Build a customer classification model

Budget: 16800 UAH Deadline: 10 days

I can help with building a customer classification model in Python. To assess the cost and timelines, I would like to clarify:
1. What specific data is contained in MongoDB/SQL (transactions, demographics, activity, interaction history, etc.)?
2. Are there already prepared class labels, or is it necessary to perform behavioral segmentation (clustering) and form groups?
3. What is the volume of data: only 20,000 customers or are there also related tables with transaction history?
4. Which DBMS are you using (MongoDB, PostgreSQL, MySQL, or another)?
5. What result do you expect to receive: a ready model, source code, API, or a Jupyter Notebook with explanations?
6. Is it necessary to perform data analysis, feature engineering, model quality assessment, and result visualization?
7. Which libraries or frameworks would you prefer to use (scikit-learn, XGBoost, CatBoost, TensorFlow, etc.)?
8. What deadlines and budget are you considering?

Budget: 27000 UAH Deadline: 14 days

I can take the Python part turnkey - from data extraction from Mongo/SQL to features, baseline models, quality checks, and preparing the results in the form of scripts or a notebook.

Look, there’s a nuance here - if there are already labels for behavioral groups, it will be classification. If there are no labels, then it’s more appropriate to start with segmentation or clustering, and then align the business interpretation of the groups. The price includes the initial model, feature preparation, quality assessment, launch documentation, and brief conclusions for the business =)

Questions
> Is there a ready target feature or should groups be found based on behavior?
> What output is needed - a file with customer groups, a Python pipeline, a report, or integration back into the database?

Similar cases from Ingello
> https://business.ingello.com/vorfahr - automation and AI logic for business processes
> https://business.ingello.com/fractal - agent and analytical architecture for complex systems
> https://business.ingello.com/forma-crm - CRM and working with customer data, close to the segmentation task

Main page for FLH - https://systems-fl.ingello.com/ua

Estimate - 48,000 UAH and 14 days for the first working version. I will provide a more accurate estimate after reviewing the table structure, an example of raw data, and the desired output format. Expand

Budget: 700 UAH Deadline: 2 days

Kostiantyn, I can quickly transform your raw data from Mongo/SQL into understandable behavioral groups. First, I will clean and prepare 20k records, then I will build features, select a classification model in Python, and validate its quality. I have extensive experience with data, automation, and optimization of processing — so I will provide not only the model but also a reproducible pipeline. I am ready to discuss the details of the task.

Budget: 1200 UAH Deadline: 7 days

Good day, Kostyantin!

I have analytical skills and experience that will help your business grow through marketing. I understand that you plan to build a customer classification model based on data from Mongo/SQL. I can assist in creating features and an effective model in Python for segmenting customers into behavioral groups, which will allow for a better understanding of the audience and adapting marketing strategies.

I conducted a preliminary analysis of your project and have a few ideas regarding feature building and the model. I can send detailed proposals in private messages. I also see potential in using AI to gain valuable insights from your data.

Before starting, I will conduct an audit of the existing customer data to understand its structure and quality. Based on this analysis, I will formulate a tailored feature-building strategy for the classification model.

I am ready to discuss the project details and your expectations. My profile contains examples of work and real cases that may interest you:

- 243 conversions per month: insurance services
- 178 applications in 1.5 months: legal services in London
- 263 thousand organic users in the first year of the website's operation
- From 0 to 450 organic clicks from Google per day

I suggest we move to discussing your project in private messages to review the details regarding customer data and the goal of building the classification model. This will help synchronize our actions to achieve the best results. Expand

Budget: 1500 UAH Deadline: 3 days

Hello, Kostiantyn! I will do it turnkey in Python: I will connect to Mongo/SQL, clean and gather features from your ~20k records, and train a model that will categorize clients into behavioral groups. The output will be code (pandas + scikit-learn), a reproducible pipeline, and a brief analysis of each segment so that the result can be used, not just viewed as numbers.

I work with Python analytics and automation. I am starting with a low price — I am currently gathering my first reviews here, so I am offering a good deal.

Approximately 1500 UAH and 3 days after access to the data.

