = model.evaluate(test)
+
+predictions.predictions.show(20)
+
+evaluator = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction', labelCol='Outcome')
+evaluator.evaluate(model.transform(test))
+
+model.save("model")
+
+from pyspark.ml.classification import LogisticRegressionModel
+model = LogisticRegressionModel.load('model')
+