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"cells": [
{
"cell_type": "markdown",
"id": "cdd7b111",
"metadata": {},
"source": [
"### EDA and Data Lookup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "50ddeacc",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:31.659870Z",
"iopub.status.busy": "2026-07-05T07:01:31.659399Z",
"iopub.status.idle": "2026-07-05T07:01:32.505941Z",
"shell.execute_reply": "2026-07-05T07:01:32.504134Z"
}
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "599b8d5d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.510739Z",
"iopub.status.busy": "2026-07-05T07:01:32.510245Z",
"iopub.status.idle": "2026-07-05T07:01:32.593359Z",
"shell.execute_reply": "2026-07-05T07:01:32.591374Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>role</th>\n",
" <th>type</th>\n",
" <th>job_desc</th>\n",
" <th>year</th>\n",
" <th>qualification</th>\n",
" <th>experience</th>\n",
" <th>tech_skills</th>\n",
" <th>soft_skills</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>AI Engineer</td>\n",
" <td>Internship</td>\n",
" <td>Final year students / recent graduates in CS, ...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0</td>\n",
" <td>Python, ML, LangChain, Hugging Face, OpenAI API</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>AI Engineer</td>\n",
" <td>Internship</td>\n",
" <td>Proficiency in Python and comfort working with...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0</td>\n",
" <td>Python, APIs, LLMs, Prompt Engineering, LangCh...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>AI Engineer</td>\n",
" <td>Internship</td>\n",
" <td>Strong interest in AI and a willingness to lea...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0</td>\n",
" <td>Python, Javascript, React, Full Stack, APIs, M...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>AI Engineer</td>\n",
" <td>Senior</td>\n",
" <td>6+ years full-stack software engineering exper...</td>\n",
" <td>2026</td>\n",
" <td>NaN</td>\n",
" <td>5-10</td>\n",
" <td>Python, Javascript, React, Full Stack, SQL, AP...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>AI Engineer</td>\n",
" <td>Junior</td>\n",
" <td>AI/ML models, Python, Core ML concepts, ML fra...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0-1</td>\n",
" <td>Python, ML, TensorFlow/PyTorch, Scikit-Learn, ...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id role type \\\n",
"0 1 AI Engineer Internship \n",
"1 2 AI Engineer Internship \n",
"2 3 AI Engineer Internship \n",
"3 4 AI Engineer Senior \n",
"4 5 AI Engineer Junior \n",
"\n",
" job_desc year qualification \\\n",
"0 Final year students / recent graduates in CS, ... 2026 Bachelors \n",
"1 Proficiency in Python and comfort working with... 2026 Bachelors \n",
"2 Strong interest in AI and a willingness to lea... 2026 Bachelors \n",
"3 6+ years full-stack software engineering exper... 2026 NaN \n",
"4 AI/ML models, Python, Core ML concepts, ML fra... 2026 Bachelors \n",
"\n",
" experience tech_skills \\\n",
"0 0 Python, ML, LangChain, Hugging Face, OpenAI API \n",
"1 0 Python, APIs, LLMs, Prompt Engineering, LangCh... \n",
"2 0 Python, Javascript, React, Full Stack, APIs, M... \n",
"3 5-10 Python, Javascript, React, Full Stack, SQL, AP... \n",
"4 0-1 Python, ML, TensorFlow/PyTorch, Scikit-Learn, ... \n",
"\n",
" soft_skills \n",
"0 Communication, Problem Solving \n",
"1 Communication, Problem Solving \n",
"2 Communication, Problem Solving \n",
"3 Communication, Problem Solving \n",
"4 Communication, Problem Solving "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(r\"C:\\Portfolio-Projects\\Job-Description-Analysis\\data\\raw\\Job_descriptions.csv\")\n",
"df.head(5)"
]
},
{
"cell_type": "markdown",
"id": "4addff6c",
"metadata": {},
"source": [
"### EDA"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4f762807",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.596937Z",
"iopub.status.busy": "2026-07-05T07:01:32.596513Z",
"iopub.status.idle": "2026-07-05T07:01:32.606232Z",
