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I will build a machine learning model using python and scikit learn
AI , Machine Learning, Data Analyst, Data Science
I’m a Computer Science graduate specializing in Artificial Intelligence and Data Science, with hands-on experience in Machine Learning, Deep Learning, Data Analysis, and Computer Vision.
I work with ...
About this Gig
Looking for a reliable Machine Learning solution for your dataset? I can help you build, train, and evaluate a machine learning model using Python and Scikit-learn.
I will work with your dataset to create a structured machine learning workflow, from data preprocessing and exploration to model training, evaluation, and optimization.
What I will provide:
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Feature engineering and data preparation
- Classification or regression models
- Model training and testing
- Performance evaluation using appropriate metrics
- Hyperparameter tuning and model optimization
- Clean and organized Python source code
- Clear performance analysis and documentation
Tools & Technologies:
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Whether you need a classification or regression model, I will provide a structured solution tailored to your dataset and project requirements.
Please contact me before placing an order if you have specific requirements or a large/complex dataset.
Programming language:
Python
•
MATLAB
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
Colab
My Portfolio
FAQ
What type of machine learning projects do you work on?
I work on both classification and regression problems using Python and Scikit-learn.
What do you need from me to start the project?
I usually need the dataset, a brief description of the project, and any specific requirements or goals you have.
3. Can you work with my own dataset?
Yes. I can work with your dataset and perform the necessary preprocessing, analysis, feature engineering, model training, and evaluation.
4. Which machine learning algorithms do you use?
Depending on the problem and dataset, I can use algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, SVM, Naive Bayes, XGBoost, and other suitable Scikit-learn models.
5. Will I receive the Python source code?
Yes. The completed project includes clean and organized Python source code according to the selected package.
6. Can you tune and optimize the model?
Yes. I can perform hyperparameter tuning and compare different models to help achieve better performance.