One clarification regarding the essence: should the groups be found automatically based on behavior (segmentation without labels), or do you already have ready labels and need to train a classifier specifically for them? This will affect the choice of model.

I can first create a quick prototype using a sample of your data — you will see the approach before we start.

Budget: 1500 UAH Deadline: 1 day

Hello
Everything is clear, I am ready to execute. I will do it in Python: I will extract data from Mongo/SQL, build features, and train a model that will segment clients into behavioral groups. The scope is small, so I will complete it in 1 day. The output will be ready code, client segmentation, and a brief description of each group. To start, I only need access to the data. As soon as you send it, I will get started.

Budget: 12000 UAH Deadline: 10 days

Hello!

I will do it in Python: feature engineering from raw data (Mongo/SQL, ~20k records) + customer classification model into behavioral groups.

Approach briefly:
• EDA + cleaning, selection, and feature engineering from raw fields.
• If groups are already defined by the business — training a classifier (gradient boosting / logistic regression) with validation; if not — first clustering to identify behavioral segments.
• Quality metrics, interpretation (which features define the group), reproducible pipeline + launch instructions.

I will provide readable code (Python, with comments) + a short report on the segments.
Examples of work: Freelancehunt · technical approach: https://guardlabs.online

One clarification: are the groups already defined by the business or do they need to be extracted from the data? This affects the type of model.
Price: starting from 12,000 UAH, deadline 7–10 days (depending on data cleanliness). Expand

Budget: 1000 UAH Deadline: 3 days

Good evening! I did customer segmentation in Python — features in the style of RFM (recency, frequency, monetary, activity) + clustering and profiling of each group to create live segments instead of nameless numbers. I will extract data from Mongo/SQL, clean the raw records, and build the pipeline. First of all, what is approximately in those 20k records and what you need the segments for — I will select features based on that. I will complete it in 3 days.

Budget: 2000 UAH Deadline: 2 days

Hello! The task is completely clear and fits perfectly with my skill set. I have practical experience working with both SQL relational databases and NoSQL, particularly MongoDB, so I will quickly and accurately get to work.

For the project implementation, I will use a standard analytical pipeline in Python: the Pandas library for data cleaning, handling missing values, and generating behavioral features, as well as Scikit-learn for building and evaluating the classification model. I will try several algorithms, such as Random Forest or XGBoost, to choose the best accuracy.

In the end, you will receive a clean Python script with a ready model and a description of the identified behavioral groups. I am ready to discuss the details and analyze the structure of your data right now.

Budget: 1000 UAH Deadline: 1 day

Good day. I have experience in data development and classification using various machine learning models. Feel free to reach out, and we can discuss. I would be happy to help.

Budget: 2000 UAH Deadline: 2 days

Good day, I have been working in web programming for over 9 years. I work with REST APIs, frameworks, and CMS such as Django, Laravel, Yii2, WP, OpenCart, CodeIgniter, etc. I am ready to complete the task. Reviews: Freelancehunt

Budget: 3000 UAH Deadline: 7 days

Good day! I am ready to write a classifier in Python, I can use both paid and local models if there are resources for deployment.

It would be preferable to see what the data looks like and what I will be working with.

Budget: 3000 UAH Deadline: 2 days

I am Nina, the manager of the backend development team.

Konstantin, hello! The task of processing raw data and translating it into behavioral groups is completely clear. With a volume of 20,000 records from MongoDB/SQL, we will build a clean, reproducible pipeline in Python.
We will write all the ML logic in 2 days.

What will be done:

Extraction and cleaning: We will connect to your database (Mongo/SQL), pull the raw logs, and clean them of missing values, duplicates, and anomalies using pandas.

Feature Engineering: Based on the raw actions of clients, we will design strong behavioral features (frequency, activity, volumes, recency of interaction).
Modeling and validation: If you have a ready-made group labeling - we will train a classifier (XGBoost/LightGBM) with accuracy assessment. If there are no labels - we will go through clustering (KMeans/DBSCAN) to find the optimal number of segments.

Conditions: 3000 UAH for a ready and documented turnkey pipeline through Safe. Deadline - 2 days. We will deliver clean code (notebook or scripts) in a Docker container.

