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English
$
USD
I will develop a classification and prediction models
About this Gig
Welcome! Are you looking to unlock the power of your data with high-performance Machine Learning and Data Science solutions? I specialize in developing accurate Classification and Prediction models tailored to solve complex business problems.
What I Offer:
- Predictive Modeling: Sales forecasting, customer churn, and trend analysis.
- Classification Tasks: Binary & multi-class classification, sentiment analysis, and fraud detection.
- Deep Learning: Custom Neural Networks (ANN, CNN, RNN) using TensorFlow, Keras, and PyTorch.
- Data Science: Data cleaning, preprocessing, and Exploratory Data Analysis (EDA).
- Time Series: Advanced forecasting and pattern recognition.
Algorithms & Expertise:
- Linear/Logistic Regression, Decision Trees, Random Forest.
- XGBoost, LightGBM, Gradient Boosting.
- SVM, KNN, and Clustering (K-Means).
- Model Optimization & Hyperparameter Tuning.
Tools & Tech:
- Python (Pandas, NumPy, Scikit-Learn).
- Jupyter Notebook, Google Colab, VS Code.
- Data Visualization (Matplotlib, Seaborn).
Why Work With Me?
- Professional Quality: Clean, optimized, and well-documented code.
- Accuracy-Driven: Focus on high-precision results and robust validation.
Please contact me before placing an order!
Programming language:
Python
•
R
•
SQL
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
Excel
Frameworks:
Scikit-learn
•
Google ML Kit
•
Keras
•
PyTorch
•
TensorFlow
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What's the difference between classification and prediction? Which do I need?
Classification assigns inputs to discrete categories (spam/not spam, customer churn: yes/no, disease: present/absent). Prediction (regression) estimates a continuous value (sales next month, house price, customer lifetime value). Tell me your target variable and I'll recommend the right approach.
What types of data do you work with?
Tabular/structured data (CSV, Excel, SQL exports) is the primary focus for this gig. For image-based classification, see my separate image classification gig.
Which algorithms and libraries do you use?
scikit-learn, XGBoost, LightGBM, and CatBoost for most tabular problems. For deep learning on structured data, PyTorch. I choose based on dataset size, feature types, and interpretability requirements.
What do I need to provide?
Your dataset (CSV or Excel is fine), the column you want to predict, and any context about the problem — industry, constraints, what "good" looks like for you.
My dataset is messy — missing values, inconsistent formatting. Can you still work with it?
Yes. Data cleaning and preprocessing (handling nulls, encoding categoricals, outlier treatment, feature engineering) are included as part of the pipeline.
How do you make sure the model actually generalizes and isn't just overfitting?
I use cross-validation, proper train/val/test splits, and report metrics on held-out data. I'll flag any overfitting and apply regularization or resampling (SMOTE for imbalanced classes) as needed.
What will I receive as a deliverable?
A trained model file, a clean Python script or notebook with the full pipeline (preprocessing → training → evaluation), feature importance plots, and a written summary of results and key findings.
