This project involves the development of a predictive analytical solution for outlier sales detection using the Isolation Forest algorithm. The project aims to identify unusual sales patterns and provide insights for effective inventory management and business decision-making.

• Developed a predictive analytical solution using the Isolation Forest algorithm for outlier sales detection.
• Conducted univariate and bivariate analysis resulting in approximately 25% outlier detection.
• Collaborated across different teams and business units to understand requirements, including key performance indicators (KPIs) like contribution gap and sales growth.
• Communicated results to stakeholders, providing valuable insights for informed business decisions.

1. Data Loading and Preprocessing

   • Loaded training and test datasets (Train.csv and Test.csv).
   • Handled duplicate rows and transformed data to align with sales-related parameters.

2. Outlier Detection

   • Used Isolation Forest algorithm to predict outlier sales.
   • Identified and visualized potential outliers using boxplots and scatter plots.

3. Data Exploration and Visualization

   • Explored sales trends, product categories, and geographical sales distributions.
   • Visualized correlations and relationships between variables.

4. Data Processing and Scaling

   • Extracted relevant features and target variables.
   • Applied MinMaxScaler to scale feature variables.

5. Isolation Forest for Outlier Detection

   • Fitted Isolation Forest model to the scaled training data.
   • Predicted outliers in the training dataset.

6. Tableau Dashboard (Optional)

   • Created a Tableau dashboard to visually present analysis results.
   • Incorporated interactive visualizations and filters for user exploration.

7. Results and Communication

   • Shared insights with stakeholders, emphasizing contribution gap, sales growth, and outliers.
   • Facilitated informed business decisions through clear communication of findings.

• Python 3.x
• Required Python libraries (NumPy, Pandas, Seaborn, Scikit-learn, Matplotlib)

1. Clone this repository.
2. Install the required Python libraries using pip install -r requirements.txt.
3. Run the provided Jupyter Notebook to perform data analysis and outlier detection.
4. Optionally, create and explore the Tableau dashboard for visualizing project results.