Customer Review Data Analysis

@pkundu25

Hi there, I am a senior Data Analytics/Data Science expert. I will deliver a fully reproducible, end-to-end Python pipeline focused strictly on text mining and feedback analysis. The Solution Approach: - Data Text Engineering: Clean raw CSVs using pandas, removing noise while preserving timestamp and product ID integrity. - NLP & Sentiment Mining: Deploy NLTK/spaCy for text tokenization, lemmatization, and sentiment scoring to isolate satisfaction drivers. - Theme Extraction: Implement unsupervised machine learning (scikit-learn TF-IDF / LDA) to automatically cluster recurring complaints and praises. - BI Visual Dashboard: Build an interactive Tableau/Power BI pack (or native Seaborn/Plotly charts) highlighting sentiment distribution and anomalies. - Reproducible Handoff: Deliver a fully commented, end-to-end Python notebook that runs flawlessly on your machine. Deliverables: 1. Cleaned, query-ready dataset. 2. Documented Python notebook. 3. Executive slide/dashboard pack. 4. Brief product improvement recommendations. Let's connect to review the sample data and align on the timeline. I am ready to start the project immediately.

@shadabkhan92

As a developer with a solid foundation on intelligent systems anchored in real-world data, I'm confident I can add significant value to your customer review data analysis project. My experience in leveraging Python and pandas for these tasks is extensive, not to mention my proficient handling of scikit-learn, NLTK and spaCy that will be invaluable in conducting a thorough sentiment analysis and extracting strong recurring themes from your customer reviews. What you're looking for is an analytically-driven process rooted in careful and thoughtful extraction valuable insights from raw data. This speaks right to the core of who I am as a professional - I don't believe in shortcuts when it comes to examining datasets. My forte lies in cleaning raw data into a purified form; an activity crucial in ensuring that the findings derived hold unwavering accuracy.

@muneeb6767

I can turn your review CSVs into actionable customer insights using Python, pandas, NLP, sentiment analysis, and theme extraction, with a fully reproducible notebook and clean documented dataset. I’ll also deliver clear visuals/dashboard outputs and concise recommendations linking recurring feedback to product and service improvements.

@TeraByte26

Hi I will deliver a cleaned CSV dataset, a fully commented Python notebook (pandas, scikit‑learn, NLTK) and a concise Tableau dashboard showing sentiment distribution, top themes and anomalies plus brief product recommendations. I can finish within 10 days for 30,000 INR. Shall I start with a sample notebook? Regards Waiting for your response in chat! Best Regards.

@khaliduom103

‎Hi i am an experienced python/Matlab developer with PhD in applied mathematics and data analyst.I can help you Matlab/python coding, optimization, numerical analysis،simulation and stock data prediction of the stock data. ‎ ‎

@pebambang5

Hello, I have read the outline of your project, and I’m sure can solve the task, provide correct result, free revision guarantee. My background is in statistics and applied mathematics using Python/R/JS programming for model statistics, predictive analytics, machine learning and artificial intelligence. #---Steps: . Data preparation, cleaning data using pandas,clean noise/outlier while preserving timestamp and product ID integrity. . Deploy NLTK/spaCy for text tokenization, lemmatization, and sentiment scoring to isolate satisfaction drivers. . Use model for unsupervised machine learning (scikit-learn TF-IDF / LDA) to automatically cluster recurring complaints and praises. . Visualization, Seaborn/Plotly charts, highlighting sentiment distribution and anomalies. . Provide and Delivery with a fully commented,Python notebook/Flask application runs in local machine. #----Deliverables, with clean dataset, Python notebook/Flask (clear dashboard), easy application. Please share your data, detail requirements, budget/delivery negotiable. Thank you. Best rgds, Bambangpe

@mahads31

Having honed my skills in data analysis and statistical comprehension, I can provide you with actionable insights on your customer review data. My skillset includes proficiency in Python, which I understand is your preferred language for this project, as well as experience using pandas and scikit-learn for data cleaning and mining. With NLTK and spaCy, we can readily extract sentiment and identify recurring themes within the reviews you've collected. What sets me apart is not only my technical prowess, but also my ability to translate complex data into easily comprehensible recommendations, essential for driving product and service improvements. Furthermore, I am adept at using Tableau or Power BI for data visualization which will allow us to distill your customer's sentiments, visualize them clearly, allowing for better strategy making. The end-to-end reproducibility of my analyses will be a breeze in your environment because thorough documentation and clean coding are key tenets of my work. Given our common focus on clarity and strategy, I believe that partnering on this project would be mutually beneficial. Please provide a sample dataset at your earliest convenience so we can confirm the approach and initiate the next steps toward turning raw data into impactful insights.

@CauaAlves

Hi, I'd approach this in pandas/Python: clean and structure the CSV reviews, run sentiment and theme extraction (spaCy/NLTK), then build a reproducible notebook so every visual traces back to the code. I'd wrap it up with a slide/dashboard pack on sentiment distribution and top themes, plus written recommendations tied to what's actually driving satisfaction or frustration. Could you share a sample of the CSV columns so I can confirm the schema before we start?

