You will get MACHINE LEARNING AND EVALUATION
Project details
Losing top talent is expensive, but it doesn't have to be a surprise. If you have historical HR data, I can help you identify which employees are at the highest risk of leaving and exactly why they might walk out the door.
In this project, I build an end-to-end Machine Learning pipeline specifically designed to predict employee attrition. Raw HR datasets often suffer from severe class imbalances (most employees stay, a few leave). I specialize in addressing this using advanced techniques like SMOTE, ensuring the model accurately catches those at risk rather than just guessing the majority.
What to expect from this project:
Deep Data Cleaning: Handling missing values, encoding categories, and preparing your data for ML.
Insightful Exploratory Analysis: Visualizing your data to find immediate trends (e.g., how salary, department, or tenure impacts turnover).
Advanced Modeling: Training and tuning powerful algorithms like XGBoost and Random Forest for maximum precision and recall.
Actionable Deliverables: You get fully commented Python code (Jupyter Notebook/ VS code), performance charts, and a clear breakdown of the key factors driving your team's flight risk.
In this project, I build an end-to-end Machine Learning pipeline specifically designed to predict employee attrition. Raw HR datasets often suffer from severe class imbalances (most employees stay, a few leave). I specialize in addressing this using advanced techniques like SMOTE, ensuring the model accurately catches those at risk rather than just guessing the majority.
What to expect from this project:
Deep Data Cleaning: Handling missing values, encoding categories, and preparing your data for ML.
Insightful Exploratory Analysis: Visualizing your data to find immediate trends (e.g., how salary, department, or tenure impacts turnover).
Advanced Modeling: Training and tuning powerful algorithms like XGBoost and Random Forest for maximum precision and recall.
Actionable Deliverables: You get fully commented Python code (Jupyter Notebook/ VS code), performance charts, and a clear breakdown of the key factors driving your team's flight risk.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$25
|
Advanced
$35
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 15 days |
Number of Revisions | 2 | 4 | 8 |
Number of Model Variations | 1 | 4 | 6 |
Number of Scenarios | 1 | 4 | 6 |
Number of Graphs/Charts | 2 | 4 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Graph/Chart
+$5About Saman
AI & Machine Learning | Artificial Intelligence, C++, python
Lahore, Pakistan - 5:11 pm local time
AI Evaluator and Data Specialist with experience auditing multimodal models and building predictive machine learning
pipelines. Skilled in Python, ensemble learning, advanced data preprocessing (SMOTE), and unsupervised clustering.
Hands-on experience with Supervised Fine-Tuning (SFT), independent AI agent evaluation for computer use, and
OSWorld benchmarking, with a strong focus on mitigating model hallucinations, improving visual grounding, and
optimizing data-driven systems.
Projects
AI Date Sheet Generator
* Engineered an AI-driven exam scheduling engine in Python using Genetic Algorithms to automate course,
room, and invigilator allocations.
* Formulated multi-factor fitness penalty functions to enforce mandatory system constraints (Friday breaks,
zero room overlaps) and student preferences.
* Implemented Roulette Wheel Selection, Single-Point Crossover, and Mutation operators to efficiently explore
complex scheduling search spaces.
Steps for completing your project
After purchasing the project, send requirements so Saman can start the project.
Delivery time starts when Saman receives requirements from you.
Saman works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preprocessing & Exploratory Analysis
I will clean your dataset, handle missing values, and generate visual charts to uncover initial patterns, correlations, and trends related to employee turnover.
Class Balancing & Feature Engineering
Employee attrition data is often unbalanced. I will apply resampling techniques like SMOTE and engineer key features to ensure the model isn't biased toward the majority class.