Python script for personal recommendations
137 USD Need a Python developer to create a script that:
accepts a CSV with online store orders (1000–30,000 rows);
groups products by orders;
calculates product correlations (Support / Confidence / Lift);
identifies seasonal bestsellers (by order date);
generates a personal product recommendation for each customer (1 customer → 1 product),
using:first the best correlation pairs (by lift),
if there is no suitable rule — seasonal bestseller;
exports the final CSV in the format: Customer ID → Recommended Product → Reason.
Implementation method (Apriori, FP-Growth, or your own solution) — at the discretion of the performer.
No visualizations, interfaces, or ML needed — just working logic + final CSV.
Deadline — up to 5 days.
Detailed task description in the attached file.
Applications 1
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3 days137 USD3 days137 USD
Hi Mark,
I can deliver this in 2-3 days for 500 PLN.
I'll build a clean Python script using pandas for data processing and mlxtend for efficient association rule mining (Apriori/FP-Growth). The logic will be straightforward:
Parse your CSV and group transactions
Calculate Support/Confidence/Lift for product pairs
Extract seasonal patterns from order dates
Generate one recommendation per customer using correlation rules (prioritized by lift), falling back to seasonal bestsellers
Export with clear reasoning for each recommendation
…
I've handled similar e-commerce analytics projects and regularly work with large CSV datasets. The output will be production-ready code with proper memory optimization for your 30k row files.
One quick question: do you have a sample CSV structure I can reference, or should I work with the format described in the attached doc?
Looking forward to working on this
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3 days137 USD3 days137 USD
Greetings!
I am ready to implement a cart analysis script and generate personalized recommendations.
My experience with the task:
Working with large CSV files (pandas, memory optimization for 30k+ rows)
Implementation of association rule algorithms (Apriori/FP-Growth) for analyzing product correlations
Calculation of metrics Support, Confidence, Lift to identify relationships between products
Segmentation of data by time periods (seasonality, trends)
… What I will offer:
An efficient algorithm for grouping orders and calculating correlations
The logic for personalized recommendations: prioritizing correlation pairs (by lift) → fallback to seasonal bestsellers
A clean final CSV with fields Customer ID, Recommended Product, Reason
Readable code with comments for possible further development
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4 days137 USD4 days137 USD
Looking for a solution to create personalized recommendations quickly and accurately? I offer a powerful Python script that will turn your CSV into a useful analytics tool. My specialization in optimizing complex data and knowledge of approaches such as Apriori and FP-Growth ensure the accuracy of calculations for product correlations and seasonal bestsellers. With experience working on e-commerce projects, I am ready to group your orders and create value recommendations for each customer. Shall we discuss how this script will become an asset for your business?
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5 days137 USD
272 5 days137 USDHello!
I am ready to implement a Python script for generating personalized product recommendations.
Technical approach:
Algorithm: Apriori (mlxtend library)
Data processing: pandas
Metrics: calculation of Support, Confidence, Lift for product pairs
… Seasonal analytics: grouping by dates with identification of top sales
Recommendation logic: prioritizing correlations (maximum lift), in absence — seasonal bestseller
What you will receive:
Python script (.py) with comments
Final CSV: Customer ID → Recommended Product → Reason
Brief instruction for running (README)
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4 days137 USD
242 4 days137 USDGood day!
I am ready to complete the task of building personalized upsell recommendations in Python according to your technical specifications.
Correlation / recommendation method
- I will load the CSV using pandas, grouping items by Order ID.
- For all products, I will calculate:
• total number of purchases,
• number of joint purchases of product pairs,
… • metrics support, confidence, lift for rules of the form A→B.
- This is a classic approach to market basket analysis / association rules (essentially Apriori for product pairs, without longer combinations, as specified in the technical specifications).
- Based on the order dates, I will determine the season (winter/spring/summer/autumn) and calculate the top products for each season.
- For each Customer ID:
• I will look at all the products they have purchased,
• I will select the rule A→B with the maximum lift,
• if there are no suitable rules, I will substitute the seasonal bestseller for the current season.
- The output will be a CSV of the form Customer ID / Recommended Product / Reason (correlation / seasonal) with a 1-to-1 recommendation for the customer.
Deadlines and cost
- Completion time: 3–4 days from the moment the test CSV is provided.
- Cost: $250
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1 day137 USD
267 1 day137 USDHello!
I am ready to complete your project to create a Python script for personal recommendations within 1 days.
I have experience working with large CSV files (up to 30,000+ rows), building associative rules (Apriori / FP-Growth via mlxtend or optimized implementation), and calculating Support, Confidence, and Lift metrics. I have also implemented seasonal segmentation logic based on order dates.
I propose the following approach:
Parsing and normalizing the source CSV with grouping of products by orders;
Calculating associations between products with ranking by Lift;
… Identifying seasonal bestsellers (by month/quarter — I will clarify according to your date format);
Forming one recommendation per customer: first — based on strong correlations, if there are none — seasonal bestseller;
Exporting the final CSV in the format: Customer ID → Recommended Product → Reason.
The code will be readable, with comments and launch instructions.
Translated with DeepL.com (free version)
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3 days137 USD
316 1 0 3 days137 USDHello, I have extensive experience in web development using Python! I am the one you need, feel free to contact me!
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5 days137 USD
1345 4 0 5 days137 USDHello!
I am ready to create a Python script for you that will process a CSV with orders, calculate product associations, and generate personalized recommendations for clients in the required format.
Here is how I see the solution to the task:
Importing orders from CSV (1,000–30,000 rows), normalizing data.
