Telecom Descriptive Analytics Platform

@neerajdhameliya

Hello, Your telecom analytics platform is a strong match for my experience with AI-driven data pipelines, Python analytics, React dashboards, and PostgreSQL. I can ingest network metrics and ticket/chat data, engineer descriptive features, and surface incident frequency, duration, recurring errors, anomalies, geography, and device impact. I’ll also deliver a lightweight dashboard with drill-downs, optimized for 15-minute refreshes and <2-second response times. Recently, I built an AI-powered lead scoring workflow using n8n, Python, Close CRM, Google Sheets, and external APIs to turn messy data into actionable insights. Looking forward to your reply so we can jump on a quick call and get started! Best, Niral

@ShaikhAneesa

Hi there, I understand you need a production-ready descriptive analytics layer that combines high-volume network performance metrics with historical customer service logs, turning raw telecom data into clear evidence of what happened, where it happened, and how widespread each issue was. I’m confident I can build the pipeline, analytical layer, and dashboard as one cohesive system rather than treating them as separate components. My approach is to first map the network counters and ticket/chat data, define the required dimensions such as incident frequency, duration, geography, device type, and error category, then design a query-efficient ingestion and storage layer. Next, I’ll implement the Python/pandas analytical pipeline with statistical and ML techniques where they add value, including anomaly and trend analysis, while keeping the results explainable. I’ll then build the dashboard in Grafana/Kibana or React with daily, weekly, and monthly trends, recurring issues, and drill-down access to underlying records. Finally, I’ll optimize the pipeline to meet the 15-minute refresh target and dashboard performance requirements, add unit tests, deliver the code through Git with clear documentation, and provide a practical handover session for your team. Are the network metrics currently available through Kafka/streaming APIs, or are the hourly batches delivered through files, databases, or another source? I’m ready to start immediately. Warm Regards, Aneesa.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@denisSolution

Hi, This sounds like a classic telecom ops analytics setup—raw network counters and support logs need reliable parsing, enrichment, and fast reads to spot issues before customers complain. The hourly refresh and sub-second dashboard response are the real constraints here. I’ve built similar pipelines before, where high-volume time-series data from devices had to merge cleanly with ticket text to produce rolling incident summaries. Usually the trick is keeping the joins lightweight and the storage columnar for speed. For this I’d lean on Spark Structured Streaming for the ingestion side—handles backpressure better than plain Kafka consumers—and store the cleaned data in a Parquet-backed warehouse. Feature tables would be pre-aggregated at hourly/daily grains so dashboards never hit raw fact tables. Grafana works fine for the UI layer as long as the time-series query layer is optimized; I’d keep the React option only if you need heavy custom filtering. The tricky part is always the text side—support logs are messy, full of shorthand codes and irrelevant chatter. A simple rule-based parser plus TF-IDF for issue categorization usually beats deep learning here unless you have labeled examples. If the data volumes or schema drift go beyond what Spark tuning can handle, we might need a lightweight Flink job for stateful error detection. But I’d start lean and see. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@mubeenm5026

Within Web Crest, you've found an experienced team with the skills necessary to build your ideal Telecom Descriptive Analytics platform. Understanding the importance of data and its effect on decision-making, we have honed our abilities particularly in stream processing and refined feature engineering. Your need for efficient refreshes aligns seamlessly with our capability to ensure a 15-minute completion for each hourly batch—a promptness guaranteed by our expertise in Git, structuring your data pipeline. Our proficiency in Python, pandas, scikit-learn will prove valuable in transforming your raw data into insightful patterns that increase understanding of service interruptions. We appreciate a quick and purposeful interface is vital to glean actionable insights from real-time network performance metrics and historical customer logs- another reason to choose us! Whether it's using Grafana, Kibana or React-based front end, we'll deliver a dashboard that visualizes daily, weekly/monthly error trend and facilitates drill-down access down to log entries proficiently. Lastly, as you desire future-proof solutions robust enough for your in-house team’s easy extension of the system, you can trust us to deliver. We believe in comprehensive knowledge transfer and will provide thorough documentation along with a concise hand-off session ensuring all boxes ticked and your expectations surpassed.

