I will custom machine learning, and deep learning models in python
About this Gig
Are you looking to turn complex datasets into accurate predictions, automated forecasts, or production-ready Machine Learning models?
I build robust, high-performance Data Science and Machine Learning pipelines in Python designed for real-world reliability, high accuracy, and actionable business insights.
️ WHAT I BUILD & DELIVER:
1. Predictive Modeling & Machine Learning:
Classification & Regression (Customer churn, lead scoring, fraud detection, price prediction)
Time-Series Forecasting (Sales forecasting, stock/inventory trends with Prophet & ARIMA)
Clustering & Customer Segmentation (K-Means, DBSCAN)
2. Data Preprocessing & Advanced Feature Engineering:
Handling missing values, outliers, imbalance (SMOTE), and encoding.
Statistical data analysis & interactive visualizations (Matplotlib, Seaborn, Plotly).
3. Model Optimization & Validation:
Hyperparameter tuning (GridSearchCV, Optuna) for maximum F1/ROC-AUC score.
Cross-validation to eliminate overfitting.
4. Production Deployment & Dashboards:
Export models (.pkl / .onnx) with REST API endpoints (FastAPI) or interactive Streamlit dashboards.
Every delivery includes clean, fully-commented code, and documentation..
Programming language:
Python
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SQL
Frameworks:
Scikit-learn
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Keras
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PyTorch
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Panda
Tools:
Jupyter Notebook
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OpenCV
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TensorFlow
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Excel
•
Colab
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What do I need to provide to get started?
You need to share your dataset in CSV, Excel, or any structured format along with a brief explanation of your requirements and the expected outcome.
What kind of problems can you solve with ML?
I can help with prediction, classification, clustering, churn analysis, recommendations, and more.
In what format will I receive the final project?
You will receive a clean, well-documented Jupyter Notebook (.ipynb), Python source files (.py), trained model artifacts, and instructions on how to run them.
Can you work with messy or unstructured datasets?
Yes! Data cleaning, handling missing values, outlier detection, and feature engineering are all core parts of my workflow.
