Predicting future sales is a critical task for businesses looking to optimize their operations and make informed decisions. This repository houses a robust machine learning project that leverages Linear Regression to forecast sales, utilizing Python and its powerful data science libraries.
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Linear Regression Model: I've implemented a Linear Regression algorithm to create accurate sales predictions based on past data from 2013-2018.
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Data Preprocessing: Our project showcases comprehensive data preprocessing techniques, including data cleaning, feature engineering, and normalization, to ensure the model's reliability.
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Data Visualization: We provide insightful data visualizations that help you better understand the underlying trends in your sales data.
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Model Evaluation: We've used various evaluation metrics to assess the model's performance, ensuring that it meets real-world requirements.
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Installation: Clone this repository and set up a Python environment.
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Data: You can use your own dataset, but I have included a sample dataset for demonstration which is from Kaggle.
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Notebooks: Explore our Jupyter notebooks or Google Colab to understand the data preprocessing, model building, and evaluation process.
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Training the Model: Follow the code examples to train your own Linear Regression model on the provided dataset or your own which u can find online.
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Predictions: Learn how to make sales predictions using your trained model.
I have used standard evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared to measure the model's performance.
