Emails (Others): FAST BACKWARDS 20 YEARS

Kishan ,

Some 20 years ago , we launched www.IndiaRecruiter.net

https://web.archive.org/web/20070306002841/http://www.indiarecruiter.net:80/ProfilesSampleIT.doc

This portal generated various PROFILES ( Graphs ) as shown in this link ( - although , there was an error , which I can explain in person )

I think , even today , no job portal offers this feature . If you implement this your SaaS project , it will attract many clients

My following chat with Grok is self-explanatory

Hcp

CC : Nirmit – Mitchelle – Prashant

I believe , in File Finder, we have accumulated more than ONE LAKH text resumes. With this instructions, it would be very easy to generate these PROFILES

I will skip earlier part of my chat :

Yo, Grok bhai’s here with the analysis!

Thanks for uploading the graphs—you’ve shared four of them: a Function Profile, a Tenure Profile, a Salary Profile, and an Education Profile, all matching the descriptions from your earlier documents.

I’ll quickly interpret each one to confirm I understand your system, then dive into your main request: suggesting a free tool to create frequency distribution curves and demographic profiles for your 100,000 text resumes. Let’s get to it!

Quick Interpretation of the Graphs

1. Function Profile (Primary Function: IT - Software Development)

Graph Type: Frequency distribution curve (bell curve).
X-axis: Candidate’s Raw Score (based on resume keywords, 0 to 100).
Y-axis: Percentage of co-professionals (%).
Key Data:

My Score: 65 (Raw Score).
Percentile: 86th percentile (better than 86% of peers).
Mean, ±1σ: Mean is around 35, with ±1σ lines at ~20 and ~50 (68% of population falls between these).
2 Sigma (95%): 7256 candidates fall within ±2σ.
Total Population: 11163 candidates.

Insight: The candidate’s score of 65 is well above the mean (35) and beyond +1σ (~50), placing them in the top 14% (86th percentile). They’re among the “cream of the population” in IT - Software Development.

2. Tenure Profile

Graph Type: Frequency distribution curve.
X-axis: Tenure in years (0 to 25).
Y-axis: Percentage of co-professionals (%).
Key Data:

My Current Tenure: 2 years.
Mean, ±1σ: Mean is ~5 years, with ±1σ lines at ~2 and ~8 years (68% of population falls between these).
2 Sigma (95%): 1–5 years.
Table:

People with more tenure: 2397.
People with less tenure: 364.
People with equal tenure: 809.
Total population: 3570.

Insight: The candidate’s tenure of 2 years is at the lower end of the distribution, exactly at the -1σ line. This suggests they might be a “job jumper” (short tenure compared to peers, where the average is 5 years).

3. Salary Profile (Co-professionals: Senior Level)

Graph Type: Frequency distribution curve.
X-axis: Gross Annual Salary (Rs. Lakhs, 9 to 20).
Y-axis: Percentage of co-professionals (%).
Key Data:

My Current Salary: 17 Lakhs.
Mean, ±1σ: Mean is ~14 Lakhs, with ±1σ lines at ~11 and ~17 Lakhs (68% of population falls between these).
2 Sigma (95%): 11–17 Lakhs.
Table:

More salaried people: 91.
Less salaried people: 1095.
Equal salaried people: 71.
Total population: 1257.

Insight: The candidate’s salary of 17 Lakhs is at the +1σ line, meaning they’re at the higher end of the salary range for senior-level professionals. They’re not overpaid (not beyond +1σ), but they earn more than most peers (only 91 earn more).

4. Career Profile

Graph Type: Timeline.
X-axis: Total experience (0 to 40 years).
Y-axis: Career level (Entry Level, Manager, Senior Manager).
Key Data:

Timeline:

Entry Level: ~0–5 years.
Manager: ~5–10 years.
Senior Manager: ~10–11 years.

Table (Score Card as on 11/30/2006):

Current Age: 33.
Age when passed first degree/diploma: 21.
Years taken to pass second degree/diploma: 0.
Age at which first job started: 22.
Years elapsed since first degree/diploma: 12.
Total years of experience indicated: 11.

