data-science-infinity · Kat-Bristol · Feb 2, 2026 · Feb 2, 2026 · Feb 2, 2026 · Feb 2, 2026
diff --git a/_config.yml b/_config.yml
@@ -8,22 +8,22 @@ plugins:
 
 #-------------------------------
 # General Site Settings
-title: Johnny Hopkins
-description: "Hi I'm Johnny, and I'm a Data Scientist.  My portfolio focuses on interesting projects I've recently undertaken, with a strong emphasis on business impact. Please visit my Github & LinkedIn pages (or download my Resume) by using the links below!"
+title: Aikaterini Tsaousi
+description: "Hi I'm Kat and I'm a Data Scientist.  My portfolio focuses on interesting projects I've recently undertaken. Please visit my Github & LinkedIn pages (or download my Resume) by using the links below!"
 baseurl: "" # the subpath of your site, e.g. /blog
 url: "" # the base hostname & protocol for your site, e.g. http://example.com
 
 #-------------------------------
 # About Section
 subtitle: Data Science <span style="color:#f58506"> Portfolio </span>
-location: "London, UK"
-resume_url: /docs/resume.docx
-avatar_image: /img/profile_picture.JPG
+location: "Bristol, UK"
+resume_url: /docs/Draft_CV_Jan_2026.pdf
+avatar_image: /img/SoY_Profile_Photo.jpg
 
 #-------------------------------
 # Contact links
-linkedln: "https://linkedln.com/#" # Add your linkedln handle
-github: "https://github.com/#"  # Add your github handle
+linkedln: "https://www.linkedin.com/in/aikaterini-tsaousi/" # Add your linkedln handle
+github: "https://github.com/Kat-Bristol"  # Add your github handle
 
 paginate: 6
 paginate_path: "/page/:num"
@@ -52,3 +52,12 @@ defaults:
 #   - vendor/gems/
 #   - vendor/ruby/
 
