Employee Attrition Prediction Model

@MashoodurRehman1

I am a data scientist with extensive experience in building predictive models and cleaning datasets. I have a strong foundation in Python and am proficient with libraries such as pandas, scikit-learn, and matplotlib, ensuring I can deliver a robust attrition prediction model for your needs. I have previously developed machine learning pipelines that required similar preprocessing steps, such as handling missing data and encoding categorical variables. My background includes performing exploratory data analysis to uncover key data insights. I am well-versed in K-Nearest Neighbours, and I can effectively tune and compare it with other classification algorithms to provide an optimized solution. I will ensure comprehensive documentation so you can replicate the workflow seamlessly. I look forward to discussing this project further and am ready to clarify any specific requirements you might have for the visualizations or report. Feel free to reach out with any questions.

@proffesor1

Hello, I work as data analyst 10+ years and could help you with this analysis and visualization. I could suggest the best way how classify data based on demographics and attrition probability. I could prepare a detailed report according to specifications depicted in the job description and working well structure Python notebook for results replication. I estimate this in 8-10 hrs of work. I could start today evening or tomorrow and complete all in 3-4 days. Please let me know if you are interested in my services. Regards, Alex.

@irsolutions

With over a decade of experience in building high-performance solutions, I understand your goal of creating an Employee Attrition Prediction Model using demographic details and job performance records. My background in scaling projects for over 1 million users and working on high-security FinTech systems directly applies to the complexity of turning raw data into a reliable prediction pipeline for your project. For strategic insight, I would recommend focusing on thorough data cleaning and feature engineering to ensure the model's accuracy. In a past project, I successfully implemented a similar pipeline that accurately predicted user behaviors, showcasing my ability to handle complex data transformations and model training effectively. I encourage you to reach out so we can discuss the roadmap for your Employee Attrition Prediction Model. I am confident that my expertise in data preprocessing, model training, and result evaluation align perfectly with your project requirements. Let's work together to create a cutting-edge solution that drives actionable insights for your management team.

@TheElitePro

Hi, 1. I understand you need a fully reproducible attrition prediction pipeline, starting from raw HR data to a validated KNN-based model with clear insights and visualizations for decision-making. 2. I can do data preprocessing, ML modeling, and end-to-end pipeline development using pandas and scikit-learn. My focus is on clean, interpretable workflows that run reliably and produce business-ready insights. 3. My approach: a) Data cleaning: handle missing values, outliers, encoding (One-Hot/Label), scaling b) EDA: identify attrition drivers, validate distributions, detect imbalance c) Feature engineering where useful (tenure bands, derived metrics) d) Model: train/tune KNN (k selection, distance metrics, scaling impact) e) Cross-validation + evaluation (Accuracy, Precision, Recall, F1, ROC-AUC) 4. Visualizations: • Attrition by department • Attrition by tenure bands • Feature importance plot (interpretable for stakeholders) 5. Deliverables: a) Clean dataset (CSV/Parquet) b) End-to-end Python notebook (fully reproducible) c) All visualizations embedded/exported d) Concise report with insights + recommendations 6. I am a preferred freelancer and have 5 star rating with track record of delivering all the project successfully. All this has resulted into repeat customers. Let's connect today if possible. I am also available this weekend if you want to proceed with the project urgently. Regards, Vishal

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@KolmanOuassou

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@FaizalShaik80

Hello, hope you are well. I’ve carefully reviewed your requirements, and this is essentially the same type of project I completed two months ago. I am a skilled freelancer with 6+ years of experience in Python and I can deliver the results as quickly as possible. You can visit my profile to check my latest work and recent reviews. Looking forward to working with you, connect in chat. Regards.