Konstantin, please let us know the main thing: are the behavioral groups already defined and labeled in the database, or do they need to be identified automatically based on clustering? We are waiting in private messages, ready to start. Expand

Budget: 1200 UAH Deadline: 2 days

Hello! I am ready to implement the client classification project in Python. The task is completely clear, and I have experience with data analysis and ML models.

How I will complete the work:

Export and cleaning: I will connect to your database (Mongo/SQL) or process the ready export, cleaning the raw data from missing values and anomalies using pandas.

Feature Engineering: I will create key behavioral metrics based on the available data (action frequency, activity, checks, etc.).

Modeling: I will build a classification/clustering model (using scikit-learn) that will clearly separate 20,000 clients into logical behavioral groups.

Report: I will provide a ready script with comments and a clear visualization (distribution graphs of groups).

Since I am building my rating on the exchange, I will do the work for a very reasonable budget and as quickly as possible. I work through Safe. I am ready to start the discussion right now! Expand

Budget: 700 UAH Deadline: 1 day

Hello. The task of classifying clients using Python and data from Mongo/SQL is familiar to me. I have experience in processing raw data and creating features for subsequent analysis, as well as implementing logic for user grouping. I would be interested to know what the ultimate goal of this segmentation into behavioral groups is and how they will be applied. I am ready to discuss the details and suggest approaches.

Budget: 4500 UAH Deadline: 3 days

Hello, Konstantin!

The task is very clear. Working with raw data, designing quality features (feature engineering), and classification is a standard and favorite pipeline. 20,000 records is a great volume to build an accurate and stable model without overfitting.

Here’s how we will tackle this task:

Step 1 (Cleaning): We will retrieve the raw data from your database (Mongo/SQL), clean it from anomalies, missing values, and duplicates.

Step 2 (Features): We will create strong behavioral features that clearly separate clients into target groups.

Step 3 (Model): We will select the optimal classification algorithm (Scikit-learn / XGBoost / LightGBM) and conduct cross-validation to ensure the metrics are real and not just "pretty" on paper.

Step 4 (Final): We will format everything as a clean Python script. If needed, we will wrap it in Docker for quick one-click deployment in your environment.

Why everything will be clear:
I specialize in Python and building resilient data processing systems. I write clean, structured code that is easy to maintain later.

If you are ready to discuss the details and data structure, I look forward to your private messages. Let's get started right away! Expand

Budget: 27000 UAH Deadline: 14 days

👋 Hello. My portfolio - Freelancehunt

I work with Python and machine learning tasks, specifically with classification and segmentation of clients based on raw data from SQL/Mongo.

💼 I can take your ~20,000 records, perform complete data preparation, then create features based on customer behavioral patterns and build a classification model into groups.

⏰ Deadline - 2 weeks.
💵 Budget - $1000.

🚀 I organize work well and take responsibility for deadlines and preserving your nerves.

Budget: 27000 UAH Deadline: 14 days

👋 Hello, the largest and most interesting projects — Freelancehunt

I understood the task: there are ~20,000 client records in MongoDB/SQL with raw data. It is necessary to build features and a classification model for clients into behavioral groups (RFM, clustering, etc.).
What I will do:

- Data loading and cleaning
- EDA (exploratory data analysis)
- Feature Engineering (creating useful features: purchase frequency, average check, recency, behavioral metrics, etc.)
- Clustering/classification of clients (KMeans, DBSCAN, Hierarchical or supervised, if there is a target)
- Visualization of segments
- Preparing the model for further use (pickle / ONNX / API)

Stack: Python (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, PyMongo / SQLAlchemy)
Cost: $1000
Deadline: 2 weeks
Deliverables:

- Jupyter Notebook with the complete process
- Clean Python code
- Description of segments and recommendations for working with them

Ready to start immediately after access to the data is provided and the desired groups are clarified.
What do you think of this proposal? Expand

Budget: 25000 UAH Deadline: 14 days

Good day, I have over 5 years of experience in Python and data work, and I can easily implement feature construction and classification models based on your data from Mongo/SQL. I will choose the optimal approach - from dataset preparation and feature engineering to training, validation, and a final solution ready for integration. I look forward to your message in private!