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diff --git a/_posts/2021-06-25-coffee-and-python.md b/_posts/2021-06-25-coffee-and-python.md
@@ -0,0 +1,77 @@
+
+
+coffee_and_python.txt
+Page
+1
+/
+1
+100%
+---
+layout: post
+title: Coffee & Python
+image: "/posts/coffee_python.jpg"
+tags: [Python, Coffee]
+---
+
+# My first project
+## is all about
+### how much
+#### I LOVE
+##### Python & Coffee!
+
+---
+
+Firstly, I love Python so much, here is some code!
+
+```
+my_love_for_python = 0
+my_python_knowledge = 0
+
+for day in lifetime:
+    my_love_for_python += 1
+    my_python_knowledge += 1
+```
+
+Just so you really see how much I love Python, here is some code BUT with some colours for keywords & functionality!
+
+```python
+my_love_for_python = 0
+my_python_knowledge = 0
+
+for day in lifetime:
+    my_love_for_python += 1
+    my_python_knowledge += 1  
+```
+
+Here is an **unordered list** showing some things I love about Python
+
+* For my work
+    * Data Analysis
+    * Data Visualisation
+    * Machine Learning
+* For fun
+    * Deep Learning
+    * Computer Vision
+    * Projects about coffee
+
+Here is an _ordered list_ showing some things I love about coffee
+
+1. The smell
+    1. Especially in the morning, but also at all times of the day!
+2. The taste
+3. The fact I can run the 100m in approx. 9 seconds after having 4 cups in quick succession
+
+I love Python & Coffee so much, here is that picture from the top of my project AGAIN, but this time, in the BODY of my project!
+
+![alt text](/img/posts/coffee_python.jpg "Coffee & Python - I love them!")
+
+The above image is just linked to the actual file in my Github, but I could also link to images online, using the URL!
+
+A line break, like this one below - helps me make sense of what I'm reading, especially when I've had so much coffee that my vision goes a little blurry
+
+---
+
+I could also add things to my project like links, tables, quotes, and HTML blocks - but I'm starting to get a cracking headache.  Must be coffee time.
+Displaying coffee_and_python.txt.
+
+---
diff --git a/...6-09-Finding-Prime-Numbers-With-Python.md → ...1-30-Finding-Prime-Numbers-With-Python.md b/...6-09-Finding-Prime-Numbers-With-Python.md → ...1-30-Finding-Prime-Numbers-With-Python.md
diff --git a/_posts/2026-02-01-Earthquakes-Map-Tableau.md b/_posts/2026-02-01-Earthquakes-Map-Tableau.md
@@ -0,0 +1,11 @@
+---
+layout: post
+title: Earthquake Tracking Dashboard Using Tableau
+image: "/posts/Map.png"
+tags: [Tableau, Data Viz]
+---
+
+This is a Tableau Dashboard that tracks global Earthquake activity across a 30-day period. You can use the slide bar to select the magnitude of earthquakes shown and/or the drop down menus to select the days and countries you want to include.
+
+<iframe seamless frameborder="0" src="https://public.tableau.com/views/DSIEarthquakeDashboard_17696419825620/Map?:embed=yes&:display_count=yes&:showVizHome=no" width = '1090' height = '900'></iframe>
+
diff --git a/_posts/2026-02-04-Guessing-Number-Game.md b/_posts/2026-02-04-Guessing-Number-Game.md
@@ -0,0 +1,58 @@
+---
+layout: post
+title: Python Guessing Number Game
+image: "/posts/games_guess_the_number.png"
+tags: [Python]
+---
+
+Here is the Python Script for the game
+
+---
+
+
+
+
+import random
+
+random.randint(1, 10)
+
+LOWER_BOUND = 0
+
+UPPER_BOUND = 100
+
+GUESS_LIMIT = 5
+
+GUESS_COUNTER = 0
+
+
+CORRECT_NUMBER = random.randint(LOWER_BOUND, UPPER_BOUND)
+
+print(f'Try guessing the number that I am thinking. It is between {LOWER_BOUND} and {UPPER_BOUND}. '
+      f'Good luck, you have {GUESS_LIMIT} guesses')
+
+while True:
+    try:
+        user_guess = int(input('What is your guess?? '))
+    except ValueError:
+        print('Please enter a valid number')
+        continue
+
+    if not (LOWER_BOUND <= user_guess <= UPPER_BOUND):
+        print(f'Your guess is out of range! Try a guess between {LOWER_BOUND} and {UPPER_BOUND}')
+        continue
+
+    GUESS_COUNTER += 1
+    remaining_guesses = GUESS_LIMIT - GUESS_COUNTER
+
+    if user_guess == CORRECT_NUMBER:
+        print(f'Wow, you got it in {GUESS_COUNTER} guesses - well done!')
+        break
+    elif user_guess < CORRECT_NUMBER:
+        print(f'Your guess is too low, try again! Guesses remaining: {remaining_guesses}')
+    else:
+        print(f'Your guess is too high, try again! Guesses remaining: {remaining_guesses}')
+
+    if remaining_guesses == 0:
+        print(f"Sorry, you're out of guesses. The number you were after was {CORRECT_NUMBER}")
+        break
+---
diff --git a/_posts/2026-02-07-AB-Testing.md b/_posts/2026-02-07-AB-Testing.md