"shell.execute_reply": "2026-07-05T07:01:32.605189Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"id 210\n",
"role 210\n",
"type 210\n",
"job_desc 210\n",
"year 210\n",
"qualification 210\n",
"experience 210\n",
"tech_skills 210\n",
"soft_skills 210\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().count()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e9aab529",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.610391Z",
"iopub.status.busy": "2026-07-05T07:01:32.609903Z",
"iopub.status.idle": "2026-07-05T07:01:32.628368Z",
"shell.execute_reply": "2026-07-05T07:01:32.627211Z"
}
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>210.000000</td>\n",
" <td>210.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>105.823810</td>\n",
" <td>2026.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>60.033264</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
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" <th>min</th>\n",
" <td>1.000000</td>\n",
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" <td>106.500000</td>\n",
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" id year\n",
"count 210.000000 210.0\n",
"mean 105.823810 2026.0\n",
"std 60.033264 0.0\n",
"min 1.000000 2026.0\n",
"25% 54.250000 2026.0\n",
"50% 106.500000 2026.0\n",
"75% 158.750000 2026.0\n",
"max 201.000000 2026.0"
]
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e21cc8a1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.632751Z",
"iopub.status.busy": "2026-07-05T07:01:32.632391Z",
"iopub.status.idle": "2026-07-05T07:01:32.652382Z",
"shell.execute_reply": "2026-07-05T07:01:32.650828Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.DataFrame'>\n",
"RangeIndex: 210 entries, 0 to 209\n",
"Data columns (total 9 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 id 210 non-null int64\n",
" 1 role 210 non-null str \n",
" 2 type 210 non-null str \n",
" 3 job_desc 210 non-null str \n",
" 4 year 210 non-null int64\n",
" 5 qualification 171 non-null str \n",
" 6 experience 210 non-null str \n",
" 7 tech_skills 210 non-null str \n",
" 8 soft_skills 210 non-null str \n",
"dtypes: int64(2), str(7)\n",
"memory usage: 14.9 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d3f29f62",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.656033Z",
"iopub.status.busy": "2026-07-05T07:01:32.655547Z",
"iopub.status.idle": "2026-07-05T07:01:32.671172Z",
"shell.execute_reply": "2026-07-05T07:01:32.669846Z"
}
},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>2</th>\n",
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" <td>AI Engineer</td>\n",
" <td>Internship</td>\n",
" <td>Strong interest in AI and a willingness to lea...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0</td>\n",
" <td>Python, Javascript, React, Full Stack, APIs, M...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>AI Engineer</td>\n",
" <td>Senior</td>\n",
" <td>6+ years full-stack software engineering exper...</td>\n",
" <td>2026</td>\n",
" <td>Not Specified</td>\n",
" <td>5-10</td>\n",
" <td>Python, Javascript, React, Full Stack, SQL, AP...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>AI Engineer</td>\n",
" <td>Junior</td>\n",
" <td>AI/ML models, Python, Core ML concepts, ML fra...</td>\n",
" <td>2026</td>\n",
" <td>Bachelors</td>\n",
" <td>0-1</td>\n",
" <td>Python, ML, TensorFlow/PyTorch, Scikit-Learn, ...</td>\n",
" <td>Communication, Problem Solving</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id role type \\\n",
"0 1 AI Engineer Internship \n",
"1 2 AI Engineer Internship \n",
"2 3 AI Engineer Internship \n",
"3 4 AI Engineer Senior \n",
"4 5 AI Engineer Junior \n",
"\n",
" job_desc year qualification \\\n",
"0 Final year students / recent graduates in CS, ... 2026 Bachelors \n",
"1 Proficiency in Python and comfort working with... 2026 Bachelors \n",
"2 Strong interest in AI and a willingness to lea... 2026 Bachelors \n",
"3 6+ years full-stack software engineering exper... 2026 Not Specified \n",
