diff --git a/Logic 1.0 b/Logic 1.0
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-#Logic for the ideation stage
-
-1. Start
-
-
-2. Imputation process for missing values
-.
-
-# Imputation by mean method
-
-
-3. Predicting future values
-
-4. Quality process for imputation
-5. End
-
diff --git a/The Predictors Logic 1.0 b/The Predictors Logic 1.0
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+#Logic for the ideation stage
+
+1. Start
+#we have used summary function to determine the number of Na's in the data given
+summary(input_data)
+
+#we also tried to fit normal distribution but it was not following its some basic properties such as the proportion of concentration of data corresponding to Z values.
+#data is not plotted as belled shape
+
+# we also tried to use the data as time series object but the main feature of multivariate time series data i.e  the variable to be co-integrated is I(1) does not fulfilled.
+
+2. Imputation process for missing values
+
+#we have used mean imputation, but it was not following the trend and adhering to dataset.
+#mean imputation
+(data_i$M4[which(is.na(data_i$M4))]=mean(data_i$M4,na.rm=TRUE))
+
+# imputing the missing values 
+# mice imputation using pmm method 'Predictive mean matching' 
+# we used pmm method as data doesn't follow normal distribution so this method
+#gives us an egde over regression method 
+
+my_imp<-mice(input_data[,17:21],m=5,method="pmm",maxit = 200)
+my_imp$imp$M4
+summary(input_data$M4)
+input_data[,17:21]=complete(my_imp,1)
+
+
+3. Predicting future values
+
+# prediction for the fifth week 
+#we have assumed that data of monday in week 5 will be dependent on previous weeks data.
+M5<-lm(input_data$M4~input_data$M1+input_data$M2+input_data$M3,data = input_data)
+# similarly we have computed the values for rest 4 days
+
+4. Quality process for imputation
+#we have observed linear trend within the week
+#we check the quality of imputation used
+
+5. End
+