@corpmember29

HELLO, I HAVE CAREFULLY REVIEWED YOUR REQUIREMENTS FOR THE TELECOM DESCRIPTIVE ANALYTICS PLATFORM. I HAVE 10+ YEARS OF EXPERIENCE IN DATA ENGINEERING, PYTHON, AI/ML, DATA PIPELINES, REAL-TIME PROCESSING, API DEVELOPMENT, AND ANALYTICS DASHBOARDS. I CAN BUILD A SECURE, SCALABLE PIPELINE FOR HIGH-VOLUME NETWORK METRICS AND CUSTOMER SERVICE DATA, WITH DESCRIPTIVE ANALYTICS FOCUSED ON INCIDENTS, ERROR TRENDS, FREQUENCY, DURATION, AND AFFECTED SEGMENTS. WORKING FLOW → Data Ingestion → Cleaning & Normalization → Feature Engineering → Statistical/ML Analysis → Query-Optimized Storage → Grafana/Kibana/React Dashboard → Drill-Down & Reporting → Testing & Optimization. I WILL TARGET THE 15-MINUTE DATA REFRESH AND SUB-2-SECOND DASHBOARD REQUIREMENTS, WITH PROPER UNIT TESTS, GIT-BASED DELIVERY, DOCUMENTATION, AND HAND-OFF SUPPORT. I AM COMFORTABLE WITH PYTHON, PANDAS, SCIKIT-LEARN, KAFKA, SPARK/FLINK, SQL, AND REAL-TIME ANALYTICS SYSTEMS. I WILL FOLLOW AGILE METHODOLOGY, PROVIDE COMPLETE SOURCE CODE, 2 YEARS OF FREE ONGOING SUPPORT, AND ASSIST YOUR TEAM FROM INITIAL DEVELOPMENT THROUGH HANDOVER. I EAGERLY AWAIT YOUR POSITIVE RESPONSE. THANKS

@belarjsoufiane20

Hi, I can help build the complete analytics platform, from data ingestion and cleaning to statistical analysis and dashboard development. I have a 10+ years of experience in statistics and data analytics, with strong expertise in Python, pandas, scikit-learn, SQL, feature engineering, anomaly detection, and dashboards. I can handle the network metrics and customer-service data, identify error trends, recurring issues, incidents, affected geographies/devices, and build a lightweight dashboard with drill-down capabilities. I’ll also deliver a clean Git repository with documentation, unit tests, and a clear hand-off for your team. I’d be happy to discuss the data sources and architecture with you.

@topcreator100

Hello, With a strong background in building scalable applications, I am keen to bring your Telecom Descriptive Analytics Platform to life. Your detailed project requirements resonate with my expertise in data processing, statistical modeling, and building analytical UIs. I work extensively with Python, pandas, scikit-learn for feature engineering and statistical analysis - ensuring that I can design, implement and maintain a robust data pipeline that delivers timely insights for your teams. Furthermore, I have experience working with high-volume data ingestion using technologies like Kafka, Spark or Flink which can streamline your data pipeline process efficiently while maximizing its effectiveness. My use of modern tools such as Grafana or Kibana ensures slick dailies analyses and in-depth reporting across various dimensions for effective troubleshooting. Another strength I possess is translating business requirements into scalable technical solutions. This will come in handy while supporting your team with documentation, a clear handoff session for knowledge transfer, and extending the solution as per evolving requirements - all conducted effectively through the use of Git. Rest assured my deliveries are tested via unit tests and benchmarked for latency to ensure agility without comprising the quality Thanks!

@Hubready

Hello!, This is James from Hollywood... I read your Telecom Descriptive Analytics Platform description carefully, and I understand the goal is to help telecom vendors clearly see what happened, why it happened, and what it means in a way that is practical and decision-ready. I’ve spent about 15 years working with Python, statistics, ML, Spark, data analysis, visualization, and production data systems, so this is exactly the kind of work I like. I’m used to turning messy operational data into clear insights and dashboards that teams can actually trust and use. My approach would be: 1. Review the data sources and define the key event patterns, KPIs, and business questions 2. Build the analysis pipeline in Python/Spark with proper validation and statistical checks 3. Create clear visual outputs in Kibana or a dashboard layer so the story is easy to understand 4. Refine everything based on edge cases and real telecom scenarios Could you please clarify the following questions to help me better understand the project? 1. What telecom data sources will I be working with, and are they raw or already cleaned? 2. Do you want only descriptive analytics, or should I also include basic anomaly detection or prediction? 3. What is the preferred final output format: dashboard, API, reports, or all three? If helpful, I can share relevant examples of analytics and dashboard work I’ve done.