@kamran2012

Good to see this project, I will deliver the full attrition-prediction pipeline — cleaned dataset, documented notebook, KNN classifier with hyperparameter tuning, and all three visualizations your management team needs. I will use SHAP values alongside permutation importance to approximate feature relevance for KNN — this gives richer insight than permutation alone, especially for correlated features like tenure and department. Questions: 1) How large is the dataset — roughly how many rows and how imbalanced is attrition? 2) Is there a preferred metric to optimize during tuning — recall or F1? Looking forward to discussing further. Best regards, Kamran

@emilesd617

Hi, I am a full-stack AI developer with over 8 years of rich experience in software development, with a background in data science, Python, and machine learning. For this project, I will clean and preprocess your dataset, then build a reliable attrition-prediction model using K-Nearest Neighbours, while comparing it with other algorithms if necessary. I’ll also provide visual insights on attrition rates and feature importance to help your management team. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@alizain721

You want KNN included in the final deliverable even if it’s outperformed — that tells me you care about explainability and consistency, not just leaderboard scores. Often the real issue isn’t the classifier but messy encodings, class imbalance and unclear tenure bands; fixing those and documenting each decision yields far better, trustworthy predictions for management to act on. I recently built an end-to-end attrition pipeline for a mid-sized client (~5k records) where KNN served as a robust baseline and SHAP/permutation importance drove the retention recommendations. I’ll clean and document every preprocessing step (missing values, encoding, scaling), run focused EDA (department and tenure-band views), train and tune KNN with stratified CV, report accuracy/precision/recall/F1/ROC-AUC, and produce permutation/SHAP explanations plus PNG visuals. You’ll get the cleaned dataset (CSV or Parquet), a single runnable Python notebook, all charts, and a short report. I can do this for $20. Quick question: is the target column named "Attrition" with Yes/No values, and do you have preferred tenure-band cutoffs?

@MZITSERVICES

Hi there, I am A.R.M. MASUD, with a strong Data Science background. As a Python developer, I have extensive experience building robust, scalable, and efficient solutions that address various business needs. I understand the importance of delivering high-quality, well-architected code, and I am committed to working closely with you to ensure the success of this project. I implement core functionality using Python, utilizing relevant libraries and frameworks such as Pandas, NumPy, GUI, SciPy, Matplotlib, Seaborn, Plotly, Scikit-learn, TensorFlow, Keras, PyTorch, spaCy, Flask, Django, FastAPI, OpenCV, and Jupyter. I am a professional responsible for extracting actionable insights and knowledge from large volumes of data through Machine Learning models, including CNNs, RNNs, LSTMs, GANs, Transformers, FNNs, ANNs, and DNNs. I conduct comprehensive unit, integration, and performance testing to ensure the solution is error-free and optimized. https://www.freelancer.com/u/MZITSERVICES I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@chiragardeshna

Hi there, A strong fit for this work, with experience building end-to-end ML pipelines for structured datasets including preprocessing, modeling, and clear business insights. Clear understanding of your requirement to clean data, perform EDA, build a KNN-based attrition model, compare alternatives, and deliver reproducible results with visual insights. Hands-on expertise with pandas, scikit-learn, and visualization tools ensures clean pipelines, proper tuning, and accurate evaluation across all metrics. Risk stays minimized through reproducible workflows, validation checks, and consistent metric verification. Available to start immediately happy to discuss approach and next steps. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@arslanshahid386

Hi there, I see that you're looking to build an employee attrition prediction model using your dataset, which includes demographic details, job performance records, and employee engagement survey results. My approach would start with thorough data cleaning and preprocessing to ensure the dataset is ready for analysis. I’ll then conduct an exploratory analysis to identify key factors influencing attrition before moving on to the K-Nearest Neighbours classifier. With 4+ years of experience in data science and a solid understanding of Python and its libraries, I can efficiently handle the entire pipeline, document each step, and provide clear visual insights for your management team. Could you clarify if there are any specific departments or factors you'd like me to focus on during the exploratory analysis? Best regards, Arslan Shahid

@malkesh3m

⭐ Hello, I have checked your requirements for AI/ML Developer. We are a highly skilled data scientist, AI/ML developer with expertise in predictive modeling, statistics, and machine learning, analytics specifically within Python Our experience with AI/ML, Data Science, and Python includes proficiency in - Python, Django, Flask, FastAPI, Jupyter Notebook, Selenium, Data Visualization, ETL - Web App Development, Data Science, Web/API Scrapping - API Development, Authentication, Authorization - SQLAlchemy, PostegresDB, MySQL, SQLite, SQLServer, Datasets - Web hosting, Docker, Azure, AWS, GPC, Digital Ocean, GoDaddy, Web Hosting - Python Libraries: NumPy, pandas, scikit-learn, tensorflow, etc. Tableau, PowerBI We can start as soon as possible and work 20-40 hours weekly. We look forward to hearing from you soon. Thank you for your consideration.

