diff --git a/py_scripts/engineer.py b/py_scripts/engineer.py
index 2570c46..9016ffb 100644
--- a/py_scripts/engineer.py
+++ b/py_scripts/engineer.py
@@ -1,7 +1,14 @@
 # Visualisations for Data
+import matplotlib.pyplot as plt
 import seaborn as sns
 import gunner
+from numpy import count_nonzero
+
+sns.set()
+plt.xticks(rotation=90)
 
 games_vis = gunner.game_sales_NA
 
-sns.relplot(data = games_vis, x = "Year", y = "NA_Sales")
+games_fig = sns.barplot(data = games_vis, x = games_vis["Year"], y = games_vis["NA_Sales"], estimator = count_nonzero)
+
+plt.show()
diff --git a/py_scripts/gunner.py b/py_scripts/gunner.py
index ac02f63..fb00f12 100644
--- a/py_scripts/gunner.py
+++ b/py_scripts/gunner.py
@@ -11,20 +11,34 @@ global games_dat
 global game_sales_NA
 # Sales Globally
 global game_sales_GLO
+# Crime Recorded in The US
+global crime_US
+# Crime Recorded in Canada
+global crime_CA
 
 # Loading Datasets
-game_sales2019_dat = pd.read_csv('datasets/videogames/vgsales-12-4-2019-short.csv')
+game_sales_dat = pd.read_csv('datasets/videogames/vgsales-12-4-2019-short.csv')
 games_dat = pd.read_csv('datasets/videogames/Games.xls')
 
+crime_CA = pd.read_excel('datasets/crime/clean_crime_canada_dataset.xlsx')
+crime_US = pd.read_csv('datasets/crime/report.csv')
+
 # Printing information regarding datasets
-print("Data Sets' Info:\n")
-game_sales2019_dat.info()
+print("Game Datasets' Info:\n")
+game_sales_dat.info()
 games_dat.info()
 
+print("Crime Datasets' Info:\n")
+crime_US.info()
+crime_CA.info()
+
 # Printing First n values (index start: 0)
-print("Game Sale Data:\n", game_sales2019_dat.head(10))
+print("Game Sale Data:\n", game_sales_dat.head(10))
 print("Game Scores:\n", games_dat.head(10))
 
+print("US Crime Data:\n", crime_US.head(10))
+print("CA Crime Data:\n", crime_CA.head(10))
+
 # Regarding the Games.xls dataset:
 # Coercing the non-numeric values will result in NaN
 # thus allowing easier removal through `.notnull()`
@@ -41,8 +55,15 @@ games_dat.info()
 NA_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales']
 GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales']
 
-game_sales_NA = game_sales2019_dat.drop(columns = NA_col_list, axis = 1)
-game_sales_GLO = game_sales2019_dat.drop(columns = GLO_col_list, axis = 1)
+game_sales_NA = game_sales_dat.drop(columns = NA_col_list, axis = 1)
+game_sales_GLO = game_sales_dat.drop(columns = GLO_col_list, axis = 1)
 
-print("Game Sales for NA:\n", game_sales_NA.head(10))
-print("Game Sales Globally:\n", game_sales_GLO.head(10))
+print(f"Game Sales for NA:\n{game_sales_NA.head(10)} \nWith minimum year being: {game_sales_NA['Year'].min()}")
+print(f"Game Sales Globally:\n{game_sales_GLO.head(10)}\nWith minimum year being: {game_sales_GLO['Year'].min()}")
+
+# Getting the range of years which both datasets share
+crime_year_min = max(crime_US['report_year'].min(), crime_CA['year'].min())
+crime_year_max = min(crime_US['report_year'].max(), crime_CA['year'].max())
+
+crime_CA = crime_CA[(crime_CA['year'] >= crime_year_min) & (crime_CA['year'] <= crime_year_max)]
+crime_US = crime_US[(crime_US['report_year'] >= crime_year_min) & (crime_US['report_year'] <= crime_year_max)]