@maxscender

I can help you. I will build a complete descriptive analytics solution that transforms your raw network and log data into clear, actionable insights for your engineering and account teams. Here is how I will approach it: 1. Data Pipeline & Storage I will design a robust ingestion pipeline using Kafka for real-time data flow. The architecture will normalize performance counters and chat transcripts into a query-friendly schema in a columnar store (like ClickHouse or TimescaleDB), ensuring your 15-minute refresh target is consistently met. 2. Modeling & Analysis I will focus on pragmatic statistical models and feature engineering to identify incident patterns. The output will classify issues by frequency, duration, and geographic/device impact. I will only implement deep learning if the data volume or complexity justifies it; otherwise, I will use faster, more interpretable methods. 3. Dashboard & Visualization I will build a lightweight, high-performance dashboard (using Kibana or a custom React app) that gives your teams instant visibility. It will provide the required daily/weekly/monthly trend views, highlight top recurring issues, and allow users to drill down from a summary chart to individual raw log entries in one click. I understand the specific requirements around data security and performance. The final system will be optimized to ensure dashboards load in under two seconds, even with high-volume data.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Statistics, Machine Learning (ML), Statistical Analysis, Git, Spark, Data Visualization, Data Analysis, Deep Learning, Kibana and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@AgnarConsulting

Hi There, I have strong experience with Python data processing, statistical analysis, machine learning workflows, and building data pipelines for structured analysis. For this project, I can build the pipeline to ingest high-volume network performance metrics together with historical customer service logs, clean and normalize the data, and store it in a query-friendly format. The analytics layer can then summarize incident frequency, duration, recurring errors, usage anomalies, and affected geographies or device types using Python, pandas, and scikit-learn where appropriate. The dashboard can present daily, weekly, and monthly error trends, recurring issues, and drill-down from aggregated metrics to the underlying log entries. I can also structure the codebase with Git, README documentation, unit tests, and the required hand-off material so your team can maintain and extend the solution. I’m happy to discuss the data sources and expected batch volume first and confirm the most suitable pipeline and analytics approach. - Rishan

@TeamGTW

Hello! Based on your project description, you are looking to build an AI driven telecom descriptive analytics platform that securely processes high volume network performance metrics alongside historical customer service logs. The goal is to turn complex operational data into clear insights around incidents, error trends, frequency, duration, affected locations, and device types, with dashboards that allow teams to quickly identify recurring issues and drill into individual records. I will focus on building a reliable data pipeline with Python and scalable processing components, structured storage, feature engineering, statistical analysis, and practical ML where it adds value. I will also create a lightweight React or Grafana based dashboard with daily, weekly, and monthly trends, drill downs, and performance aligned with your two second target. The solution will include testing, documentation, Git delivery, and a clear handover so your team can maintain it confidently. I specialize in full stack, data, and AI development with 7+ years experience and I have done similar work in past please open the chat window so I can share with you. Thank you for considering my proposal. I look forward to hearing from you soon. Best regards, Nikita Gupta.

@emilesd617

Hi, I am a data engineer with 8 years of experience in software development, with a strong background in analytics platforms, data pipelines, and machine learning workflows. I am familiar with Python, pandas, scikit-learn, Kafka, Spark, PostgreSQL, Elasticsearch, Kibana, Grafana, React, statistical analysis, anomaly detection, Git, and unit testing. I can build the ingestion pipeline for network metrics and customer service logs, normalize and store the data for fast querying, then create descriptive models for incident frequency, duration, geography, device type, and recurring issue patterns. I can also build a lightweight dashboard with fast drill-down views and design the pipeline to meet your 15-minute refresh and sub-2-second dashboard targets. I'm an individual freelancer and can work in any time zone you prefer. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@RaspberryOculus