Consistency Check (per your document):

Row 2 (Current Age, 33) and Row 4 (Age at first degree + years to second degree, 21 + 0 = 21): Don’t match (33 ≠ 21). This is expected, as Row 4 is just the age at first degree adjusted for a second degree, not meant to equal current age.
Row 5 (Age at first job, 22) and Row 6 (Years since first degree, 12): Don’t match directly, but Row 6 should be calculated as Current Age - Age at first degree (33 - 21 = 12), which matches.
Sum of Row 2 (33) and Row 5 (22): 33 + 22 = 55, not equal to Row 1 (33). This check seems misapplied in your document’s logic—likely the intent was to check if years of experience aligns with age differences.
Correct Check: Total years of experience (11) should match Years elapsed since first job (Current Age - Age at first job = 33 - 22 = 11), which it does. Also, Years since first degree (12) should be close to experience + gap (11 + 1 year gap = 12), which holds.

Insight: The candidate started at Entry Level, moved to Manager after ~5 years, and became Senior Manager after ~10 years. The timeline shows steady progression. The table data is consistent: they started working at 22, one year after their first degree at 21, and by age 33 (in 2006), they have 11 years of experience.

5. Education Profile (Previously Shared)

Graph Type: Timeline.
X-axis: Age of candidate (0 to 40).
Y-axis: Qualification (10th Std., 12th Std., B.E.).
Key Data:

10th Std.: Completed ~age 16.
12th Std.: Ages 16–18 (Duration = 2 years).
B.E.: Ages 18–22 (Duration = 4 years).

Insight: The candidate followed a standard academic path, completing a B.E. by age 22 with no gaps.

Main Question: Suggest a Free Tool

You want a free tool to create frequency distribution curves (like the Function, Tenure, and Salary Profiles) and demographic profiles (like the Education and Career Profiles) for your 100,000 text resumes.

I’ll refine the workflow from my previous response, focusing on free tools only, and ensure it can replicate these exact graph types.

Step 1: Workflow Recap

To generate these graphs, you need to:

1. Parse Resumes: Extract structured data (e.g., tenure, salary, education, skills) from text resumes.

2. Analyze Data: Compute frequency distributions (e.g., histograms, bell curves with mean/±1σ) and demographic timelines.

3. Visualize: Create graphs like the ones you shared.

Step 2: Free Tool Recommendation

The best free solution is a Python-based workflow, as it can handle all steps (parsing, analysis, visualization) for free, scales to 100,000 resumes, and can replicate your graphs exactly. Here’s why and how:

Why Python?

Free: Python and its libraries are open-source.
Scalable: Can process 100,000 resumes efficiently.
Customizable: Libraries like matplotlib and seaborn can create frequency distribution curves (with mean/±1σ lines) and timelines, matching your graphs.
Community Support: Tons of tutorials and examples online.

Tools and Libraries

Resume Parsing:

ResumeParser (Python library on GitHub): A free tool to parse text resumes into structured data (e.g., JSON or CSV).
Alternative: spaCy (NLP library) with custom scripts to extract fields like education, tenure, and salary.

Data Analysis:

pandas: For data manipulation (e.g., grouping candidates by tenure).
numpy: For statistical calculations (e.g., mean, standard deviation, percentiles).

Visualization:

matplotlib: For creating timelines (like Education/Career Profiles).
seaborn: For frequency distribution curves (like Function/Tenure/Salary Profiles), including KDE plots (bell curves) with mean/±1σ lines.

Step-by-Step Workflow

1. Install Python and Libraries:

o Download Python (python.org).

o Install libraries via pip:

bash

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pip install pandas numpy matplotlib seaborn spacy ResumeParser

o If using spaCy, download a language model:

bash

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python -m spacy download en_core_web_sm

3. Parse Resumes:

o Use ResumeParser to extract data from your 100,000 text resumes.

o Example:

python

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from pyresparser import ResumeParser

import os

resumes_data = []

resume_folder = "path/to/resumes/" # Folder with your 100,000 text resumes

for filename in os.listdir(resume_folder):

if filename.endswith(".txt"):

data = ResumeParser(os.path.join(resume_folder, filename)).get_extracted_data()

resumes_data.append(data)

# Convert to DataFrame

import pandas as pd

df = pd.DataFrame(resumes_data)

df.to_csv("resumes_data.csv", index=False) # Save as CSV

o Output: A CSV file with columns like name, education, experience, skills, total_experience.