@alluankit01

Hi, I can analyze your customer review CSV data and turn it into clear insights on sentiment, recurring themes, anomalies, and product/service improvement opportunities. My approach will be to first clean the review data, validate ratings, timestamps and product IDs, then run text mining and sentiment analysis using Python, pandas, scikit-learn, NLTK or spaCy. I can help with: * Review data cleaning * Sentiment analysis * Theme/topic extraction * Rating and time trend analysis * Product-level feedback review * Anomaly detection * Python notebook reporting * Dashboard or slide summary * Written recommendations Deliverables: * Cleaned analysis-ready dataset * Reproducible Python notebook * Sentiment distribution visuals * Top recurring themes * Notable review anomalies * Dashboard or slide pack * Brief recommendation summary * Clear comments so the notebook reruns easily I’ll focus on practical, customer-feedback insights your product and marketing teams can act on, with transparent code and visuals that match the analysis output. Best regards Ankit

@caajoys

Thank you for considering my proposal. I have gone through the requirements in detail. I can transform your raw customer reviews into a clean, reproducible analysis that clearly identifies sentiment, recurring themes and actionable customer insights. With 10+ years of experience in data analysis, financial analytics, reporting and dashboard development, I am a Chartered Accountant (ICAI) and CPA with strong Excel, Python and BI capabilities. I have uploaded samples of similar data analysis, visualization and dashboard projects completed by me earlier in my profile. I’ll clean and standardize the CSV data, validate ratings and timestamps, and build a reproducible Python notebook covering text preprocessing, sentiment analysis, theme extraction and anomaly identification using appropriate NLP techniques. I’ll then create clear visuals showing sentiment distribution, key themes and product-level patterns, with insights directly connected to customer satisfaction and improvement opportunities. The final package will include the cleaned dataset, fully documented notebook, concise dashboard/slide pack and practical recommendations. I’ll ensure the notebook runs end-to-end and that visual outputs reconcile exactly with the underlying analysis. Payment & delivery assurance: ✅ No upfront payment ✅ Release payment after completion or milestone ✅ Timely delivery ✅ 100% commitment to project completion

@imranahmadquresh

Hi, I understand you need Data Analyst using Python to turn customer reviews into clear, actionable insight. I offer my services for this project. I am IBM Data Analyst certified. I have made many Data Analysis based projects using Python as follows; • Data analysis of Employment data of Wales using PCA, Correlation, K-means and Hierarchical Clustering. • Data analysis of Sensor data using multi-variate Linear Regression, Correlation, Normalization and Regularization. • Minimum Maximum Temperature and Rainfall of 4-Stations. • Data analysis of 10 weather monitoring stations around Denver area. • Data analysis of Temperature and Rainfall of Washington,DC and Denver,CO. • Data analysis and Hypothesis Testing Hotel Booking Cancellation Prediction. • Data analysis of Fish Catching in UAE. • Data analysis of Transactions. • Data analysis of Number of Patients Treated at Primary Health Centers (PHC) Categorized by Disease Type and Medical District. • Data analysis of UAE General Total Trade volume by Emirate from 2010 to 2019. • Data analysis of Iris Dataset. • Data analysis of Breast cancer Dataset. • Data analysis of Muffin & Cupcake ingredient Dataset. • Data analysis of Nursery Dataset. • Data analysis of CIFAR-10 Dataset. • Data analysis of Nursery Dataset. • Data analysis of Groceries Dataset. • Data analysis of Bankmortgage Dataset. I ensure to complete your project efficiently and on time.

@rohank2502

Your reviews already hold what drives satisfaction and frustration. I will clean the files, map the sentiment and recurring themes, and hand you a pack your product and marketing teams can use the same day. I can start right now. In 24 to 48 hours you get a live sample on a slice of your reviews: sentiment split, top themes, and any odd spikes, so you can judge the fit first. Cleaned file ready for extra checks. Analysis runs on your machine. Chart totals match the numbers. Recommendations point to real product or service changes. Share one CSV and I will start the sample today.

@JoseRCarvas

Hi — this is squarely my lane, and you've scoped it clearly, so here's exactly how I'd deliver. I'll take your reviews CSV (text, rating, product ID, timestamp), clean and normalise it (dedupe, encoding/language fixes, timestamp parsing), then build one reproducible Python notebook that runs end-to-end on your machine: pandas for cleaning, sentiment scoring (VADER plus a transformer model as a cross-check), and theme mining (keywords + topic modelling) so the recurring drivers of satisfaction and frustration surface per product and over time. You'll get: • A cleaned, documented dataset ready for ad-hoc queries • The notebook — import to visualisation, every step commented • A concise pack (slides/dashboard): sentiment distribution, top themes, notable anomalies • Short written recommendations tying findings to product and marketing actions I'll make the visual totals match the code output and confirm it reproduces cleanly on your side — exactly your acceptance criteria. Two quick questions: roughly how many reviews, and is there a product or category field you'd like the themes broken down by? Rated 5.0★ on Freelancer. I can start right away.

