Python Developer Internship Portfolio

Cloud Counselage & GIFT & Career Foundation
Intern Details: Dhairya Manish Jesani (IP11275)
Domain: Python Developer
University: Universal AI University

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📌 Repository Overview

This repository contains the source code, data analysis pipelines, and formal deliverables for two analytical projects completed during the Python Developer Internship (June 12, 2026 - July 1, 2026). The projects demonstrate data engineering (cleansing, deduplication, outlier handling), feature engineering, exploratory data analysis (EDA), statistical modeling, and automated report generation in Python.

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⚙️ Data Analytics Workflow

Both projects follow a modular, production-grade data engineering pipeline:

graph TD
    A[Raw Data Ingestion] --> B[Data Cleaning & Quality Audit]
    B -->|Deduplication & Formatting| C[Feature Engineering]
    C -->|Calculate Complexity / Categoricals| D[Statistical Modeling]
    D -->|ANOVA, Welch's t-test, OLS| E[Data Visualization]
    E -->|Matplotlib / Seaborn| F[Report Compilation]
    F -->|PDF / Word / PPTX| G[Completed Submission Packages]

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📊 Sub-Project 1: Student Demographics Analytics (Basic)

• Goal: Clean and analyze a dataset of 4,894 webinar registrations to uncover relationships between student demographics, academic performances, technical competence, and expected salaries.
• Core Findings:
  • Deduplication: Wiped out redundant email registrations, reducing the dataset to 2,157 unique students (retaining first registration).
  • Income & Academics: A One-way ANOVA test revealed a statistically significant relationship between family income and CGPA ($F = 4.175$, $p = 0.006$). The highest mean CGPA was in the 7 Lakh+ bracket (8.41), while the lowest was in the 5-7 Lakh bracket (7.57).
  • Salary Expectations: Linear regression models (OLS) showed that CGPA and Python experience explain 4.5% of expected salary variance. High-performing students (CGPA >= 8.5) expect an average salary of 17.16 Lakhs (30% premium over the baseline of 13.20 Lakhs).
  • Extracurricular Impact: A Welch's t-test showed that leadership/extracurricular activities had no statistically significant impact on expected salary ($p = 0.998$).
  • Top Channels: WhatsApp was the most effective active promotion channel (1,294 unique students).
• Key Source Files:
  • Basic/GPI_Project/cleaning.py: Cleans registration rows, standardizes design/colleges, and handles missing data.
  • Basic/GPI_Project/main.py: Main script running the EDA queries (Q1-Q16) and exporting 18 Seaborn plots.
  • Basic/GPI_Project/generate_notebook.py: Generates the analysis notebook programmatically.
  • Basic/GPI_Project/generate_report.py: Programmatically compiles the final statistical PDF summary.

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🍲 Sub-Project 2: Indian Food Cuisine Analytics (Intermediate)

• Goal: Clean and analyze a database of 5,994 recipes to profile cooking times, dietary compliance, regional dominant courses, and dish complexity scores.
• Core Findings:
  • Deduplication: Removed 18 duplicate recipe rows, leaving 5,976 unique dishes.
  • Outlier Capping: Identified a 7,200-minute cooking time outlier for homemade raisins that skewed the cooking time average to 167 minutes. Implementing a 300-minute statistical cap normalized the average cooking time to 31.67 minutes.
  • Complexity Score: Feature engineered a recipe Complexity Score. A Welch's t-test proved that International cuisines have higher average complexity (41.64) compared to Indian cuisines (39.76) ($t = -2.16$, $p = 0.03$).
  • Rating Comparison: A Welch's t-test compared rating distributions between high-protein veg and non-veg dishes ($t = -2.28$, $p = 0.02$). Veg ratings averaged slightly higher (4.00 vs 3.99).
  • Dietary Breakdown: Vegan dishes had the longest average cooking times (38.22 minutes), and eggetarians had the highest median cooking times (30.00 minutes).
  • Course Representation: Continental dishes dominated breakfast (29.89%) and desserts (33.87%), while North Indian recipes dominated main courses (13.31%).
• Key Source Files:
  • Intermediate/src/data_cleaning.py: Ingests and cleans recipe databases, standardizes units, and extracts ratings.
  • Intermediate/src/feature_engineering.py: Computes recipe complexity scores and dietary flag variables.
  • Intermediate/src/analysis.py: Runs Welch's t-tests and course mode distribution summaries.
  • Intermediate/src/visualize.py: Generates and exports Seaborn distribution and correlation charts.
  • Intermediate/src/report_generator.py: Compiles the executive PDF report using ReportLab.

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📁 Internship Submission Deliverables

The deliverables for both projects are organized in two dedicated folders:

• Deliverables_Basic/ (Student Demographics project documents)
• Deliverables_Intermediate/ (Indian Food Cuisine project documents)

Each folder contains the following 8 completed, aligned, and properly formatted files:

#	File Name Pattern	Description / Updates Made
1	IAC IP11275 - Requirement Elicitation Questionnaire - Python Developer - Dhairya Manish Jesani.xlsx	80 comprehensive elicitation questions answered with project context; populated metadata.
2	IAC IP11275 - Project Charter - Python Developer - Dhairya Manish Jesani.xlsx	Fully drafted business case, constraints, milestone dates, and approval scopes.
3	IAC IP11275 - Software Requirement Specifications (SRS) - Python Developer - Dhairya Manish Jesani.xlsx	Detailed functional/non-functional specs and hardware/software environment versions.
4	IAC IP11275 - Python Developer - Project Schedule - Dhairya Manish Jesani.xlsx	Formatted Gantt chart schedule mapping out phase tasks with dependencies and owners. Point 4 in Guide sheet updated.
5	IAC IP11275 - RAID Log - Python Developer - Dhairya Manish Jesani.xlsx	5 detailed logs covering Risks, Assumptions, Issues, and Dependencies with mitigations.
6	IAC IP11275 - Lessons Learnt Log - Python Developer - Dhairya Manish Jesani.xlsx	Completed log sheets capturing key events, recommendations, and owner details.
7	IAC IP11275 - Project Report - Python Developer - Dhairya Manish Jesani.docx	Replaced all brackets with formal text. Filled first-page cover shapes (<DOMAIN NAME> & <PROJECT NAME>). Added Appendix A quantitative results table.
8	IAC IP11275 - WBS - Python Developer - Dhairya Manish Jesani.pptx	PowerPoint deck containing exactly 1 slide of the WBS diagram. Slide title changed to "WORK BREAKDOWN STRUCTURE - IP11275 Live Project Deliverables 'Python Developer - Data Analytics'". Updated SmartArt box texts to reflect project-specific tasks.

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🚀 Execution & Setup Guide

Prerequisites

Make sure you have Python 3.8+ installed.

Dependencies

Install the required packages using the requirements.txt file located in the project folders:

pip install pandas numpy openpyxl matplotlib seaborn python-docx python-pptx reportlab jupyter

Running the Pipelines

1. Basic Project Demographics Pipeline:

   cd "Basic/GPI_Project"
   python main.py

2. Intermediate Project Recipes Pipeline:

   cd "Intermediate/src"
   python main.py