diff --git a/module3/exo3/exercice_en.Rmd b/module3/exo3/exercice_en.Rmd
index a85afaf78e17c3d04946c8adbfd6dd0f512b1a63..cfcb70ac94f01d626f77c187169bfcdc3291e325 100644
--- a/module3/exo3/exercice_en.Rmd
+++ b/module3/exo3/exercice_en.Rmd
@@ -1,24 +1,37 @@
 ---
-title: "CO2 Keeling"
+title: "CO2 Keeling Curve and Hisitorical Ice Core Data"
 author: "David Elser"
 date: "05-03-2021"
-output: html_document
+output: pdf_document
 ---
-## Data preprocessing fetch the Data file and check if file already exists
 
-The data is available from the  Web site of the Scripps Institute "https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html"
+#### Please note that I am not a Computer Scientist and my programming skills are very limited. 
+
+### Data preprocessing fetch the Data file and check if file already exists
+
+The data is available from the  Web site of the Scripps Institute
+
+"https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html"
+"https://scrippsco2.ucsd.edu/assets/data/atmospheric/merged_ice_core_mlo_spo/merged_ice_core_yearly.csv"
 
 Soure script available at : https://github.com/DemirhanOzelTrojan/Keeling_Predict/blob/master/main.R
+
+`
 install.packages('forecast', dependencies = TRUE)
 install.packages("imputeTS")
+install.packages("tidyverse")
+`
 
-```{r}
+### Load librarys
 
-install.packages("imputeTS")
+```{r}
+library(ggplot2)
 library(forecast)
 library(imputeTS)
+library(lubridate)
 ```
 
+### Fetch CO2 measurements from Mauna Loa observatory, Hawaii, USA
 
 ```{r}
 data_url = "https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv"
@@ -29,23 +42,151 @@ if (!file.exists(data_file)) {
 }
 ```
 
-Skip the frist 5 lines of the file
+### Fetch Ice Core CO2 Data
+
+```{r}
+
+
+data_url = "https://scrippsco2.ucsd.edu/assets/data/atmospheric/merged_ice_core_mlo_spo/merged_ice_core_yearly.csv"
+data_file1 = "Ice-CO2.csv"
+
+if (!file.exists(data_file1)) {
+    download.file(data_url, data_file1, method="auto")
+}
+```
+
+### Skip the frist 36 lines
+
+```{r}
+data_ice = read.csv(data_file1, header=FALSE, skip=36)
+```
+
+### Skip the frist 5 lines of the file
 
 ```{r}
 data = read.csv(data_file, header=TRUE, skip=5)
 ```
 
-Let's have a look at what we got:
+### Let's have a look at what we got and check NA
+
+```{r}
+head(data_ice)
+tail(data_ice)
+
+na_records = apply(data_ice, 1, function (x) any(is.na(x)))
+data[na_records,]
+
+
+
+```
+### Conversion of decimal-date into Years, rename V2 into ppm
+
+
+```{r}
+
+
+data_ice$date<- format(date_decimal(data_ice$V1), "%Y")
+head(data_ice)
+tail(data_ice)
+names(data_ice)[names(data_ice) == "V2"] <- "ppm"
+data_ice$Year=as.numeric(data_ice$date)
+head(data_ice)
+tail(data_ice)
+
+
+```
+
+
+
+### Let's have a look at what we got from Mauna Loa observatory and check NA
+
 ```{r}
 head(data)
 tail(data)
+
+na_records = apply(data, 1, function (x) any(is.na(x)))
+data[na_records,]
 ```
 
+### impute data of column X.ppm..4(CO2, seasonally filled,adjusted filled)
 
 ```{r}
-data$X.ppm.[data$X.ppm. == -99.99] <- NA
-data$X.ppm. <- na.interpolation(data$X.ppm.)
+data$X.ppm..4[data$X.ppm..4 == -99.99] <- NA
+data$X.ppm..4 <- na.interpolation(data$X.ppm..4)
 head(data)
 tail(data)
 ```
 
+
+### data preparation
+```{r}
+data.ts <- ts(data$X.ppm..4, start = c(1958, 1), end = c(2021, 1), freq = 12)
+
+```
+
+### split training and testing data, use 90% of available data in the co2.csv file
+```{r}
+train.test.split <- 0.9
+split.index <- round(length(data.ts) * train.test.split)
+```
+
+### train time series
+```{r}
+data.train.ts <- window(data.ts, start = c(1958, 1), end = c(1958, split.index))
+```
+
+### test time series
+```{r}
+data.test.ts <- window(data.ts, start = c(1958, split.index + 1), end = c(1958, length(data.ts)))
+```
+
+### create a fit using the  time series
+```{r}
+fit <- ets(data.ts, model = 'AAA')
+```
+
+### forecast using the fit
+```{r}
+predictions <- forecast(fit, 12 * 20, level = 0.95)
+```
+
+
+## Plot Ice Core Data
+
+```{r}
+
+theme_set(theme_minimal())
+
+ggplot(data = data_ice, aes(x = Year, y = ppm))+
+  geom_line(color = "#00AFBB", size = 1)
+
+
+```
+
+## Plot predictions till 2025
+
+```{r}
+
+plot(predictions,
+     main = '',
+     xlab = 'Time',
+     ylab = 'CO2 (ppm)',
+     xlim = c(1958, 2025),
+     shaded = TRUE,
+     shadecols=c('light blue'))
+legend('topleft',
+       legend=c('Historical Data',
+                'Predicted Data',
+                '95% CI'),
+       fill=c(NA, NA, 'light blue'),
+       col=c('black', 'blue', NA),
+       lty=c(1, 1, NA),
+       lwd=c(1, 2, NA),
+       border=c(NA, NA, 'black'),
+       inset=0.01)
+
+
+```
+
+
+
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