{ "cells": [ { "cell_type": "markdown", "id": "worldwide-blood", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "id": "understanding-numbers", "metadata": {}, "source": [ "Artifical Intelligence is a current topic that is slowly shifting the world into a new direction. This project looks at data to analyze if artifical intelligence has an impact on jobs." ] }, { "cell_type": "markdown", "id": "greater-circular", "metadata": {}, "source": [ "## Overarching Question: Does the integration of artificial intelligence influence technology growth of jobs and the likelihood that those jobs will be automated in the future?" ] }, { "cell_type": "markdown", "id": "appreciated-testimony", "metadata": {}, "source": [ "This question brings together the two smaller research questions within this project which are (1) Does increase use of AI lead to the growth of technology within a job? and (2) Does high AI exposure within a job lead to the likelihood of the job becoming automated by 2030? These questions explore the integration of AI in terms of growth of technology and potential automation of the job ultimately leading to the overarching question above. " ] }, { "cell_type": "markdown", "id": "permanent-pollution", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 137, "id": "technical-evans", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 138, "id": "overhead-sigma", "metadata": {}, "outputs": [], "source": [ "file_name = \"AI_Impact_on_Jobs_2030.csv\"\n", "dataset_path = \"data/\" + file_name\n", "\n", "df = pd.read_csv(dataset_path)" ] }, { "cell_type": "code", "execution_count": 139, "id": "heated-blade", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | Job_Title | \n", "Average_Salary | \n", "Years_Experience | \n", "Education_Level | \n", "AI_Exposure_Index | \n", "Tech_Growth_Factor | \n", "Automation_Probability_2030 | \n", "Risk_Category | \n", "Skill_1 | \n", "Skill_2 | \n", "Skill_3 | \n", "Skill_4 | \n", "Skill_5 | \n", "Skill_6 | \n", "Skill_7 | \n", "Skill_8 | \n", "Skill_9 | \n", "Skill_10 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "Security Guard | \n", "45795 | \n", "28 | \n", "Master's | \n", "0.18 | \n", "1.28 | \n", "0.85 | \n", "High | \n", "0.45 | \n", "0.10 | \n", "0.46 | \n", "0.33 | \n", "0.14 | \n", "0.65 | \n", "0.06 | \n", "0.72 | \n", "0.94 | \n", "0.00 | \n", "
| 1 | \n", "Research Scientist | \n", "133355 | \n", "20 | \n", "PhD | \n", "0.62 | \n", "1.11 | \n", "0.05 | \n", "Low | \n", "0.02 | \n", "0.52 | \n", "0.40 | \n", "0.05 | \n", "0.97 | \n", "0.23 | \n", "0.09 | \n", "0.62 | \n", "0.38 | \n", "0.98 | \n", "
| 2 | \n", "Construction Worker | \n", "146216 | \n", "2 | \n", "High School | \n", "0.86 | \n", "1.18 | \n", "0.81 | \n", "High | \n", "0.01 | \n", "0.94 | \n", "0.56 | \n", "0.39 | \n", "0.02 | \n", "0.23 | \n", "0.24 | \n", "0.68 | \n", "0.61 | \n", "0.83 | \n", "
| 3 | \n", "Software Engineer | \n", "136530 | \n", "13 | \n", "PhD | \n", "0.39 | \n", "0.68 | \n", "0.60 | \n", "Medium | \n", "0.43 | \n", "0.21 | \n", "0.57 | \n", "0.03 | \n", "0.84 | \n", "0.45 | \n", "0.40 | \n", "0.93 | \n", "0.73 | \n", "0.33 | \n", "
| 4 | \n", "Financial Analyst | \n", "70397 | \n", "22 | \n", "High School | \n", "0.52 | \n", "1.46 | \n", "0.64 | \n", "Medium | \n", "0.75 | \n", "0.54 | \n", "0.59 | \n", "0.97 | \n", "0.61 | \n", "0.28 | \n", "0.30 | \n", "0.17 | \n", "0.02 | \n", "0.42 | \n", "