Grouping items by orders (basket view).
… Calculating product associations: support / confidence / lift for products within a single order (Apriori / FP-Growth or my own solution — I will choose the optimal option based on speed).
Identifying seasonal bestsellers based on the order date (month / quarter or specified period).
Generating one recommendation for the client:
first based on the best rules with maximum lift;
if there are no rules — a seasonal bestseller is provided.
Exporting the final CSV format:
Customer ID → Recommended Product → Reason (rule / seasonal bestseller).
I work with Python, pandas, processing large CSV files, and associative rules (Apriori/FP-Growth). I will include concise code, comments, and a brief description of how to run the script in the result.
The task in terms of complexity and volume fits well within the specified timeframe (up to 5 days). I am ready to clarify details regarding the structure of the original CSV and the format of the fields before starting.
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1 day137 USD
586 5 0 1 day137 USDI am ready to immediately start developing this Python script. My experience in data analysis and the development of association rule algorithms (Apriori/FP-Growth) will allow me to create an effective solution in 5 days that will automatically process CSV files with orders (up to 30,000 rows), identify product correlations, determine seasonal bestsellers, and generate personalized recommendations for each customer, exporting the result in the required CSV format.
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7 days137 USD
991 12 1 7 days137 USDI am ready to promptly and qualitatively complete your order. I have experience working with similar projects, always adhere to deadlines and technical specifications. I will be happy to collaborate!
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2 days151 USD
540 1 0 2 days151 USDReady to take on the implementation of your project to create a Python script for generating personalized upsell recommendations.
What will be done:
Processing CSV files with up to 30,000 rows.
Grouping products by orders, building product pairs, and calculating:
Support
…
Confidence
Lift
Forming a list of A→B rules, sorted by lift metric.
Seasonal analysis to identify top products for each season.
Generating a personal recommendation for each client (1-to-1 mapping).
Forming the final CSV in the appropriate format.
Methodology:
For calculating associative rules, I plan to use Apriori or my own optimized implementation tailored to your data volumes (to be specified depending on the structure in the CSV example).
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3 days137 USD
9351 20 0 1 3 days137 USDI will create a Python script for you that reads a CSV with orders, groups products by orders, and calculates correlations, all according to the technical specifications. I will be happy to collaborate.
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2 days137 USD
12862 4 2 2 days137 USDHi Mark,
I can make your script in 1~2 days using Python.
Best,
Jeo
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5 days137 USD
1117 4 0 5 days137 USDHello!
I can prepare a script in Python that will read your orders from a CSV file, calculate correlations between products, and generate a personalized recommendation for each customer. I plan to use association rules based on lift to highlight the strongest connections between products, and in the absence of a clear correlation, combine this with seasonal bestsellers, so that each customer receives a valuable suggestion. I also have a small idea to label each recommendation with the season it pertains to, which will provide additional insight into the temporal context and preferences. I just want to make sure that all dates in your CSV are in a uniform format YYYY-MM-DD.
Thank you!
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3 days137 USD
1334 6 0 3 days137 USDHello!!!
I have extensive experience in Python, I will use the Apriori method.
Feel free to contact me.
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3 days137 USD
656 9 0 3 days137 USDGood day, Mark!
In general, the task is clear, but for an accurate response regarding deadlines and price, I would like to clarify some questions that arose after analyzing your task.
Please write in private messages — we will discuss the details and your wishes.
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5 days137 USD
6195 53 4 5 days137 USDGood day. I have extensive experience in writing scripts in Python for working with CSV files. I am ready to write a script to solve your task after discussing the details.
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5 days135 USD
117 5 days135 USDHello.
I will do it with quality and on time.
I think there will be no problems.
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5 days137 USD
6425 74 1 5 days137 USDHello. I have extensive experience with similar tasks. I have reviewed the technical specifications. I need an example of the input data.
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4 days165 USD
3276 28 0 4 days165 USDGood day, Mark! I have checked the technical assignment in the document and am ready to start today. The proposal states the price and maximum deadline, but I will try to meet a shorter deadline. I will need 2-3 examples of how the option is selected based on correlation and season. I will be waiting for your message.
Thank you!
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5 days137 USD
5659 12 0 5 days137 USDHello, I have 7 years of experience in Python development. I will complete your project as quickly and efficiently as possible. I have experience working with CSV as well as with FP-Growth. I am ready to start working right now.
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5 days137 USD
4227 53 1 1 5 days137 USDHello Mark, ready to write such a script for you
Write, we will discuss
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1 day137 USD
1595 7 0 1 day137 USDMy name is Roman, and I am among the top 5 developers in the category of "Artificial Intelligence and Machine Learning" among ~1600 specialists on the platform.
I guarantee:
- Fast and quality execution of the task
- Clear adherence to deadlines
- Regular communication throughout the entire process
I would be happy to discuss the details of your project in private messages.
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5 days137 USD
2161 4 2 5 days137 USD👋 Welcome!
We are Spectrium LLP — a team from the United Kingdom that helps businesses launch effective digital products.
📈 For your task, we offer:
• Processing CSV with orders (1k–30k rows) and grouping products by orders;
• Calculating product correlations (Support / Confidence / Lift) and selecting the best rules;
• Identifying seasonal bestsellers by order date;
• Generating personal recommendations "1 customer → 1 product" with explanation (Reason);
• Exporting the finished CSV with Customer ID → Recommended Product → Reason.
…
✅ The script will be optimized, functional, and ready for integration into your business processes, with the ability to customize rule and seasonality parameters.
🙌 Our work and project details can be viewed in the presentation:
Google Drive
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