You will get I will build an accurate text classification solution for your business
Project details
Automate your text organization with an accurate machine learning classification solution
I help businesses automatically categorize unstructured text such as:
• Emails
• Feedback & reviews
• Support tickets
• Product descriptions
• User messages
This service includes:
✔️ Text cleaning & preprocessing
✔️ Feature extraction (TF-IDF, embeddings)
✔️ Model training & evaluation
✔️ Custom classification pipeline
✔️ Output delivery (CSV / JSON / dashboard)
✔️ Performance metrics visualization
You will receive:
• Clean Python code (reproducible & documented)
• A working prediction pipeline
• Evaluation results (Accuracy, F1, Precision, Recall)
• Easy interpretation of results
Business value:
• Reduce manual sorting effort
• Improve data workflows automation
• Accelerate document handling
• Enable real-time text categorization
👉 Ready to automate your text categorization? Let’s discuss your dataset!
I help businesses automatically categorize unstructured text such as:
• Emails
• Feedback & reviews
• Support tickets
• Product descriptions
• User messages
This service includes:
✔️ Text cleaning & preprocessing
✔️ Feature extraction (TF-IDF, embeddings)
✔️ Model training & evaluation
✔️ Custom classification pipeline
✔️ Output delivery (CSV / JSON / dashboard)
✔️ Performance metrics visualization
You will receive:
• Clean Python code (reproducible & documented)
• A working prediction pipeline
• Evaluation results (Accuracy, F1, Precision, Recall)
• Easy interpretation of results
Business value:
• Reduce manual sorting effort
• Improve data workflows automation
• Accelerate document handling
• Enable real-time text categorization
👉 Ready to automate your text categorization? Let’s discuss your dataset!
Machine Learning Tools
ChatGPT, Keras, MLflow, NLTK, NumPy, NVIDIA AI Platform, pandas, Python, PyTorch, scikit-learn, Stanford CoreNLP, TensorFlow, TextBlob, Word2vecWhat's included
| Service Tiers |
Starter
$40
|
Standard
$120
|
Advanced
$230
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
About Christian Junior
Machine Learning Engineer|Predictive Modeling & Data Analysis (Python)
Yaounde, Cameroon - 5:47 am 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.
Preparation of textual data
Cleaning and standardization of texts (removal of stopwords, punctuation, etc.). Vectorization with TF-IDF, Word2Vec, or modern embeddings. Creation of training and test sets.
Training on vectorized data.
Model design and training Model selection (SVM, Logistic Regression, LSTM, Transformer, etc.). Evaluation using metrics such as accuracy, F1-score, and confusion matrix.