With over two and a half decades of experience in complex system integration, I am confident that my unique skill set aligns well with your project's needs. Although my background has been largely focused on hardware, it has honed my meticulousness, attention to detail, and unwavering commitment to deliver state-of-the-art solutions that not only perform optimally but also withstand the test of time. In relation to your project, I have hands-on experience in implementing systems with large volumes of data derived from diverse sources, and then transforming them into a format that is easily queriable for analysis. My skills in Python, pandas, scikit-learn, and others allow me to engineer features in a way that can summarise incidents, highlight patterns and anomalies swiftly and accurately. I fully understand the exigency of your project so timely reporting with my robust data-refresh process is guaranteed. Additionally, my proficiency in various IoT protocols (such as ACARS/ED137) could be an add-on for your task which might involve some sort of hardware-software interfacing. The use of real-time streaming processing (Kafka/Spark/Flink) coupled with efficient data visualization techniques (Grafana/Kibana/React) draws parallels to some of the challenging undertakings that I have successfully accomplished. My adherence to best-practice documentation will facilitate your team's maintenance and extension work seamlessly.

@Tanmoy236

Hi There! I specialize in telecom data analytics, with 9+ years of experience building Python pipelines, statistical models and high-performance dashboards. Here’s how I can help: 1. Build secure pipelines for network metrics, tickets and chat transcripts. 2. Develop descriptive models for incidents, trends, anomalies and affected segments. 3. Create Grafana/Kibana dashboards with drill-downs, tests and clear documentation. Would you be open to a quick discussion about your current data sources and Kafka/Spark setup?

@mayankinnovative

With my extensive experience in PHP-based development, I come equipped with the skills to build your data pipeline for securely ingesting high-volume performance counters and transcripts while ensuring a smooth transformation for query-friendly access. My focus on clean, maintainable solutions aligns well with your need for accurate and insightful analytics. Building robust Laravel based backend applications and APIs have endowed me with strong skills in stream processing techniques like Kafka and Spark as well as data analysis tools like pandas and scikit-learn. Having worked on several projects involving WooCommerce, I understand the value of real-time data and actionable insights to improve performance. I will leverage this knowledge to design a descriptive analytic solution tailored to your exact needs and capable of rendering key-metrics within seconds to highlight recurring issues quickly. Moreover, as you envision a long-term solution, my far-reaching expertise includes not just building but maintaining such scalable solutions as well. My client testimonials are flooded with praise for my ability to understand requirements quickly and deliver reliable results - a quality I believe is essential to ensure that your system remains stable as it grows. I look forward to working hand-in-hand with your in-house team, providing extensive documentation along the way. Let's discuss how we can contribute towards bettering network understanding and thereby improving operations!

@juanestebana17

Hello!@Project@[newline]I read your project and understand that you need an AI-driven telecom analytics platform to turn raw network data into actionable insights. @Why I'm a good fit@[newline]With extensive experience integrating Python, pandas, and scikit-learn for statistical analysis and ML models, I’ve developed data pipelines and dashboards for network analytics. My expertise includes stream processing with Kafka, Spark, and building user-friendly UI in React.js, perfect for your project. I have spent several years solving complex telecom data problems, ensuring high-volume data ingestion, real-time analysis, and performance visualization. I'm ready to start working immediately. Thanks!

@Aaleen110

The real challenge on this job is handling the two distinct data sources, real-time metrics and historical logs, so they can be joined and analyzed together effectively. I would build the data pipeline using Python and Apache Spark for ingestion and processing, creating a unified schema in PostgreSQL for query-friendly storage. For the real-time metrics, I'd set up a Kafka stream consumer to process data as it arrives, while the historical customer service logs would be ingested via batch processing, likely with pandas. Cleaning would involve standardizing formats, handling missing values, and de-duplicating records. For feature engineering and descriptive analytics, I'd focus on Python libraries like pandas and scikit-learn. To surface patterns and error trends, I’d employ techniques like outlier detection and frequency analysis on the performance metrics, also looking at common keywords and sentiment in the customer service logs to identify recurring issues. Usage anomalies would be spotted by looking for deviations from typical daily or weekly patterns in the performance data. I am a Preferred Freelancer on Freelancer with a 5.0 rating, 100% on time and 100% on budget. The brief mentions AI-driven and ML models, but the primary focus is descriptive analytics. I will assume you want to identify patterns and trends from the data rather than predictive forecasting or classification. What is the expected volume of real-time network performance metrics per hour? I need the cloud environment details where the platform will be deployed.