@dataEntryExperty

Hi, I can build a robust employee attrition prediction pipeline from your dataset. I’ll handle data cleaning, preprocessing, and EDA to uncover key attrition drivers, then develop and tune a KNN-based model (with comparisons if needed). You’ll get a fully reproducible workflow, detailed evaluation (accuracy, precision, recall, F1, ROC-AUC), and clear visual insights including attrition trends and feature importance (via SHAP/permutation). I’ll deliver a clean dataset and a well-documented end-to-end Python3 notebook. I'll give you notebook run error free on your computer. I'll give you report as well as short video for explaining each step.. Let’s discuss your data and timeline.

@Srashtasoft

Hi there, I have read your project requirement carefully. You need to build a complete attrition prediction pipeline including data preprocessing, exploratory analysis, KNN-based modeling (with comparison), evaluation metrics, visualizations, and a fully reproducible Python workflow. We will clean and preprocess your dataset (handling missing values, encoding, scaling), perform EDA to identify key attrition drivers, and build a KNN model with proper hyperparameter tuning and cross-validation. We will also compare with other models if needed, generate required visualizations, and deliver a fully executable notebook along with a concise report and cleaned dataset. Before we proceed, I have a few questions: ================================= What is the size of your dataset (rows/columns)? Is the target variable already defined as attrition (yes/no)? Do you prefer SHAP or permutation importance for feature analysis? Any specific format preference for the final report (PDF, markdown, etc.)? Best Regards, Srashtasoft Team

@jayantm76

─── ⚡⭐⋆☆⋆⭐⚡ ─── Hi, Client I am a strong fit for this because I build reliable HR analytics and machine learning pipelines in Python using pandas, scikit learn, matplotlib, and SHAP, and my rate is $20 per hour. I can handle missing values, categorical encoding, scaling, cross validation, KNN tuning, metric reporting, and clean reproducible notebooks that run end to end on a standard Python 3 setup. I have done similar prediction work where I turned messy business data into documented models, clear visuals, and reports that non technical teams could actually use. Your real goal is a trustworthy attrition pipeline with solid KNN results, clear driver analysis, and outputs your team can rerun and validate easily. So, I'll: Build the full cleaning, EDA, KNN modeling, and validation workflow. Deliver the cleaned dataset, visuals, and a report with next step recommendations. Best, Jayant

@ayesha86664

Hi there! You are building an attrition prediction pipeline and the real challenge is making KNN reliable on mixed HR data while keeping the workflow fully reproducible and interpretable for decision making. I recently developed an employee attrition model where proper preprocessing and feature handling improved recall by 32% and revealed key drivers through SHAP-based insights. My work focuses on clean pipelines using pandas and scikit-learn with clear validation and reproducible notebooks. I will clean and preprocess your dataset, perform focused EDA, and build a tuned KNN model with cross-validation and full metric reporting. I will also include visual insights for department, tenure, and feature impact with clear explanations for management use. Check our work: https://www.freelancer.com/u/ayesha86664 Question: Is the attrition label balanced or should I plan for techniques like resampling to handle class imbalance? I am ready to start — just say the word. Best Regards, Ayesha

@souvikbasu

KNN for attrition is a smart starting point — it gives surprisingly clean baselines once feature scaling and categorical encoding are handled right. I'd pair it with permutation importance so the management deck isn't just accuracy numbers but "here's why". My approach: • Preprocessing: imputation, target-encoding for high-cardinality categoricals, StandardScaler (critical for KNN) • Compare KNN vs RandomForest/XGBoost with stratified 5-fold CV — accuracy/precision/recall/F1/ROC-AUC • SHAP + permutation importance for interpretability (works well alongside KNN) • Clean notebook + runnable script, pinned requirements, end-to-end reproducible Two quick questions: 1. Approx dataset size and % of the attrition=1 class? Drives SMOTE/class-weight decisions. 2. Any tenure bands you use internally (0–1y, 1–3y, etc.), or pick sensible defaults? Happy to share a reproducible notebook within 2–3 days once we agree on scope. I've built HR attrition pipelines like this before — will keep it light and auditable.