Do you have raw data but aren’t sure how to turn it into actionable predictions? I specialize in creating end-to-end machine learning solutions that transform your data into accurate, deployable models. Whether you need a simple regression, a complex deep-learning pipeline, or data preprocessing + feature engineering, I’ve got you covered.
What This Gig Includes
Data Exploration & Cleaning
Inspect your dataset for missing values, outliers, and inconsistencies
Perform basic Exploratory Data Analysis (EDA) with visualizations (histograms, scatter plots, correlation matrices)
Clean and normalize/standardize features, handle categorical encoding, impute missing values
Feature Engineering & Selection
Create new features (polynomial features, interaction terms, text embeddings, etc.)
Use techniques like PCA or SelectKBest to reduce dimensionality (if needed)
Perform feature importance analysis to identify top predictors
Model Selection & Training
Compare multiple algorithms (e.g., Linear Regression / Random Forest / XGBoost / Logistic Regression / SVM / Neural Net)
Hyperparameter tuning via GridSearchCV or RandomizedSearchCV (or Bayesian optimization if requested)
Train the final model on your full dataset with cross‐validation to avoid overfitting
Evaluation & Reporting
Provide clear performance metrics (e.g., accuracy, F1 score, ROC-AUC, RMSE)
Visualize learning curves, confusion matrices, or residual plots
Summarize results in a concise PDF or markdown report, explaining what’s working (and why)
Deployment & Deliverables
Package the final model in a Python script or Jupyter Notebook, fully documented
(Optional) Create a simple Flask/FastAPI endpoint or Docker container for quick deployment on Heroku/AWS/GCP
Include instructions on how to load the model, run predictions on new data, and reproduce all results
Provide all source code, dataset processing pipelines, and trained model files (Pickle or HDF5 format)