Budget: 5000 UAH Deadline: 5 days

Good day!

I professionally develop solutions for data analysis and machine learning models in Python. I am ready to implement a complete ML pipeline for you: from extracting raw data from MongoDB/SQL and building informative features (Feature Engineering) to training an accurate classification (or clustering) model of clients into behavioral groups (pandas, scikit-learn, XGBoost/LightGBM) + structuring all the code for convenient future maintenance.

Message me privately, and we will clarify the details.

Budget: 900 UAH Deadline: 7 days

Hello!

I have over 6 years of experience in data analytics. I work with Python, SQL, and machine learning. I can help with data preparation from Mongo/SQL, feature engineering, and creating a customer classification model based on behavioral groups.

I am ready to review the data and discuss the project details. Feel free to reach out!

Budget: 1300 UAH Deadline: 5 days

Good day! I am interested in your project. I have a strong background in Python and working with databases, and I am ready to take on the analysis and building of a customer classification model. Here is how I propose to structure the work: Data Preparation -> Feature Engineering -> Modeling (Behavioral Grouping). All stages will be implemented in Python (I will use Pandas, Scikit-learn). I would be happy to take a look at the structure of your data to better estimate the timeline.

Budget: 8000 UAH Deadline: 3 days

Hello, I am ready to implement a solution in the Python programming language for classifying clients into various behavioral groups. If you are interested in a quality execution of the project, please message me directly.

Budget: 27000 UAH Deadline: 10 days

We already have a practically ready solution for AI segmentation and classification of clients in Python, which can be quickly adapted to your data and put into operation. ))

We can complete the stage in 10 days for $1600 - connecting to Mongo or SQL, cleaning, feature building, model training, quality assessment, a notebook or script, and a brief report on client groups.

Here’s the nuance - if you already have target labels for the groups, then this will be classification; if there are no labels, it’s better to go through clustering and then describe the behavioral groups.

We need to clarify two things - whether there are already correct groups for training and what output is needed - a model as a file, API, report, or integration into your system.

Relevant examples from Ingello:
- https://business.ingello.com/vorfahr - AI and SaaS logic with data processing and automation
- https://business.ingello.com/fractal - automation of complex workflows and agent scenarios
- https://business.ingello.com/forma-crm - working with client data, CRM, and business logic

FLH landing page Ingello - https://systems-fl.ingello.com/ua

We can discuss this here in the project; I am available. Expand

Budget: 5000 UAH Deadline: 3 days

I understand the task: there are about 20,000 client records in Mongo or SQL with raw data, and based on that, features and a classification model of clients into behavioral groups need to be built, all in Python.

Before taking on the model, the key question is whether the task has predefined group labels or if the groups need to be extracted from the data. If the behavioral groups are not predefined, this is more about clustering plus segment interpretation rather than classical classification. If labels are available, then it’s a supervised model. This will determine the entire pipeline, so this is the first thing to clarify.

General plan: data extraction and cleaning from Mongo or SQL, feature construction from raw fields (behavior aggregates, frequency, recency, sums, etc.), then the model. For segmentation, clustering with the selection of the number of groups; for classification, gradient boosting or a simpler baseline model as a starting point. The output will be a reproducible Python pipeline, quality assessment, and a brief description of what each group means.

I work with Python and data constantly; processing, features, and models using pandas and scikit-learn are my working tools.

Please advise: are the client groups already defined and labeled, or do they need to be extracted from the data? And is it better to take the data from Mongo or from SQL?

Budget: 8000 UAH Deadline: 7 days

Good day!

I will do it in Python: I will extract data from Mongo/SQL, build features based on raw data, and train a model to segment clients into behavioral groups.

One important point in essence (which affects the approach): if groups are not predefined and there are no labels — this is clustering (unsupervised: segmentation through k-means / hierarchical, with selection of the number of groups and interpretation of what these segments are). If groups/labels are already defined — then it is classification (supervised, with accuracy assessment). I can do both options, it's just that they are different models — let's clarify what you have.

Regarding the process: data exploration and cleaning (~20k records) → feature engineering → model training → quality assessment → interpretation of segments (so that groups are not just numbers, but understandable behavioral types that can be used). Stack — Python (pandas, scikit-learn).