@InjectAI

Hi, I can turn your customer reviews into clear and useful insights for your product and marketing teams. I have hands on experience with Python, pandas, scikit learn, matplotlib, NLP and machine learning, so this fits well with my background. I will start by cleaning and validating the CSV data, handling missing values, duplicates, timestamps and text issues. Then I will perform exploratory analysis, sentiment analysis and theme extraction to identify what customers like, dislike and repeatedly mention. I can build the analysis in a fully reproducible Python notebook using pandas, NLTK or spaCy, scikit learn and matplotlib. I will keep the workflow clearly documented so you can rerun it with new review files later. Deliverables: • Cleaned and documented dataset • Complete reproducible Python notebook • Sentiment distribution and rating analysis • Recurring themes and topic insights • Anomaly and notable review analysis • Clear charts and visual summaries • Concise recommendations connected to customer feedback • Dashboard or slide style summary if required I will make sure the visual totals match the underlying code and that the notebook runs end to end on your machine. I can review your sample data first, confirm the approach, and then start the full analysis immediately.

@patodeh

Analyzing your customer review CSVs to extract clean sentiment scores and recurring themes is the core need. I will clean the raw files, standardize timestamps, and create a documented dataset ready for ad‑hoc queries. I will deliver a reproducible Python notebook that imports the data, performs preprocessing with pandas, applies spaCy for tokenization, uses scikit‑learn or NLTK for sentiment classification, and visualizes results with matplotlib and seaborn. A concise slide deck or Power BI dashboard will highlight sentiment distribution, top themes, and any notable anomalies. I will also write brief, actionable recommendations linking the insights to product and marketing improvements. One point to clarify: should the sentiment model be pretrained or do you prefer a custom‑trained classifier based on your rating column? Let’s chat, lock in the exact scope, and get started.

@fidaa23

As an experienced freelancer specializing in data analysis and visualization with Python, Statistics, and a solid understanding of Natural Language Processing (NLP) libraries like NLTK and spaCy, I am confident that I can extract invaluable insights from your customer review data. In addition to Python and pandas, I also have proficiency in Tableau that is well-suited for visualizing trends and identifying crucial sentiments from textual data. My commitment to delivering high-quality work is not just limited to analyzing the data but also ensuring that it is understandable and easy to work with. By focusing on creating clean, user-friendly, and responsive solutions, I make sure my deliverables are not only accurate but also easily reproducible. The value I place on time management ensures timely delivery without sacrificing quality. Having engaged in similar projects before, I understand the significance of actionable insights when it comes to boosting satisfaction or addressing frustrations. Therefore, my analysis will not only be limited to sentiment identification but will proactively address recurring themes to drive real product or service improvements. With the ability to run end-to-end analyses on your machine without discrepancies in output and performance, I am confident that my approach aligns perfectly with what you’re seeking for this project. I look forward to discussing more about the scope and our collaboration.

@thomasl062

I can turn your customer review CSVs into a clean, reproducible analysis focused specifically on sentiment, recurring themes, satisfaction drivers, and customer frustrations. I’d use Python with pandas, scikit-learn, and NLTK/spaCy, with clear preprocessing and documented NLP steps. I’ll deliver a cleaned dataset, end-to-end Jupyter notebook, sentiment and theme analysis, anomaly detection, and a concise dashboard/slide pack showing the key findings. I’ll also provide practical recommendations directly tied to the feedback, while ensuring all visual totals and insights are reproducible from the notebook. I can start by reviewing a sample of the CSVs to confirm the text quality, rating distribution, languages, and appropriate NLP approach before processing the full dataset. A few points I’d confirm: approximate number of reviews, whether reviews are single-language or multilingual, and whether you prefer Power BI, Tableau, or a Python-based dashboard.

@anujkashyapapj

I can turn your raw customer reviews into clear, actionable insights—not just charts. I’ll clean and structure the CSV data, validate ratings and timestamps, perform sentiment analysis and theme/topic extraction, and identify recurring sources of satisfaction, frustration, and anomalies. I’ll build a fully reproducible Python notebook using pandas and appropriate NLP techniques, with clear comments so you can rerun or extend the analysis. **Deliverables:** * Clean, analysis-ready dataset * End-to-end Python analysis notebook * Sentiment distribution and key themes * Product-level insights and notable anomalies * Clear visualizations/dashboard-ready outputs * Concise recommendations tied directly to customer feedback I’ll also ensure that the visual totals match the underlying code and that the entire workflow runs cleanly from raw CSV to final insights. I can start immediately and deliver within **3 days**. If you share a sample of the data, I can quickly confirm the approach before proceeding.

@shivamthakre77

Azure Data Engineer experienced in building and optimizing scalable data pipelines using **Azure Data Factory, ADLS, Azure Synapse, and SQL**. Skilled in ETL/ELT, data integration, transformation, data warehousing, and pipeline monitoring. Strong in developing reliable data solutions and optimizing data processes for analytics and reporting.