diff --git a/src/features/google_activity_recognition.py b/src/features/google_activity_recognition.py
index 0e814e26..95becab3 100644
--- a/src/features/google_activity_recognition.py
+++ b/src/features/google_activity_recognition.py
@@ -4,37 +4,49 @@ import scipy.stats as stats
 
 #Read csv into a pandas dataframe
 data = pd.read_csv(snakemake.input[0])
+column = ['local_date_time','count','most_common_activity','number_unique_activities','activity_change_count']
+finalDataset = pd.DataFrame(columns=column)
+finalDataset.set_index('local_date_time',inplace=True)
 
+if data.empty:
+    finalDataset.to_csv(snakemake.output[0])
 
-#Resampling each of the required features as a pandas series
-data.local_date_time = pd.to_datetime(data.local_date_time)
-resampledData = data.set_index(data.local_date_time)
+else:
+    #Resampling each of the required features as a pandas series
+    data.local_date_time = pd.to_datetime(data.local_date_time)
+    resampledData = data.set_index(data.local_date_time)
+    resampledData = resampledData[~resampledData.index.duplicated()]
+    resampledData.rename_axis('time',axis='columns',inplace=True)
+    resampledData.drop(columns=['local_date_time'],inplace=True)
 
-resampledData = resampledData[~resampledData.index.duplicated()]
-resampledData.rename_axis('time',axis='columns',inplace=True)
+    #Finding count grouped by day
+    count = pd.DataFrame()
+    count = resampledData['activity_type'].resample('D').count()
+    count = count.rename(columns={"activity_type":"count"})
 
-resampledData.drop(columns=['local_date_time'],inplace=True)
+    #Finding most common activity of the day
+    mostCommonActivity = pd.DataFrame()
+    mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
+    mostCommonActivity = mostCommonActivity.rename(columns={'activity_type':'most_common_activity'})
 
-#Finding count grouped by day
-count = pd.DataFrame()
-count = resampledData['activity_type'].resample('D').count()
-count = count.rename(columns={"activity_type":"count"})
+    #finding different number of activities during a day
+    uniqueActivities = pd.DataFrame()
+    # countChanges = resampledData.to_period('D').groupby(resampledData.index)['activity_type'].value_counts()
+    uniqueActivities = resampledData['activity_type'].resample('D').nunique()
+    
+    #finding Number of times activity changed
+    resampledData['activity_type_shift'] = resampledData['activity_type'].shift()
+    resampledData['activity_type_shift'].fillna(resampledData['activity_type'].head(1),inplace=True)
+    #resampledData['different_activity'] = resampledData['activity_type'].apply(lambda x: 0 if resampledData['activity_type'] == resampledData['activity_type_shift'] else 1, axis=1)
+    resampledData['different_activity']=np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
+    countChanges = pd.DataFrame()
+    countChanges = resampledData['different_activity'].resample('D').sum()
 
-#Finding most common activity of the day
-mostCommonActivity = pd.DataFrame()
-mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
-mostCommonActivity = mostCommonActivity.rename(columns={'activity_type':'most_common_activity'})
+    #Concatenating all the processed data only, no other sensor data is added here for simplicity
+    finalDataset = pd.DataFrame()
+    finalDataset = pd.concat([count,mostCommonActivity,uniqueActivities,countChanges],axis=1)
+    finalDataset.rename(columns={0:"count",1:'most_common_activity','activity_type':'number_unique_activities','different_activity':'activity_change_count'},inplace = True)
 
-#finding different number of activities during a day
-countChanges = pd.DataFrame()
-# countChanges = resampledData.to_period('D').groupby(resampledData.index)['activity_type'].value_counts()
-countChanges = resampledData['activity_type'].resample('D').nunique()
-
-#Concatenating all the processed data only, no other sensor data is added here for simplicity
-finalDataset = pd.DataFrame()
-finalDataset = pd.concat([count,mostCommonActivity,countChanges],axis=1)
-finalDataset.rename(columns={0:"count",1:'most_common_activity','activity_type':'activity_changes_count'},inplace = True)
-
-#Export final dataframe with extracted features to respective PID
-finalDataset.to_csv(snakemake.output[0])
+    #Export final dataframe with extracted features to respective PID
+    finalDataset.to_csv(snakemake.output[0])