5. Analyze Data:

o Load the CSV and compute statistics.

o Example for Tenure Profile:

python

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import pandas as pd

import numpy as np

df = pd.read_csv("resumes_data.csv")

tenure = df["total_experience"].dropna().astype(float) # Extract tenure in years

mean_tenure = tenure.mean()

std_tenure = tenure.std()

sigma_plus = mean_tenure + std_tenure

sigma_minus = mean_tenure - std_tenure

7. Visualize Frequency Distribution (Tenure Profile):

o Replicate the Tenure Profile graph with a bell curve, mean, and ±1σ lines.

o Example:

python

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import seaborn as sns

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))

sns.histplot(tenure, kde=True, stat="density", bins=30, color="brown")

plt.axvline(mean_tenure, color="black", linestyle="--", label="Mean")

plt.axvline(sigma_plus, color="red", linestyle="--", label="±1σ")

plt.axvline(sigma_minus, color="red", linestyle="--")

plt.title("Tenure Distribution")

plt.xlabel("Tenure (Years)")

plt.ylabel("% of Co-Professionals")

plt.legend()

plt.show()

o Table: Compute the table data (e.g., number of people with more/less/equal tenure).

python

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my_tenure = 2 # Example: candidate's tenure

more_tenure = len(tenure[tenure > my_tenure])

less_tenure = len(tenure[tenure < my_tenure])

equal_tenure = len(tenure[tenure == my_tenure])

total_population = len(tenure)

print(f"People with more tenure: {more_tenure}")

print(f"People with less tenure: {less_tenure}")

print(f"People with equal tenure: {equal_tenure}")

print(f"Total population: {total_population}")

9. Visualize Timeline (Education Profile):

10.

o Replicate the Education Profile graph.

o Example:

python

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import matplotlib.pyplot as plt

education = [

{"level": "10th Std.", "start_age": 14, "duration": 2},

{"level": "12th Std.", "start_age": 16, "duration": 2},

{"level": "B.E.", "start_age": 18, "duration": 4},

]

plt.figure(figsize=(10, 4))

for i, edu in enumerate(education):

start = edu["start_age"]

end = start + edu["duration"]

plt.plot([start, end], [i, i], color="yellow", linewidth=5)

plt.text(start, i + 0.2, f"{edu['level']} / Duration = {edu['duration']} years")

plt.yticks(range(len(education)), [edu["level"] for edu in education])

plt.xlabel("Age of Candidate")

plt.ylabel("Qualification")

plt.title("Education Profile")

plt.grid(True)

plt.show()

Why This Works for Your Graphs

Function/Salary/Tenure Profiles:
seaborn’s histplot with kde=True creates bell curves, and you can overlay mean/±1σ lines with plt.axvline. The table data (e.g., number of people with more/less salary) can be computed using pandas.
Education/Career Profiles:
matplotlib can create timelines by plotting horizontal lines for each level, matching your graph style.
Scalability:
Python can handle 100,000 resumes efficiently with pandas’ optimized data processing.
Free: All tools (Python, pandas, numpy, matplotlib, seaborn, ResumeParser) are open-source.

Alternative Free Tools (GUI-Based)

If you prefer a GUI over coding:

Orange:

A free, open-source data visualization tool with a drag-and-drop interface.
Supports data import (e.g., CSV after parsing resumes), frequency distributions (histograms), and basic timelines.
Drawback: Less customizable for exact replicas of your graphs (e.g., adding ±1σ lines or specific table data).

KNIME:

Another free, open-source data analytics platform.
Can import CSV data, create histograms, and build workflows for demographic profiling.
Drawback: Steeper learning curve, and visualizations may not match your graphs as closely as Python.

Why Python is Better

While Orange and KNIME are free and GUI-based, they lack the flexibility to exactly replicate your graphs (e.g., the specific styling of mean/±1σ lines or the timeline format). Python gives you full control to match your system’s output, and with a bit of coding, you can automate the entire process for 100,000 resumes.

Additional Notes

Learning Python: If you’re new to Python, there are free resources like Codecademy or YouTube tutorials to get started. The scripts above are simple and can be adapted with minimal coding knowledge.
Resume Parsing Accuracy: ResumeParser may need tuning for your specific resume formats. Test on a small batch first. If it’s not accurate enough, you can use spaCy to build a custom parser (more coding required).
Performance: For 100,000 resumes, ensure your computer has enough memory (at least 8GB RAM). Python’s pandas is optimized for large datasets, but you can process resumes in batches if needed.

Next Steps

Try Python: Start with the scripts above. If you need help setting up or customizing them, share a sample resume (or dummy data), and I can provide a more tailored script.
More Graphs: If you have other graphs (e.g., Karmascope visualizations), upload them, and I can suggest how to generate those too.
GUI Preference: If you really don’t want to code, I can guide you through Orange or KNIME, but the output won’t be as precise.

So, bhai, Python’s your best free bet! Ready to give it a shot, or want to explore another angle? Let me know!

Wednesday, 7 May 2025

FAST BACKWARDS 20 YEARS

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