You will get I will perform sentiment analysis to extract customer insights
Project details
Automate sentiment analysis and uncover key insights from text data
I help businesses extract actionable insights from customer reviews, social media comments, survey responses, and more using deep learning-based sentiment analysis.
This service includes:
✔️ Text preprocessing & cleaning
✔️ Tokenization & embeddings
✔️ Deep learning model training (RNN/CNN/Transformer)
✔️ Sentiment classification (positive/neutral/negative)
✔️ Performance evaluation & interpretation
✔️ Results visualization
You will receive:
• Clean, reproducible Python code
• Accuracy, precision, recall evaluation
• Visualization charts
• Easy-to-interpret results
Business value:
• Understand customer satisfaction
• Improve product feedback loops
• Monitor brand reputation
• Automate text insights at scale
Ready to analyze your text data?
Send me a sample of your text data and I’ll provide a preview analysis.
I help businesses extract actionable insights from customer reviews, social media comments, survey responses, and more using deep learning-based sentiment analysis.
This service includes:
✔️ Text preprocessing & cleaning
✔️ Tokenization & embeddings
✔️ Deep learning model training (RNN/CNN/Transformer)
✔️ Sentiment classification (positive/neutral/negative)
✔️ Performance evaluation & interpretation
✔️ Results visualization
You will receive:
• Clean, reproducible Python code
• Accuracy, precision, recall evaluation
• Visualization charts
• Easy-to-interpret results
Business value:
• Understand customer satisfaction
• Improve product feedback loops
• Monitor brand reputation
• Automate text insights at scale
Ready to analyze your text data?
Send me a sample of your text data and I’ll provide a preview analysis.
Machine Learning Tools
BERT, ChatGPT, GitHub Copilot, Keras, NumPy, Open Neural Network Exchange, pandas, Python, Python Scikit-Learn, PyTorch, TensorFlow, Word2vecWhat's included
| Service Tiers |
Starter
$60
|
Standard
$130
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 2 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$60 - $300Frequently asked questions
About Christian Junior
Machine Learning Engineer|Predictive Modeling & Data Analysis (Python)
Yaounde, Cameroon - 2:54 pm local time
Are you looking to turn your data into accurate predictions and actionable insights?
I am a Machine Learning Engineer specialized in predictive modeling and data-driven solutions. I help businesses:
✔️ Build classification and regression models
✔️ Predict customer churn and sales
✔️ Segment customers using clustering
✔️ Improve model accuracy and performance
✔️ Clean, analyze, and structure datasets
Recent Projects
- Telco Churn Prediction (Classification models, feature engineering, evaluation)
- Sales Forecasting using ML techniques
- Customer Segmentation with K-Means
- Deep learning experiments (CNN, RNN, NLP models)
Technical Stack
- Python (Pandas, NumPy, Scikit-learn, PyTorch)
- SQL (MySQL, PostgreSQL)
- Data visualization (Matplotlib, Seaborn, Power BI)
- Model evaluation & optimization
My Approach
- Clear understanding of business goals
- Structured data analysis
- Clean and reproducible code
- Clear interpretation of results
I focus on delivering practical, reliable, and well-documented solutions.
Let’s discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Christian Junior can start the project.
Delivery time starts when Christian Junior receives requirements from you.
Christian Junior works on your project following the steps below.
Revisions may occur after the delivery date.
Data preparation
Collection and cleaning of annotated textual dataset (positive, negative, neutral) Tokenization, stop word removal, normalization Text vectorization (TF-IDF, Word2Vec, GloVe, or Transformers embeddings)
Model design and training
Choice of architecture (LSTM, CNN, BERT, etc.) depending on the level of the project Model training with cross-validation Performance evaluation (accuracy, F1-score, confusion matrix)