Please let me know: are the client groups already defined (are there labels) or do they need to be discovered from the data? And what raw fields are available (to understand what features to build). We can gladly discuss the details in private.

Budget: 1500 UAH Deadline: 1 day

Good day, I can complete your task today. Write to me =)

Budget: 26990 UAH Deadline: 29 days

Good day! My name is Valentin, and I represent Arctic Web Agency. We are a team that specializes in creating modern and effective solutions for businesses. I can provide examples of our similar work in personal messages. We are ready to take your project to work!

Sincerely,
Arctic Web Team
Freelancehunt

Budget: 700 UAH Deadline: 1 day

Hello!

I am ready to start working on the Python project.

I can assist with the full cycle of work:

• connecting to MongoDB / SQL and analyzing raw data;
• cleaning and preparing data;
• building features based on customer behavior;
• segmentation / classification of customers into behavioral groups;
• training and testing the model;
• evaluating the model's quality;
• preparing results in a convenient format;
• if needed — documentation and explanation of the logic.

I am ready to discuss the data structure and start working immediately.

Please let me know:

• What specific data is in the database: purchases, activity, payments, applications, events?
• Are the behavioral groups already defined, or do they need to be formed based on the data?
• Is classification with predefined labels needed, or clustering of customers?
• In what format do you expect the result: Python script, Jupyter Notebook, API, or report?
• Is there access to the database or a test data dump?

I look forward to collaborating. Expand

Budget: 4500 UAH Deadline: 1 day

Hello. I have been working with Python for over 9+ years. I am ready for collaboration. Feel free to contact me.

Budget: 6000 UAH Deadline: 5 days

Hello!

The task is clear. I have experience with Python, pandas, numpy, sklearn, and working with MongoDB/SQL.

What I will do:
- Analyze raw data and clean it
- Build relevant features from behavioral patterns
- Create a classification/clustering model for behavioral groups
- Validate results and describe groups

Before providing an accurate estimate, I would like to clarify: what is the data structure in the database, which attributes are significant for classification, and what do behavioral groups mean for your business.

I am ready for discussion. Expand

Budget: 7000 UAH Deadline: 1 day

Hello!

The task is clear — behavioral segmentation of customers based on 20,000 records from MongoDB/SQL using Python.

I see it like this: EDA and data cleaning, feature engineering to extract behavioral features, clustering to find natural groups, and a classifier that assigns a new customer to the appropriate group. The output will be code, a report, and visualization of the clusters.

We can discuss the details regarding timelines and pricing privately — I want to first clarify what fields are in the data and whether there are already defined behavioral groups or if they need to be found independently.

I hope for collaboration!

Budget: 10000 UAH Deadline: 5 days

Hello, Kostyantyn!

As I understand from the description, you need to take about 20,000 records from MongoDB/SQL, prepare features (feature engineering), and build a customer classification model based on behavioral groups in Python.

In a similar project for a WMS platform, I designed the API and integration layer from scratch, where a lot of attention was paid to preparing and normalizing data from various sources.

I also worked with high-load analytical systems using Go, ClickHouse, and MySQL, where data quality and the construction of effective features directly impacted the results.

I have a few clarifications:

a) Are the behavioral groups already labeled (supervised classification), or do they need to be defined first using clustering, and then train the classifier?
b) What specific fields do the records contain (transactions, activity, demographics, interaction history, etc.)?

I would start with data quality analysis, feature construction, a baseline model, and comparing several algorithms. A non-obvious risk is if the classes were formed manually or are heavily imbalanced; separate work on imbalance and validation will be needed, otherwise the metrics may be misleading.

Estimated budget: 10,000–25,000 UAH, timeframe 5–10 days. The final price will be determined after clarifications regarding the data structure, classification criteria, and expected results.

I suggest working through a Secure Agreement (Safe). I would be happy to discuss the project details. Expand

Budget: 10000 UAH Deadline: 10 days

Hello!

I have extensive experience in Python development.
I am ready to complete the assigned task.

I suggest we discuss the details in private messages.

Budget: 9999 UAH Deadline: 7 days

Good day. I have experience working with tabular data in Data Science. If you can, please send a sample of the data, and I will see what can be done with it.