@@ -0,0 +1,110 @@
+---
+layout: post
+title: Assessing Campaign Performance
+image: "/posts/Performance_Marketing_mod.jpg"
+tags: [A/B Testing, Chi-Square, Stats]
+---
+
+A/B tests are used in business to describe a hypothesis test. An A/B Test is essentially a randomised experiment containing two groups, A & B that receive different experiences. Within an A/B test, we look to understand and measure the response of each group. Marketing campaign performance analysis measures the effectiveness of marketing initiatives against defined goals. 
+
+
+---
+
+Example of a Marketing Campaign Pilot:
+
+ - Group 1 – customers received Mailer 1 (a basic cheaper version) to sign up to loyalty scheme
+ - Group 2 – customers received Mailer 2 (a colourful more expensive version) to sign up to loyalty scheme
+ - Group 3 = control group - received no Mailer at all (but can still sign up to loyalty scheme via main menu)
+
+The marketing team is certain that customers who received a mailer (i.e. Groups 1 or 2) signed up to the company's loyalty scheme more than the control group but are unsure whether the quality of mailer they received made a significant difference. 
+
+We can answer this question using hypothesis testing, in particular using the chi-square test for independence.
+
+---
+
+
+
+##### IMPORT REQUIRED PACKAGES
+```
+import pandas as pd
+from scipy.stats import chi2_contingency as cc
+from scipy.stats import chi2
+```
+
+##### IMPORT DATA (ensure the spreadsheet is located in the same directory as this Python Script File)
+```
+campaign_data = pd.read_excel('grocery_database.xlsx', sheet_name = 'campaign_data')
+```
+
+
+
+##### FILTER THE DATA (i.e take out all rows with CTL group using the .loc method)
+```
+campaign_data = campaign_data.loc[campaign_data['mailer_type'] != 'Control']
+```
+
+
+
+##### SUMMARISE TO GET OUR OBSERVED FREQUENCIES using .crosstab() method
+```
+observed_values = pd.crosstab(campaign_data['mailer_type'], campaign_data['signup_flag']).values
+observed_values = pd.crosstab(campaign_data["mailer_type"], campaign_data["signup_flag"])
+print(observed_values)
+
+mailer1_signup_rate = 123 / (252 +123)
+mailer2_signup_rate = 127 / (209 +127)
+print(mailer1_signup_rate, mailer2_signup_rate)
+```
+
+
+
+##### STATE HYPOTHESES & SET ACCEPTANCE CRITERIA
+```
+null_hypothesis = 'there is no relationship between mailer type and signup rate. They are independednt.'
+alternate_hypothesis = 'there is a relationship between mailer type and signup rate. They are NOT independednt.'
+acceptance_criteria = 0.05
+```
+
+
+
+##### CALCULATE EXPECTED FREQUENCIES & CHI SQUARE STATISTIC
+```
+chi2_statistic, p_value, dof, expected_values = cc(observed_values, correction = False)
+print(chi2_statistic, p_value)
+```
+
+
+##### FIND THE CRITICAL VALUE FOR THE TEST using the percentage point function
+```
+critical_value = chi2.ppf(1- acceptance_criteria, dof)
+print(critical_value)
+```
+
+
+##### PRINT THE RESULTS/CONCLUSION (Chi Square Statistic)
+```
+if chi2_statistic >= critical_value:
+    print(f'As our chi-suqre-statistic of {chi2_statistic} is HIGHER than our citical value of {critical_value}, we REJECT the null hypothesis and conclude that {alternate_hypothesis}')
+else:
+    print(f'As our chi-suqre-statistic of {chi2_statistic} is LOWER than our citical value of {critical_value}, we ACCEPT the null hypothesis and conclude that {null_hypothesis}')
+ ```   
+
+
+##### PRINT THE RESULTS/CONCLUSION (p-value)
+```
+if p_value <= acceptance_criteria:
+    print(f'As our p-value of {p_value} is LOWER than our citical value of {acceptance_criteria}, we REJECT the null hypothesis and conclude that {alternate_hypothesis}')
+else:
+    print(f'As our p-value of {p_value} is HIGHER than our citical value of {acceptance_criteria}, we ACCEPT the null hypothesis and conclude that {null_hypothesis}')
+```
+---
+
+
+#### Output in our console (after the code is run):
+```
+As our chi-suqre-statistic of 1.9414468614812481 is LOWER than our citical value of 3.841458820694124, we ACCEPT the null hypothesis and conclude that there is no relationship between mailer type and signup rate. They are independednt.
+As our p-value of 0.16351152223398197 is HIGHER than our citical value of 0.05, we ACCEPT the null hypothesis and conclude that there is no relationship between mailer type and signup rate. They are independednt.
+```
+---
+
+#### *Business Insight: The Marketing Team can safely utilise the basic (cheaper) mailer as a means to increase signups. Using the colourful more expensive mailer would result in unnecessary costs/expenses for the company.*