This project focuses on forecasting sales using machine learning techniques in Python.
The goal is to analyze historical sales data, preprocess it, train predictive models, and evaluate their performance to make accurate sales predictions.
- Data loading and preprocessing
- Exploratory Data Analysis (EDA)
- Feature engineering
- Machine Learning model training
- Model evaluation and comparison
- Sales prediction visualization
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn
📊 Workflow
Load and explore the dataset
Clean and preprocess the data
Perform exploratory data analysis (EDA)
Train machine learning models
Evaluate model performance
Generate sales forecasts
📈 Results
The notebook demonstrates how machine learning models can be used to predict future sales based on historical data. Model performance is evaluated using appropriate metrics and visualized for better interpretation.
🧠 Future Improvements
Add more advanced models (XGBoost, LSTM, Prophet)
Hyperparameter tuning
Cross-validation
Deploy the model as a web app (e.g., Streamlit or FastAPI)
Sales_forcastin_.ipynb # Main notebook containing data analysis and forecasting pipeline
README.md # Project documentation