Profit-Prediction-Model

This repository contains the code and documentation for a project aimed at predicting the profits for the remainder of the financial year 2021 (April to December) for Mengary, an online E-Commerce company with three subsidiary companies: Kariox (Electronics), Fynota (Food & Beverage), and Qexty (Clothing).

Background:

Mengary, an e-commerce company similar to Amazon, experienced lower-than-expected profits in the previous financial year. To proactively address this issue and improve future performance, the company seeks to accurately predict profits for the remaining months of the current financial year.

Objective:

The primary objective of this project is to develop a predictive model that can accurately forecast the profits for each product across all three subsidiary companies. This information will enable the management to:

• Identify potentially loss-making products.
• Adjust discount strategies accordingly to maximize profitability.
• Make informed business decisions for the upcoming financial year.

Approach:

1. Data Collection and Preparation:

   • Gather historical data on sales, orders, customer behavior, and other relevant factors for each product.
   • Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
   • Engineer relevant features such as:
     • Product-specific features (e.g., price, category, brand, sales history)
     • Time-based features (e.g., seasonality, holidays, promotions)
     • Customer-related features (e.g., customer demographics, purchase history)

2. Model Selection and Training:

   • Explore and evaluate various machine learning models (e.g., linear regression, decision trees, random forests, support vector regression, neural networks) for profit prediction.
   • Train and fine-tune the selected models using appropriate techniques (e.g., cross-validation, hyperparameter tuning).

3. Model Evaluation and Selection:

   • Evaluate model performance using appropriate metrics (e.g., Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared).
   • Select the best-performing model based on evaluation metrics and domain expertise.

4. Profit Prediction:

   • Utilize the chosen model to predict profits for each product for the remaining months of the financial year.

5. Results and Recommendations:

   • Generate reports and visualizations to present the predicted profits and key insights.
   • Provide actionable recommendations to the management based on the predictions, such as:
     • Adjusting product pricing and discounts.
     • Optimizing inventory levels.
     • Implementing targeted marketing campaigns.

Technologies:

• Python
• Pandas, NumPy, Scikit-learn, etc.
• [Specific libraries used for data visualization and model building]

Note:

This README provides a high-level overview of the project. For more detailed information, please refer to the project documentation and the code within this repository.