Budget: 6500 UAH Deadline: 5 days

Good day!

I have experience in Python development, working with SQL, and data processing.

I can perform the full cycle: obtaining data from MongoDB/SQL, preparing and cleaning data, building behavioral features, and models for customer segmentation/classification.

For a more accurate assessment, I would like to clarify whether there are already defined customer groups for training the model, or if they need to be determined automatically based on the data.

I would be happy to discuss the project details.

Budget: 1300 UAH Deadline: 1 day

Hello! I am interested in your project. I am a Python developer focused on AI solutions and data work, ready to take on the full cycle of building a classification model (from raw data preparation to model deployment). I have strong experience in writing architecture in Python, integrating databases, and creating intelligent services. I have commercial experience with replacing first-line managers with AI agents, and client qualification was one of the conditions - so this is within my expertise.

Budget: 1500 UAH Deadline: 1 day

Good day! I will complete this task quickly, and most importantly, with quality in 1 day!!!! Feel free to contact me!!!!

Budget: 1200 UAH Deadline: 3 days

Hello.

I can create the first working version of an ML pipeline in Python for customer segmentation based on behavioral characteristics.

A key clarification at the start: are there already prepared group labels? If so, we will perform supervised classification with a train/test split and quality metrics. If there are no labels, we will build segmentation through clustering, selecting the number of groups and describing the logic of each segment.

In the work process:
- data extraction from MongoDB / SQL or from exports
- cleaning raw records, handling missing values, anomalies
- feature engineering: activity, frequency, recency, volumes, RFM-like features
- baseline model using scikit-learn
- quality assessment, saving the model and labeled dataset
- a brief report to clarify what each customer group means

Before starting the work, I need to clarify:
- what is the data structure in MongoDB / SQL
- are there predefined customer classes
- what result is needed: file, model, script, or writing groups back to the database
- how these groups will be used further

We can discuss the details in private messages. Expand

Budget: 5000 UAH Deadline: 4 days

Hello! I have worked on similar tasks in Python. Please clarify if these are unknown groups; do we need to find if there are existing group labels? This will determine the approach.

Budget: 1500 UAH Deadline: 1 day

Good day!

The task is clear — building behavioral segmentation of clients based on raw data.

What I will do:

Connecting to MongoDB/SQL, extracting and auditing 20k records
Data cleaning: anomalies, missing values, normalization
Feature Engineering: from behavioral logs — activity metrics, frequency, volumes, RFM features
Model: clustering (K-Means + DBSCAN) to identify behavioral groups, selecting the optimal number of clusters (elbow method + silhouette score)
Result: labeled dataset, saved model, report describing each client group and recommendations
Stack: Python, pandas, scikit-learn, pymongo

Clarify: are the data already cleaned or completely raw? Is there a target label (supervised) or do I need to find groups independently (unsupervised)? Expand

Budget: 7000 UAH Deadline: 7 days

Hello! The task of building a behavioral model of clients is completely clear to me. I am studying in the field of "Artificial Intelligence Systems," so developing the architecture of ML models and Feature Engineering is my professional stack.

Here’s how I propose to implement the project in Python:
Data Prep: We will extract raw data from your database (SQL/Mongo), perform cleaning from anomalies, and fill in missing values.
Feature Engineering: Based on behavioral logs, we will generate a dense feature matrix (activity metrics, frequency, and volumes of interaction).
Modeling: We will build and train the optimal model (classification/clustering using scikit-learn), tune hyperparameters, and derive metrics for evaluating the quality of the model.
I would be happy to discuss the specifics of the raw data in private messages!

Budget: 1000 UAH Deadline: 1 day

Hello!

Based on your 20,000 raw records from Mongo/SQL, I will develop an optimized pipeline for data cleaning and generating behavioral features in Python. I will implement an accurate model that segments customers into groups and updates the status of each user in the database as needed. You will receive a ready-to-use documented script that automatically loads raw data, forms a feature matrix, and predicts classes. Could you let me know if you already have labeled classes for training the model, or is the task to find hidden segments through clustering? Please write to me privately — we will discuss the database structure and get started.