Mehmet Emre Toktay

																		
    import numpy as np
    import matplotlib.pyplot as plt


    categories = ['Customer Experience', 'Communication skills', 'Customer service skills',
                  'Time management skills', 'Analytical skills', 'Mathematics skills', 'Problem-solving skills',
                  'Research skills', 'Writing skills', 'Project management skills', 'Process improvement skills',
                  'Leadership skills', 'Data analysis skills', 'Statistical analysis skills', 'Programming skills',
                  'Machine learning skills', 'Data manipulation skills', 'Data visualization skills', 'Business Intelligence',
                  'Technical skills']


    skill_ratings = [0.898, 0.758, 1.000, 0.818, 0.909, 0.757, 0.857, 0.969, 0.818, 0.767, 1.000,
                     0.847, 0.909, 0.929, 0.812, 0.727, 0.909, 0.767, 0.846, 0.863]

    skill_ratings = np.array(skill_ratings) * 100

    N = len(categories)

    fig, ax = plt.subplots(figsize=(7, 7), subplot_kw=dict(polar=True))

    theta = np.linspace(0, 2*np.pi, N, endpoint=False)

    skill_ratings = np.append(skill_ratings, skill_ratings[0])
    theta = np.append(theta, theta[0])

    ax.plot(theta, skill_ratings)

    ax.fill(theta, skill_ratings, alpha=0.2)

    ax.set_xticks(theta[:-1])
    ax.set_xticklabels(categories)

    ax.set_ylim(30, 100)
    ax.set_title('Exploring Professional Skills with ChatGPT',fontsize=16, fontweight='bold')
    ax.grid(True)
    plt.show()

I was curious to explore the potential of ChatGPT as a tool to help me visualize my professional skills. To that end, ChatGPT and I engaged in extended conversations about my work history, training, certifications, and academic writing. Together, we worked to evaluate the skills I had acquired during these experiences, taking into account factors such as the industry sectors of the companies I had worked for, the requirements of job descriptions, and the chronology of my work experiences.

During this process, ChatGPT provided me with information and guidance, which I reviewed and revised as needed. While I wasn't always entirely certain of the results, I found the overall experience to be enlightening and beneficial. I wanted to share the results of this evaluation with you, but I must note that these scores are subjective and reflect the perspective of the evaluator.

																		
from wordcloud import WordCloud, get_single_color_func
import matplotlib.pyplot as plt
import random

industries = ['Technology', 'BPO', 'Data Management', 'Hospitality', 'SaaS', 'Search Engines','Hardware','Software','BlockChain', 'Fintech']
counts = [3, 2, 1, 3, 2, 1, 2, 2, 1, 1]

industry_dict = dict(zip(industries, counts))

color_func = get_single_color_func('#ff5733')

def random_color(word, font_size, position, orientation, random_state=None, **kwargs):
  return tuple(random.randint(0, 255) for _ in range(3))

wordcloud = WordCloud(width=500, height=500, background_color='white', color_func=random_color,
      min_font_size=0, max_font_size=450).generate_from_frequencies(industry_dict)

plt.figure(figsize=(8,8))
plt.imshow(wordcloud)
plt.axis('off')
plt.title('Industries of Companies Worked With', fontsize=20, fontweight='bold')
plt.show()