Amazon Product Sales Prediction

📌 Project Overview

This project predicts Amazon product sales performance using Machine Learning models. The primary goal is to analyze product attributes (such as price, discount, buy box availability, etc.) and predict whether a product will sell successfully.

The project includes:

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Feature engineering
• Model training & evaluation (Logistic Regression, K-Nearest Neighbors, Linear Regression)
• Insights on the most impactful product features

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🧠 Models Used

• Logistic Regression – for binary classification (sale success vs. failure).
• K-Nearest Neighbors (KNN) – for instance-based classification.
• Linear Regression – for baseline numerical trend analysis.

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📊 Dataset

• Source: Amazon products dataset (amazon_products_sales_data_uncleaned.csv)

• Cleaning Steps:

  • Dropped irrelevant columns (image_url, product_url)
  • Encoded categorical variables using LabelEncoder
  • Handled missing values
  • Scaled numerical features using StandardScaler

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⚙️ Installation & Setup

1. Clone the Repository

git clone https://github.com/your-username/amazon-sales-prediction.git
cd amazon-sales-prediction

2. Install Dependencies

pip install -r requirements.txt

3. Run the Jupyter Notebook

jupyter notebook project.ipynb

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🚀 Usage

• Open the notebook project.ipynb.

• Run all cells sequentially.

• The notebook will:

  • Load and clean the dataset.
  • Train models (Logistic Regression, KNN, Linear Regression).
  • Evaluate performance using accuracy and other metrics.
  • Generate insights & visualizations.

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📈 Results & Insights

• Logistic Regression and KNN performed as strong predictors of product sales success.
• Features such as price, discount percentage, and buy box availability were most significant.
• The analysis highlights that optimized pricing and discounts have the greatest effect on boosting sales.

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🔮 Future Work

• Add more advanced models (Random Forest, XGBoost, Neural Networks).
• Deploy the model with a Flask/Django API.
• Build a dashboard (e.g., with Streamlit) for real-time predictions.
• Expand dataset with customer reviews & ratings for sentiment analysis.

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📦 Requirements

• Python 3.8+
• NumPy, Pandas
• Scikit-learn
• Seaborn, Matplotlib
• Jupyter Notebook

(These can be installed via requirements.txt)

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📜 License

This project is licensed under the MIT License – feel free to use, modify, and distribute.