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USD
I will build machine learning models
About this Gig
I will build and train machine learning models using Python for your data science projects.
My services include:
Data cleaning and preprocessing
Exploratory Data Analysis (EDA)
Feature engineering
Classification and regression models
Model training and evaluation
Hyperparameter tuning
Performance evaluation
Confusion matrix and classification report
Python and Scikit learn implementation
Jupyter Notebook or Google Colab projects
I can help you develop a complete machine learning solution based on your dataset and requirements. I will provide clean, organized, and easy to understand code along with model evaluation results.
Whether you need a classification model, regression model, data analysis, or machine learning project, I can help you build a reliable solution using Python.
Please contact me before placing an order so we can discuss your requirements.
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Programming language:
Python
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
Tools:
Jupyter Notebook
•
TensorFlow
•
Colab
FAQ
Q: What machine learning services do you provide?
I provide data preprocessing, EDA, feature engineering, classification, regression, model training, evaluation, and hyperparameter tuning using Python and Scikit learn.
Q: Which Python libraries do you use?
I mainly use Pandas, NumPy, Matplotlib, Seaborn, and Scikit learn for data analysis and machine learning projects.
Q: Can you work with my CSV or Excel dataset?
Yes. I can work with commonly used structured datasets such as CSV and Excel files and prepare them for machine learning.
Q: Which machine learning algorithms can you use?
I can work with algorithms such as Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Naive Bayes, and other suitable Scikit learn algorithms depending on the dataset.
Q: Do you provide the Python source code?
Yes. I provide clean and organized Python code along with the completed machine learning work.
Q: Can you perform data preprocessing and EDA?
Yes. I can handle missing values, duplicate data, categorical encoding, feature scaling, exploratory data analysis, and data visualization.
Q: How do you evaluate the machine learning model?
I use appropriate evaluation metrics such as accuracy, precision, recall, F1 score, confusion matrix, R2 score, MAE, and MSE depending on the type of problem.
