This project demonstrates an end-to-end workflow, from data preprocessing to saving the trained model for future use. It is suitable for beginners and intermediate-level practitioners looking to understand and implement machine learning pipelines.
- Data Loading: Import datasets in CSV format.
- Data Preprocessing: Handle missing values, encode categorical features, and scale numerical data.
- Exploratory Data Analysis (EDA): Visualize and summarize data insights.
- Model Training: Train machine learning models using
scikit-learn. - Model Evaluation: Assess performance using metrics like accuracy, confusion matrix, and classification reports.
- Model Saving: Save the trained model for future predictions.
To run the notebook, ensure the following Python libraries are installed:
numpypandasmatplotlibseabornscikit-learnjoblib
Install them using pip:
pip install numpy pandas matplotlib seaborn scikit-learn joblib-
Clone the Repository:
git clone https://github.com/DJay2012/Advertising_Sales_Prediction.git cd Advertising_Sales_Prediction -
Open the Notebook: Launch Jupyter Notebook and open the
model.ipynbfile:jupyter notebook model.ipynb
-
Run the Cells: Execute each cell sequentially to:
- Load and preprocess your dataset.
- Explore the data through visualizations.
- Train and evaluate the model.
- Save the trained model.
- Dataset: Replace the placeholder dataset with your own CSV file.
- Model: Update the machine learning algorithm to suit your requirements.
- Evaluation Metrics: Add or modify metrics as needed.
- Trained model file (e.g.,
.joblibor.pkl). - Visualizations for EDA and performance evaluation.
- Console outputs for key metrics.
Feel free to fork this repository, make enhancements, and submit a pull request. Contributions are welcome!
This project is licensed under the MIT License. See the LICENSE file for details.
Happy Coding! 🎉