Philippe Libioulle - Fab Futures - Data Science

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Week 2: tools - "AI impact jobs by 2030" dataset

Context

• Source: Kaggle
• Description: this dataset simulates the future of work in the age of artificial intelligence. It models how various professions, skills, and education levels might be impacted by AI-driven automation by the year 2030.

Load dataset

import pandas as pd 
import seaborn as sb
import matplotlib.pyplot as plt

df = pd.read_csv("datasets/AI_Impact_on_Jobs_2030.csv")

# 🧾 Display dataset informations
print("Dataset shape:", df.shape)
print(df.info)

Dataset shape: (3000, 18)
<bound method DataFrame.info of                 Job_Title  Average_Salary  Years_Experience Education_Level  \
0          Security Guard           45795                28        Master's   
1      Research Scientist          133355                20             PhD   
2     Construction Worker          146216                 2     High School   
3       Software Engineer          136530                13             PhD   
4       Financial Analyst           70397                22     High School   
...                   ...             ...               ...             ...   
2995               Doctor          111319                 6      Bachelor's   
2996        UX Researcher           44363                29             PhD   
2997       Data Scientist           61325                23        Master's   
2998     Graphic Designer          110296                 7             PhD   
2999     Graphic Designer          123909                25             PhD   

      AI_Exposure_Index  Tech_Growth_Factor  Automation_Probability_2030  \
0                  0.18                1.28                         0.85   
1                  0.62                1.11                         0.05   
2                  0.86                1.18                         0.81   
3                  0.39                0.68                         0.60   
4                  0.52                1.46                         0.64   
...                 ...                 ...                          ...   
2995               0.24                1.18                         0.20   
2996               0.65                0.74                         0.35   
2997               0.64                0.94                         0.39   
2998               0.95                1.23                         0.46   
2999               0.69                0.56                         0.49   

     Risk_Category  Skill_1  Skill_2  Skill_3  Skill_4  Skill_5  Skill_6  \
0             High     0.45     0.10     0.46     0.33     0.14     0.65   
1              Low     0.02     0.52     0.40     0.05     0.97     0.23   
2             High     0.01     0.94     0.56     0.39     0.02     0.23   
3           Medium     0.43     0.21     0.57     0.03     0.84     0.45   
4           Medium     0.75     0.54     0.59     0.97     0.61     0.28   
...            ...      ...      ...      ...      ...      ...      ...   
2995           Low     0.73     0.37     0.99     0.07     0.08     0.92   
2996        Medium     0.23     0.48     0.05     0.88     0.56     0.29   
2997        Medium     0.28     0.62     0.73     0.21     0.96     0.01   
2998        Medium     0.21     0.18     0.14     0.22     0.55     0.68   
2999        Medium     0.77     0.54     0.95     0.05     0.29     0.22   

      Skill_7  Skill_8  Skill_9  Skill_10  
0        0.06     0.72     0.94      0.00  
1        0.09     0.62     0.38      0.98  
2        0.24     0.68     0.61      0.83  
3        0.40     0.93     0.73      0.33  
4        0.30     0.17     0.02      0.42  
...       ...      ...      ...       ...  
2995     0.65     0.33     0.76      0.45  
2996     0.69     0.80     0.61      0.20  
2997     0.70     0.29     0.48      0.57  
2998     0.31     0.55     0.34      0.70  
2999     0.77     0.52     0.14      0.29  

[3000 rows x 18 columns]>

Explore content

df.head()

	Job_Title	Average_Salary	Years_Experience	Education_Level	AI_Exposure_Index	Tech_Growth_Factor	Automation_Probability_2030	Risk_Category	Skill_1	Skill_2	Skill_3	Skill_4	Skill_5	Skill_6	Skill_7	Skill_8	Skill_9	Skill_10
0	Security Guard	45795	28	Master's	0.18	1.28	0.85	High	0.45	0.10	0.46	0.33	0.14	0.65	0.06	0.72	0.94	0.00
1	Research Scientist	133355	20	PhD	0.62	1.11	0.05	Low	0.02	0.52	0.40	0.05	0.97	0.23	0.09	0.62	0.38	0.98
2	Construction Worker	146216	2	High School	0.86	1.18	0.81	High	0.01	0.94	0.56	0.39	0.02	0.23	0.24	0.68	0.61	0.83
3	Software Engineer	136530	13	PhD	0.39	0.68	0.60	Medium	0.43	0.21	0.57	0.03	0.84	0.45	0.40	0.93	0.73	0.33
4	Financial Analyst	70397	22	High School	0.52	1.46	0.64	Medium	0.75	0.54	0.59	0.97	0.61	0.28	0.30	0.17	0.02	0.42

Display a nice chart

target = 'Job_Title'
plt.figure(figsize=(10, 8))
sb.countplot(data=df, x=target, palette='pastel', hue=target, edgecolor='black')
plt.title(f'Distribution by {target}', fontsize=14)
plt.xlabel(target, fontsize=12)
plt.xticks(rotation=90)
plt.ylabel('Count', fontsize=12)
plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()

plt.show()