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$
USD
I will build time series forecasting and predictive modeling using python
Artificial Intelligence and Machine Learning Professional
Motivated and detail-oriented AI/ML Engineer with a strong academic
foundation in machine learning, deep learning, and data analytics. Skilled
in Python, TensorFlow, and Scikit-learn for developing in...
About this Gig
I will build a professional time series forecasting model using Python to analyze trends, seasonality, and future patterns. I can work with ARIMA, SARIMA, SARIMAX, Prophet, XGBoost, Random Forest, and LSTM models. My service includes data preprocessing, time series analysis, feature engineering, model training, hyperparameter tuning, evaluation, and future forecasting. Ideal for sales, demand, revenue, price, inventory, and business forecasting.
Expertise:
Feature learning
•
Predictive analysis
Programming language:
Python
Frameworks:
Scikit-learn
Tools:
Jupyter Notebook
•
Colab
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What type of time series data can you work with?
I can work with sales, demand, revenue, inventory, price, financial, business, and other historical time series datasets.
Which forecasting models do you use?
I can use ARIMA, SARIMA, SARIMAX, Prophet, XGBoost, Random Forest, LSTM, and other suitable models depending on the dataset.
What format should I provide my data in?
CSV, Excel, or other structured tabular formats are preferred.
Can you forecast future values?
Yes. I can build forecasting models for your required forecast horizon, such as the next 7, 30, 60, or 90 days.
Do you evaluate the forecasting model?
Yes. I use appropriate metrics such as MAE, RMSE, MAPE, or other suitable evaluation metrics.
Can you compare multiple forecasting models?
Yes. I can train and compare multiple models and select the most suitable one based on validation performance.
Do you provide the Python source code?
Yes. Python source code can be included according to the selected package.
Can you handle missing values and outliers?
Yes. I can perform data preprocessing, missing-value treatment, outlier analysis, and other necessary data-cleaning steps.
Can you analyze trend and seasonality?
Yes. I can identify trends, seasonality, stationarity, autocorrelation, and other important time-series patterns.
Should I contact you before placing an order?
Yes, especially if your dataset is large, contains multiple time series, or requires a specific forecasting model.