"4 AI/ML models, Python, Core ML concepts, ML fra... 2026 Bachelors \n",
"\n",
" experience tech_skills \\\n",
"0 0 Python, ML, LangChain, Hugging Face, OpenAI API \n",
"1 0 Python, APIs, LLMs, Prompt Engineering, LangCh... \n",
"2 0 Python, Javascript, React, Full Stack, APIs, M... \n",
"3 5-10 Python, Javascript, React, Full Stack, SQL, AP... \n",
"4 0-1 Python, ML, TensorFlow/PyTorch, Scikit-Learn, ... \n",
"\n",
" soft_skills \n",
"0 Communication, Problem Solving \n",
"1 Communication, Problem Solving \n",
"2 Communication, Problem Solving \n",
"3 Communication, Problem Solving \n",
"4 Communication, Problem Solving "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.fillna('Not Specified', inplace=True).head(5)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "719b05fd",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.675240Z",
"iopub.status.busy": "2026-07-05T07:01:32.674649Z",
"iopub.status.idle": "2026-07-05T07:01:32.687557Z",
"shell.execute_reply": "2026-07-05T07:01:32.686187Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"id 200\n",
"role 2\n",
"type 3\n",
"job_desc 208\n",
"year 1\n",
"qualification 4\n",
"experience 6\n",
"tech_skills 205\n",
"soft_skills 8\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b61eed8e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.691255Z",
"iopub.status.busy": "2026-07-05T07:01:32.690871Z",
"iopub.status.idle": "2026-07-05T07:01:32.698027Z",
"shell.execute_reply": "2026-07-05T07:01:32.696400Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StringArray>\n",
"['AI Engineer', 'AI Developer']\n",
"Length: 2, dtype: str\n"
]
}
],
"source": [
"role_unique = df['role'].unique()\n",
"print(role_unique)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "456c159c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.701548Z",
"iopub.status.busy": "2026-07-05T07:01:32.701190Z",
"iopub.status.idle": "2026-07-05T07:01:32.707737Z",
"shell.execute_reply": "2026-07-05T07:01:32.706063Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StringArray>\n",
"['Internship', 'Senior', 'Junior']\n",
"Length: 3, dtype: str\n"
]
}
],
"source": [
"type_unique = df['type'].unique()\n",
"print(type_unique)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0748fbf2",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.711265Z",
"iopub.status.busy": "2026-07-05T07:01:32.710734Z",
"iopub.status.idle": "2026-07-05T07:01:32.717419Z",
"shell.execute_reply": "2026-07-05T07:01:32.715670Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StringArray>\n",
"['0', '5-10', '0-1', '1-5', '15+', '10-15']\n",
"Length: 6, dtype: str\n"
]
}
],
"source": [
"experience_unique = df['experience'].unique()\n",
"print(experience_unique)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2b03a4a0",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-05T07:01:32.720912Z",
"iopub.status.busy": "2026-07-05T07:01:32.720366Z",
"iopub.status.idle": "2026-07-05T07:01:32.739180Z",
"shell.execute_reply": "2026-07-05T07:01:32.737432Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<>:1: SyntaxWarning: \"\\P\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\P\"? A raw string is also an option.\n",
"<>:1: SyntaxWarning: \"\\P\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\P\"? A raw string is also an option.\n",
"C:\\Users\\Akshay\\AppData\\Local\\Temp\\ipykernel_2816\\2088954097.py:1: SyntaxWarning: \"\\P\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\P\"? A raw string is also an option.\n",
" df.to_csv(\"C:\\Portfolio-Projects\\Job-Description-Analysis\\data\\clean\\cleaned_job_descriptions.csv\", index=False)\n"
]
}
],
"source": [
"df.to_csv(\"C:\\Portfolio-Projects\\Job-Description-Analysis\\data\\clean\\cleaned_job_descriptions.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "284aa9f4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.14.0"
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"nbformat": 4,
"nbformat_minor": 5
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|