Kanjo-Elkamira-Ndi

Kanjo-Elkamira-Ndi / Data-Science-Operations-Pipeline-Lab

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A complete, end-to-end data science pipeline applied to a survey dataset investigating mobile money scam prevalence, victim demographics, and loss patterns in Cameroon. The project covers Exploratory Data Analysis, Data Preprocessing, Feature Engineering, Predictive Modelling, and Evaluation, culminating in a fully formatted Word report.

12
0
100% credibility
Found Mar 05, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository contains a Python script that performs a full data science analysis on a mobile money scam dataset, generating visualizations and model predictions for educational purposes.

How It Works

1
🔍 Discover the Scam Analyzer

You find this simple tool from a data class that helps explore patterns in mobile money scams using real survey data.

2
📥 Grab the Files

Download the analysis script and place your scam data file right next to it.

3
✏️ Name Your Data File

Tell the tool the exact name of your data file so it knows what to study.

4
▶️ Start the Magic

Run the script and watch it crunch the numbers step by step on screen.

5
📊 See Insights Pour In

Beautiful charts pop up showing victim stats, ages, scam tricks, and smart predictions.

6
🖼️ Browse Your Pictures

Open the new folder full of saved graphs to dive deeper into the findings.

🎉 Master the Scam Patterns

You now understand who falls for scams, top methods, and how well guesses predict victims.

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Star Growth

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AI-Generated Review

What is Data-Science-Operations-Pipeline-Lab?

This Python project delivers a complete end-to-end data science pipeline for analyzing a survey dataset on mobile money scams in Cameroon, covering victim demographics, loss patterns, and scam methods. Drop your CSV file, tweak the path, run the script, and it handles exploratory data analysis, preprocessing, feature engineering, trains logistic regression, random forest, and SVM models, then spits out evaluation metrics, plots, and a printed summary—all saved to a plots folder. It's a complete end-to-end solution for turning raw survey data into actionable insights like class distributions and feature importance.

Why is it gaining traction?

It stands out as a complete GitHub tutorial for a full MLops-style workflow in one runnable script, no setup hassle beyond pip install requirements. Developers grab it for the instant visualizations—histograms, heatmaps, ROC curves—that reveal patterns like top scam methods without writing boilerplate. With 12 stars, it's niche but hooks those seeking a complete end-to-end devops project for quick prototyping.

Who should use this?

Data science students or bootcamp participants practicing end-to-end analysis on real-world survey data. Junior ML engineers prototyping binary classification for fraud detection. Analysts in emerging markets evaluating scam prevalence without building from scratch.

Verdict

Solid learning tool for a complete end-to-end ML project, but at 1.0% credibility and 12 stars, it's raw—expect tweaks for your data and no tests or docs. Use for education, not production.

(178 words)

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